Automatic control engineering Books

Book SynopsisThe classic text, now completely up to date This Second Edition of State Variables for Engineers is completely updated to reflect both the many changes in the field of systems and control and the fact that today''s first-year graduate students are well prepared in the background skills and techniques needed to handle this material. The book begins with an introduction to the basic concepts behind time domain techniques, comparisons between state variable feedback and classical output feedback, and a discussion of the concepts of observability and controllability. The authors stress the importance of studying matrices and linear spaces by offering state variable representations for continuous linear systems in matrix form along with the solution to the resulting linear matrix differential equation. This treatment demonstrates how these basic linear algebra tools are related to the state variable analysis of linear systems. This new edition retains thorough coverage of the eiTrade Review"...a welcome addition to the set of books on this subject." (International Journal of Robust and Linear Controls, Vol. 12, 2002)Table of ContentsTime Domain Techniques. State Variable Representation of Systems. Matrices, Linear Spaces, and Linear Systems. State Variables and Linear Continuous Systems. State Variables and Linear Discrete-Time Systems. Canonical Forms for Representing Linear Systems. Observers and Controllers. Identification and Estimation. Introduction to Stability Theory and Lyapunov's Method. Appendices. Index.

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Book SynopsisThe use of hydraulic control is rapidly growing and the objective of this book is to present a rational and well--balanced treatment of its components and systems. Coverage includes a review of applicable topics in fluid mechanisms; components encountered in hydraulic servo controlled systems; systems oriented issues and much more.Table of ContentsHydraulic Fluids. Fluid Flow Fundamentals. Hydraulic Pumps and Motors. Hydraulic Control Valves. Hydraulic Power Elements. Electrohydraulic Servovalves. Electrohydraulic Servomechanisms. Hydromechanical Servomechanisms. Nonlinearities in Control Systems. Pressure and Flow Control Valves. Hydraulic Power Supplies. Index.

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Book SynopsisThis text focuses on the management of complex automated systems encountered in industrial research and covers specific technologies and application domains that are cross-disciplinary. It offers broad discussions on the many related perspectives and presents detailed case studies.Table of ContentsIntroduction: Complexity Management for Automation and Control (T. Samad). AUTOMATION AND PEOPLE. Advanced Technology in Complex Systems: Automation, People, Culture (E. Cochran & P. Bullemer). The Human Factor in Complexity (C. Miller). Perceived Complexity and Mental Models in Human-Computer Interaction (V. Riley). SENSING AND CONTROL. Active Multimodeling for Autonomous Systems (T. Samad). Randomized Algorithms for Control and Optimization (R. Kulhav?). Complexity Management via Biology (B. Morton & T. Samad). Sensors in Control Systems (J. Zook, et al.). SOFTWARE AND COMPLEX SYSTEMS. Managing the Complexity of Software (J. Krueger). Agents for Complex Control Systems (R. Sanz). System Health Management for Complex Systems (G. Hadden, et al.). COMPLEXITY MANAGEMENT AND NETWORKS. Current and Future Developments in Air Traffic Control (S. Green & J. Jackson). Complex Adaptive Systems: Concepts and Power Industry Applications (A. Wildberger). National Infrastructure as Complex Interactive Networks (M. Amin). Multiscale Networking, Robustness, and Rigor (J. Doyle). Conclusions: Automation, Control, and Complexity (J. Weyrauch). Current Affiliations and Addresses of Contributors. Indexes.

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Book SynopsisAn integrated presentation of dynamics, vibrations, and control theory, emphasizing the fundamentals of dynamics. It also includes examples, problems and applications.Table of ContentsConcepts from Linear System Theory. Kinematics. Dynamics of a Particle. Response of First-Order and Second-Order Systems. Dynamics of Systems of Particles. Dynamics of Rigid Bodies. Elements of Analytical Dynamics. Vibration of Linear Multi-Degree-of-Freedom Systems. Introduction to System Stability. Computational Techniques for the Response. Feedback Control Systems. Appendix. Bibliography.

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Book SynopsisA comprehensive and extensive study of the latest research in control systems for marine vehicles. Demonstrates how the implementation of mathematical models and modern control theory can reduce fuel consumption and improve reliability and performance. Coverage includes ocean vehicle modeling, environmental disturbances, the dynamics and stability of ships, sensor and navigation systems. Numerous examples and exercises facilitate understanding.Table of ContentsModeling of Marine Vehicles. Environmental Disturbances. Stability and Control of Underwater Vehicles. Dynamics and Stability of Ships. Automatic Control of Ships. Control of High-Speed Craft. Appendices. Bibliography. Index.

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Book SynopsisWith a strong emphasis on applications of intelligent control, this extremely accessible book covers the fundamentals, methodologies, architectures and algorithms of automatic control systems.Table of ContentsFundamental Techniques for Intelligent Control. Learning Strategies and Algorithms. System Modeling and Estimation. Dynamic Controls. Optimization Control Techniques. Multivariate Statistics and Quality Control. Fault Detection and Diagnosis. Appendix. Bibliography. Index.

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Book SynopsisThe concept of a system as an entity in its own right has emergedwith increasing force in the past few decades in, for example, theareas of electrical and control engineering, economics, ecology,urban structures, automaton theory, operational research andindustry. The more definite concept of a large-scale system isimplicit in these applications, but is particularly evident infields such as the study of communication networks, computernetworks and neural networks. The Wiley-Interscience Series inSystems and Optimization has been established to serve the needs ofresearchers in these rapidly developing fields. It is intended forworks concerned with developments in quantitative systems theory,applications of such theory in areas of interest, or associatedmethodology. This is the first book-length treatment of risk-sensitive control,with many new results. The quadratic cost function of the standardLQG (linear/quadratic/Gaussian) treatment is replaced by theexponential of a quadratTable of ContentsBASICS. Deterministic Models. Stochastic Models. BEYOND. Risk-Sensitive and H infinity Criteria. Time-Integral Methods and Optimal Stationary Policies. Near-Determinism and Large Deviation Theory. Appendices. References. Index.

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Book SynopsisThe main objective of the authors is to deliver specifications and underlying concepts for future computer-aided tools for the design and the control of flexible manufacturing systems for mechanical and electro-mechanical assemblies. This book presents an integrated computer-aided method which supports a concurrent engineering approach for assembled products. This integrated method is divided in several modules which analyse the ease of assembly of a design, the assembly order, the design of an assembly workshop, and the simulation of the workshop taking into account scheduling and flow control. Automatic, semi-automatic and manual utilisations are presented for each module. Communication between design and manufacturing has been emphasised. The environment in this book is a real concurrent engineering one and for the first time the concurrent engineering steps are integrated in a CAD system. The method has been implemented in one of the world s most used CAD systems: CATIA.Table of ContentsThe CAD Method for Industrial Assembly and ConcurrentEngineering. Proposed Architecture for the New CAD Method. Product Design for Assembly. Assembly Planning. Resource Planning. The Simulation Module. The Scheduling Module. The Flow Control Module. Integration Aspects of the CAD Method. Introducing the Integrated CAD Method into Companies. Conclusions. Index.

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Book SynopsisMultistage Fuzzy Control a model-based approach to fuzzy control and decision making Fuzzy techniques are used to cope with imprecision in the control process. This authoritative book explains the essential principles of fuzzy logic and describes both the theoretical and practical advantages of the new model-based, prescriptive approach. Professor Kacprzyk offers a comprehensive and in depth examination of the issues underlying multistage control and decision analysis, addressing in particular fuzzy dynamic systems, fuzzy events, fuzzy probabilities and fuzzy quantifiers. The text also comprises an introduction to the basic concepts of fuzzy sets, fuzzy logic and fuzzy systems, complemented by real-world examples of the use of the model-based prescriptive approach to improve the efficiency of fuzzy control systems. Highly experienced in fuzzy control research, the author identifies new trends in the development of fuzzy sets and their direct application to decision-making processes. FuTable of ContentsBasic Elements of Fuzzy Sets and Fuzzy Systems. A General Setting for Multistage Control Under Fuzziness. Control Processes with a Fixed and Specified TerminationTime. Control Processes with an Implicitly Specified TerminationTime. Control Processes with a Fuzzy Termination Time. Control Processes with an Infinite Termination Time. Examples of Applications. Concluding Remarks. Bibliography. Index.

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Book SynopsisThis text examines synthetic and dynamical properties of fuzzy control systems in a quantitative manner. It includes fuzzy dynamical systems, controllability and sensitivity analysis and how these affect parameters in membership functions, fuzzification, defuzzification and inferencing.Trade Review"Design and control engineers will value the advanced control techniques, new design and analysis tools presented. Post-graduates...a useful reference." (Engineering Design, July 2000) "...a good read...it boldly tackles the stability issue of fuzzy control systems..." (Measurement and Control, October 2000) "Design and control engineers will value the advanced control techniques and new design and analysis tools presented. Postgraduates studying fuzzy control will find this book a useful reference...." (European Power Electronics & Drives Journal September 2001)Table of ContentsMODELING. Information Granularity in the Analysis and Design of Fuzzy Controllers. Fuzzy Modeling for Predictive Control. Adaptive and Learning Schemes for Fuzzy Modeling. Fuzzy System Identification with General Parameter Radial Basis Function Neural Network. ANALYSIS. Lyapunov Stability Analysis of Fuzzy Dynamic Systems. Passivity and Stability of Fuzzy Control Systems. Frequency Domain Analysis of MIMO Fuzzy Control Systems. Analytical Study of Structure of a Mamdani Fuzzy Controller with Three Input Variables. An Approach to the Analysis of Robust Stability of Fuzzy Control Systems. Fuzzy Control Systems Stability Analysis with Application to Aircraft Systems. SYNTHESIS. Observer-Based Controller Synthesis for Model-Based Fuzzy Systems via Linear Matrix Inequalities. LMI-Based Fuzzy Control: Fuzzy Regulator and Fuzzy Observer Design via LMIs. A Framework for the Synthesis of PDC-Type Takagi-Sugano Fuzzy Control Systems: An LMI Approach. On Adaptive Fuzzy Logic Control on Non-linear Systems--Synthesis and Analysis. Stabilization of Direct Adaptive Fuzzy Control Systems: Two Approaches. Gain Scheduling Based Control of a Class of TSK Systems. Output Tracking Using Fuzzy Neural Networks. Fuzzy Life-Extending Control of Mechanical Systems. Epilogue. Index.

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Book SynopsisThis book addresses the reader to use synergistically the concepts of membranes and sensors materials. It contains insightful contributions from leading scientists working in both the fields. The focus is on the fabrication of smart membranes from sensor materials and related impact on many technologically sophisticated areas such as telemedicine, microfluidics, drug delivery targeting, (bio)separation, labs-on-a-chip, textiles, power storage and release, environment monitoring, agro-food safety, cosmetics, architecture, automotive and so on. This book covers various topics, including the choice of materials and techniques for assembling responsive membranes with ability to transport mass, energy and signals on demand; the reader will find through the book an extensive description of the best techniques used to monitor molecular scale events, which are regarded as responsible for the smartness of multifunctional objects and for the conversion of chemical signals into optical,Table of ContentsPreface Part 1: Sensing Materials for Smart Membranes 1 1 Interfaces Based on Carbon Nanotubes, Ionic Liquids and Polymer Matrices for Sensing and Membrane Separation Applications 3 María Belén Serrano-Santos, Ana Corres Ortega, and Thomas Schäfer 1.1 Introduction 3 1.2 Ionic Liquid-Carbon Nanotubes Composites for Sensing Interfaces 5 1.3 Ionic Liquid Interfaces for Detection and Separation of Gases and Solvents 11 1.4 Ionic Liquid-Polymer Interfaces for Membrane Separation Processes 16 1.5 Conclusions 18 Acknowledgement 19 References 19 2 Photo-Responsive Hydrogels for Adaptive Membranes 21 David Díaz Díaz and Jeremiah A. Johnson 2.1 Introduction 21 2.2 Photo-Responsive Hydrogel Membranes 23 2.3 Photo-Thermally Responsive Hydrogel Membranes 44 2.4 Summary 46 2.5 Acknowledgements 48 Abbreviations 48 References 49 3 Smart Vesicles: Synthesis, Characterization and Applications 53 Jung-Keun Kim, Chang-Soo Lee, and Eunji Lee 3.1 Introduction 53 3.2 Synthesis of Soft Vesicles 54 3.3 Synthesis of Hard Vesicles 64 3.4 Characterization of Vesicular Structures 68 3.5 Stimuli-Responsive Behaviors of Vesicular Structures 72 3.6 Application of Vesicles 78 3.7 Conclusions 91 Acknowledgment 92 References 92 Part 2: Stimuli-Responsive Interfaces 105 4 Computational Modeling of Sensing Membranes and Supramolecular Interactions 107 Giacomo Saielli 4.1 Introduction 107 4.2 Non-covalent Interactions: A Physical and a Chemical View 109 4.3 Physical Interactions 109 4.4 Chemical Interactions 114 4.5 Computational Methods for Supramolecular Interactions 117 4.6 Classical Force Fields 127 4.7 Conclusions 139 References 140 5 Sensing Techniques Involving Thin Films for Studying Biomolecular Interactions and Membrane Fouling Phenomena 145 Gabriela Diaconu and Thomas Schäfer 5.1 Introduction 145 5.2 Quartz Crystal Microbalance with Dissipation Monitoring (QCM-D) 146 5.3 Surface Plasmon Resonance (SPR) 148 5.4 Applications of SPR and QCM-D 151 5.5 Conclusions 159 Acknowledgements 160 References 160 6 Smart Membrane Surfaces: Wettability Amplification and Self-Healing 161 Annarosa Gugliuzza 6.1 Introduction 161 6.2 Basics of surface wettability 162 6.3 Amplified Wettability 164 6.4 Actuation Mechanisms 165 6.5 Self-Powered Liquid Motion 170 6.6 Self-Cleaning Mechanisms 172 6.7 Self-Healing Concepts And Strategies 175 6.8 Repairable Surface Properties 177 6.9 Conclusions and Perspectives 179 References 180 7 Model Bio-Membranes Investigated by AFM and AFS: A Suitable Tool to Unravel Lipid Organization and their Interaction with Proteins 185 Andrea Alessandrini and Paolo Facci 7.1 Introduction 186 7.2 Supported Lipid Bilayers 189 7.3 Atomic Force Microscopy (AFM) and Phase Behavior of Slbs 199 7.4 Atomic Force Spectroscopy (AFS) of Supported Lipid Bilayers 205 7.5 Lipid/Protein Interactions 213 7.6 Conclusions 218 References 218 Part 3: Directed Molecular Separation 227 8 Self-Assembled Nanoporous Membranes for Controlled Drug Release and Bioseparation 229 Dominique Scalarone, Pierangiola Bracco, and Francesco Trotta 8.1 Introduction 229 8.2 General Aspects of Block Copolymer Self-Assembly 231 8.3 Block Copolymer Based Membranes 233 8.4 Fabrication of Nanoporous Membranes Derived from Block Copolymers 234 8.5 Tunability of Surface Properties 242 8.6 Application of Block Copolymer Derived Membranes to Bioseparation and Controlled Drug Release 244 8.7 Conclusion 250 References 250 Abbreviations 253 9 Hybrid Mesoporous Silica for Drug Targeting 255 Luigi Pasqua, Piluso Rosangela, Ilenia Pelaggi, and Catia Morelli 9.1 Introduction 256 9.2 Synthesis and Characterization of Bifunctional Hybrid Mesoporous Silica Nanoparticles Potentially Useful for Drug Targeting 257 9.3 Drug-Loaded Folic-Acid-Grafted Msns Specifically Target FR Expressing Tumour Cells [16] 260 9.4 Conclusion 266 References 268 10 Molecular Recognition-driven Membrane Processes 269 Laura Donato, Rosalinda Mazzei, Catia Algieri, Emma Piacentini, Teresa Poerio, and Lidietta Giorno 10.1 Molecular Imprinting Technique 270 10.2 Affinity Membranes 275 10.3 Cyclodextrins As Molecular Recognition Elements 281 10.4 Zeolite Membranes as Molecular Recognition Devices: Preparation and Characterization 283 10.5 Functionalized Particles-loaded Membranes For Selective Separation Based On Molecular Recognition 287 10.6 Biphasic Enzyme Membrane Systems with Enantioselective Recognition Properties ror Kinetic Resolution 291 10.7 Membrane Surface Modification 292 References 296 Part 4: Membrane Sensors and Challenged Applications 301 11 Electrospun Membranes for Sensors Applications 303 Pierangiola Bracco, Dominique Scalarone, and Francesco Trotta 11.1 Introduction 303 11.2 Basic Principles of Electrospinning 304 11.3 Control of the Electrospinning Process 306 11.4 Application of Electrospun Materials to Ultrasensitive Sensors 311 11.5 Conclusions 329 Abbreviations 330 References 330 12 Smart Sensing Scaffolds 337 Carmelo De Maria, Yudan Whulanza, Giovanni Vozzi, and Arti Ahluwalia 12.1 Introduction 337 12.2 Composite Sensing Biomaterial Preparation 339 12.3 Composite Sensing Biomaterial Characterisation 340 12.4 SWNTs-Based Composite Films Structural Properties 341 12.5 Tensile Properties of SWNTs-Based Composite Films 343 12.6 Electrical Properties of SWNTs-Based Composites Films 348 12.7 Electromechanical Characterisation and Strain-Dependence Measurement 350 12.8 Cell Sensing Scaffolds 352 12.9 Processing of CNT Composite: Microfabrication of Sensing Scaffold 360 12.10 Conclusions 361 References 362 13 Nanostructured Sensing Emulsion Droplets and Particles: Properties and Formulation by Membrane Emulsification 367 Emma Piacentini, Alessandra Imbrogno, and Lidietta Giorno 13.1 Introduction 367 13.2 Emulsions and Emulsification Methods 370 13.3 Senging Particles Produced by Membrane-Based Process 389 13.4 Conclusions 397 References 398 14 Membranes for Ultra-Smart Textiles 401 Annarosa Gugliuzza and Enrico Drioli 14.1 Introduction 401 14.2 Membranes and Comfort 403 14.3 Adaptive Membranes for Smart Textiles 407 14.4 Barrier Functions of Membranes 411 14.5 Membrane Materials for Self-cleaning Function 413 14.6 Interactive Membranes for Wearable Electronics 414 14.7 Conclusions and Prospects 415 References 416

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Book SynopsisFoundations of Fuzzy Control: A Practical Approach, 2nd Edition has been significantly revised and updated, with two new chapters on Gain Scheduling Control and Neurofuzzy Modelling. It focuses on the PID (Proportional, Integral, Derivative) type controller which is the most widely used in industry and systematically analyses several fuzzy PID control systems and adaptive control mechanisms. This new edition covers the basics of fuzzy control and builds a solid foundation for the design of fuzzy controllers, by creating links to established linear and nonlinear control theory. Advanced topics are also introduced and in particular, common sense geometry is emphasised. Key features Sets out practical worked through problems, examples and case studies to illustrate each type of control system Accompanied by a website hosting downloadable MATLAB programs Accompanied by an online course on Fuzzy Control which is taught by the author. StTable of ContentsForeword xiii Preface to the Second Edition xv Preface to the First Edition xvii 1Introduction 1 1.1 What Is Fuzzy Control? 1 1.2 Why Fuzzy Control? 2 1.3 Controller Design 3 1.4 Introductory Example: Stopping a Car 3 1.5 Nonlinear Control Systems 9 1.6 Summary 11 1.7 The Autopilot Simulator* 12 1.8 Notes and References* 13 2 Fuzzy Reasoning 17 2.1 Fuzzy Sets 17 2.2 Fuzzy Set Operations 25 2.3 Fuzzy If–Then Rules 33 2.4 Fuzzy Logic 36 2.5 Summary 43 2.6 Theoretical Fuzzy Logic* 43 2.7 Notes and References* 53 3 Fuzzy Control 55 3.1 The Rule Based Controller 56 3.2 The Sugeno Controller 61 3.3 Autopilot Example: Four Rules 64 3.4 Table Based Controller 65 3.5 Linear Fuzzy Controller 68 3.6 Summary 70 3.7 Other Controller Components* 70 3.8 Other Rule Based Controllers* 77 3.9 Analytical Simplification of the Inference* 80 3.10 Notes and References* 84 4 Linear Fuzzy PID Control 85 4.1 Fuzzy P Controller 87 4.2 Fuzzy PD Controller 89 4.3 Fuzzy PD+I Controller 90 4.4 Fuzzy Incremental Controller 92 4.5 Tuning 94 4.6 Simulation Example: Third-Order Process 99 4.7 Autopilot Example: Stable Equilibrium 101 4.8 Summary 103 4.9 Derivative Spikes and Integrator Windup* 104 4.10 PID Loop Shaping* 106 4.11 Notes and References* 109 5 Nonlinear Fuzzy PID Control 111 5.1 Nonlinear Components 111 5.2 Phase Plot 113 5.3 Four Standard Control Surfaces 115 5.4 Fine-Tuning 118 5.5 Example: Unstable Frictionless Vehicle 121 5.6 Example: Nonlinear Valve Compensator 124 5.7 Example: Motor Actuator with Limits 127 5.8 Autopilot Example: Regulating a Mass Load 127 5.9 Summary 130 5.10 Phase Plane Analysis* 130 5.11 Geometric Interpretation of the PD Controller* 134 5.12 Notes and References* 143 6 The Self-Organizing Controller 145 6.1 Model Reference Adaptive Systems 145 6.2 The Original SOC 147 6.3 A Modified SOC 150 6.4 Example with a Long Deadtime 151 6.5 Tuning and Time Lock 155 6.6 Summary 157 6.7 Example: Adaptive Control of a First-Order Process* 157 6.8 Analytical Derivation of the SOC Adaptation Law* 161 6.9 Notes and References* 169 7 Performance and Relative Stability 171 7.1 Reference Model 172 7.2 Performance Measures 177 7.3 PID Tuning from Performance Specifications 180 7.4 Gain Margin and Delay Margin 185 7.5 Test of Four Difficult Processes 186 7.6 The Nyquist Criterion for Stability 188 7.7 Relative Stability of the Standard Control Surfaces 191 7.8 Summary 193 7.9 Describing Functions* 193 7.10 Frequency Responses of the FPD and FPD+I Controllers* 198 7.11 Analytical Derivation of Describing Functions for the Standard Surfaces* 206 7.12 Notes and References* 216 8 Fuzzy Gain Scheduling Control 217 8.1 Point Designs and Interpolation 218 8.2 Fuzzy Gain Scheduling 219 8.3 Fuzzy Compensator Design 221 8.4 Autopilot Example: Stopping on a Hilltop 226 8.5 Summary 228 8.6 Case Study: the FLS Controller* 229 8.7 Notes and References* 235 9 Fuzzy Models 237 9.1 Basis Function Architecture 238 9.2 Handmade Models 240 9.3 Machine-Made Models 249 9.4 Cluster Analysis 253 9.5 Training and Testing 263 9.6 Summary 266 9.7 Neuro-Fuzzy Models* 267 9.8 Notes and References* 275 10 Demonstration Examples 277 10.1 Hot Water Heater 277 10.2 Temperature Control of a Tank Reactor 282 10.3 Idle Speed Control of a Car Engine 287 10.4 Balancing a Ball on a Cart 292 10.5 Dynamic Model of a First-Order Process with a Nonlinearity 301 10.6 Summary 307 10.7 Further State-Space Analysis of the Cart-Ball System* 307 10.8 Notes and References* 314 References 315 Index 319

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Book SynopsisDescribing the use of displays in microcontroller based projects, the author makes extensive use of real-world, tested projects. The complete details of each project are given, including the full circuit diagram and source code.Table of ContentsPreface xiii Acknowledgements xv 1 Introduction to Microcontrollers and Display Systems 1 1.1 Microcontrollers and Microprocessors 2 1.2 Evolution of the Microcontroller 3 1.3 Parts of a Microcontroller 4 1.3.1 Address 4 1.3.2 ALU 5 1.3.3 Analogue Comparator 5 1.3.4 Analogue-to-Digital Converter 5 1.3.5 Brown-out Detector 5 1.3.6 Bus 5 1.3.7 CAN 6 1.3.8 CISC 6 1.3.9 Clock 6 1.3.10 CPU 6 1.3.11 EEPROM 6 1.3.12 EPROM 6 1.3.13 Ethernet 7 1.3.14 Flash Memory 7 1.3.15 Harvard Architecture 7 1.3.16 Idle Mode 7 1.3.17 Interrupts 7 1.3.18 LCD Drivers 8 1.3.19 Pipelining 8 1.3.20 Power-on Reset 8 1.3.21 PROM 8 1.3.22 RAM 8 1.3.23 Real-time Clock 8 1.3.24 Register 9 1.3.25 Reset 9 1.3.26 RISC 9 1.3.27 ROM 9 1.3.28 Serial Input-Output 9 1.3.29 Sleep Mode 9 1.3.30 Supply Voltage 10 1.3.31 Timers 10 1.3.32 USB 10 1.3.33 Watchdog 10 1.4 Display Devices 10 1.4.1 LED 10 1.4.2 7-Segment LED 11 1.4.3 OLED 12 1.4.4 LCD 12 1.5 Summary 15 Exercises 15 2 PIC18F Microcontrollers 17 2.1 The PIC18F2410 Microcontroller 18 2.2 PIC18F2410 Architecture 19 2.2.1 The Program Memory 21 2.2.2 The Data Memory 21 2.2.3 Power Supply Requirements 22 2.2.4 Oscillator Configurations 24 2.2.5 The Reset 30 2.2.6 Parallel I/O Ports 31 2.2.7 Timer Modules 38 2.2.8 Analogue-to-Digital Converter Module 43 2.2.9 Special Features of the CPU 48 2.2.10 Interrupts 49 2.2.11 Pulse Width Modulator Module 53 2.3 Summary 56 Exercises 56 3 C Programming Language 59 3.1 C Languages for Microcontrollers 59 3.2 Your First mikroC Pro for PIC Program 61 3.2.1 Comments 61 3.2.2 Beginning and Ending a Program 62 3.2.3 White Spaces 63 3.2.4 Variable Names 63 3.2.5 Reserved Names 64 3.2.6 Variable Types 64 3.2.7 Constants 66 3.2.8 Escape Sequences 68 3.2.9 Volatile Variables 69 3.2.10 Accessing Bits of a Variable 69 3.2.11 sbit Type 70 3.2.12 bit Type 70 3.2.13 Arrays 70 3.2.14 Pointers 73 3.2.15 Structures 76 3.2.16 Unions 80 3.2.17 Operators in mikroC Pro for PIC 80 3.2.18 The Flow of Control 90 3.3 Functions in mikroC Pro for PIC 101 3.3.1 Function Prototypes 102 3.3.2 void Functions 103 3.3.3 Passing Parameters to Functions 104 3.3.4 Passing Arrays to Functions 106 3.3.5 Interrupt Processing 106 3.4 mikroC Pro for PIC Built-in Functions 108 3.5 mikroC Pro for PIC Libraries 109 3.5.1 ANSI C Library 109 3.5.2 Miscellaneous Library 111 3.6 Using the mikroC Pro for PIC Compiler 111 3.6.1 mikroC Pro for PIC IDE 112 3.6.2 Creating a New Source File 118 3.6.3 Compiling the Source File 122 3.7 Using the mikroC Pro for PIC Simulator 123 3.7.1 Setting a Break-Point 124 3.8 Other mikroC Pro for PIC Features 126 3.8.1 View Statistics 126 3.8.2 View Assembly 127 3.8.3 ASCII Chart 127 3.8.4 USART Terminal 127 3.8.5 Seven Segment Editor 127 3.8.6 Help 128 3.9 Summary 128 Exercises 129 4 PIC Microcontroller Development Tools – Including Display Development Tools 131 4.1 PIC Hardware Development Boards 132 4.1.1 Super Bundle Development Kit 132 4.1.2 PIC18 Explorer Board 132 4.1.3 PIC18F4XK20 Starter Kit 134 4.1.4 PICDEM 4 135 4.1.5 PIC16F887 Development Kit 135 4.1.6 FUTURLEC PIC18F4550 Development Board 137 4.1.7 EasyPIC6 Development Board 137 4.1.8 EasyPIC7 Development Board 139 4.2 PIC Microcontroller Display Development Tools 140 4.2.1 Display Hardware Tools 140 4.2.2 Display Software Tools 143 4.3 Using the In-Circuit Debugger with the EasyPIC7 Development Board 145 4.4 Summary 149 Exercises 149 5 Light Emitting Diodes (LEDs) 151 5.1 ATypical LED 151 5.2 LED Colours 153 5.3 LED Sizes 154 5.4 Bi-Colour LEDs 154 5.5 Tri-Colour LEDs 155 5.6 Flashing LEDs 155 5.7 Other LED Shapes 155 5.8 7-Segment LEDs 156 5.8.1 Displaying Numbers 157 5.8.2 Multi-digit 7-Segment Displays 159 5.9 Alphanumeric LEDs 159 5.10 mikroC Pro for PIC 7-Segment LED Editor 163 5.11 Summary 163 Exercises 164 6 Liquid Crystal Displays (LCDs) and mikroC Pro for PIC LCD Functions 165 6.1 HD44780 Controller 165 6.2 Displaying User Defined Data 168 6.3 DDRAM Addresses 169 6.4 Display Timing and Control 171 6.4.1 Clear Display 172 6.4.2 Return Cursor to Home 172 6.4.3 Cursor Move Direction 172 6.4.4 Display ON/OFF 172 6.4.5 Cursor and Display Shift 173 6.4.6 Function Set 173 6.4.7 Set CGRAM Address 173 6.4.8 Set DDRAM Address 173 6.4.9 Read Busy Flag 174 6.4.10 Write Data to CGRAM or DDRAM 174 6.4.11 Read Data from CGRAM or DDRAM 174 6.5 LCD Initialisation 174 6.5.1 8-bit Mode Initialisation 175 6.5.2 4-bit Mode Initialisation 175 6.6 Example LCD Display Setup Program 177 6.7 mikroC Pro for PIC LCD Functions 180 6.7.1 Lcd_Init 180 6.7.2 Lcd_Out 181 6.7.3 Lcd_Out_Cp 181 6.7.4 Lcd_Chr 181 6.7.5 Lcd_Chr_Cp 181 6.7.6 Lcd_Cmd 182 6.8 Summary 182 Exercises 183 7 Graphics LCD Displays (GLCD) 185 7.1 The 128 x 64 Pixel GLCD 185 7.2 Operation of the GLCD Display 187 7.3 mikroC Pro for PIC GLCD Library Functions 189 7.3.1 Glcd_Init 189 7.3.2 Glcd_Set_Side 190 7.3.3 Glcd_Set_X 190 7.3.4 Glcd_Set_Page 190 7.3.5 Glcd_Write_Data 190 7.3.6 Glcd_Fill 190 7.3.7 Glcd_Dot 191 7.3.8 Glcd_Line 191 7.3.9 Glcd_V_Line 191 7.3.10 Glcd_H_Line 191 7.3.11 Glcd_Rectangle 192 7.3.12 Glcd_Rectangle_Round_Edges 192 7.3.13 Glcd_Rectangle_Round_Edges_Fill 192 7.3.14 Glcd_Box 193 7.3.15 Glcd_Circle 193 7.3.16 Glcd_Circle_Fill 194 7.3.17 Glcd_Set_Font 194 7.3.18 Glcd_Set_Font_Adv 194 7.3.19 Glcd_Write_Char 195 7.3.20 Glcd_Write_Char_Adv 195 7.3.21 Glcd_Write_Text 195 7.3.22 Glcd_Write_Text_Adv 195 7.3.23 Glcd_Write_Const_Text_Adv 196 7.3.24 Glcd_Image 196 7.4 Example GLCD Display 196 7.5 mikroC Pro for PIC Bitmap Editor 198 7.6 Adding Touch-screen to GLCDs 199 7.6.1 Types of Touch-screen Displays 200 7.6.2 Resistive Touch Screens 200 7.7 Summary 203 Exercises 204 8 Microcontroller Program Development 205 8.1 Using the Program Description Language and Flowcharts 205 8.1.1 BEGIN – END 206 8.1.2 Sequencing 206 8.1.3 IF – THEN – ELSE – ENDIF 206 8.1.4 DO – ENDDO 207 8.1.5 REPEAT – UNTIL 209 8.1.6 Calling Subprograms 209 8.1.7 Subprogram Structure 209 8.2 Examples 211 8.3 Representing for Loops in Flowcharts 216 8.4 Summary 218 Exercises 218 9 LED Based Projects 219 9.1 PROJECT 9.1 – Flashing LED 219 9.2 PROJECT 9.2 – Binary Counting Up LEDs 226 9.3 PROJECT 9.3 – Rotating LEDs 229 9.4 PROJECT 9.4 – Wheel of Lucky Day 231 9.5 PROJECT 9.5 – Random Flashing LEDs 239 9.6 PROJECT 9.6 – LED Dice 240 9.7 PROJECT 9.7 – Connecting more than one LED to a Port Pin 246 9.8 PROJECT 9.8 – Changing the Brightness of LEDs 250 9.9 PROJECT 9.9 – LED Candle 264 9.10 Summary 267 Exercises 267 10 7-Segment LED Display Based Projects 269 10.1 PROJECT 10.1 – Single Digit Up Counting 7-Segment LED Display 269 10.2 PROJECT 10.2 – Display a Number on 2-Digit 7-Segment LED Display 271 10.3 PROJECT 10.3 – Display Lottery Numbers on 2-Digit 7-Segment LED Display 278 10.4 PROJECT 10.4 – Event Counter Using 4-Digit 7-Segment LED Display 285 10.5 PROJECT 10.5 – External Interrupt Based Event Counter Using 4-Digit 7-Segment LED Display with Serial Driver 292 10.6 Summary 302 Exercises 303 11 Text Based LCD Projects 305 11.1 PROJECT 11.1 – Displaying Text on LCD 305 11.2 PROJECT 11.2 – Moving Text on LCD 307 11.3 PROJECT 11.3 – Counting with the LCD 310 11.4 PROJECT 11.4 – Creating Custom Fonts on the LCD 315 11.5 PROJECT 11.5 – LCD Dice 317 11.6 PROJECT 11.6 – Digital Voltmeter 325 11.7 PROJECT 11.7 – Temperature and Pressure Display 327 11.8 PROJECT 11.8 – The High/Low Game 333 11.9 Summary 344 Exercises 345 12 Graphics LCD Projects 347 12.1 PROJECT 12.1 – Creating and Displaying a Bitmap Image 347 12.2 PROJECT 12.2 – Moving Ball Animation 355 12.3 PROJECT 12.3 – GLCD Dice 357 12.4 PROJECT 12.4 – GLCD X-Y Plotting 372 12.5 PROJECT 12.5 – Plotting Temperature Variation on the GLCD 374 12.6 PROJECT 12.6 – Temperature and Relative Humidity Measurement 385 12.7 Operation of the SHT11 386 12.8 Acknowledgement 389 12.9 Summary 400 Exercises 400 13 Touch Screen Graphics LCD Projects 401 13.1 PROJECT 13.1 – Touch Screen LED ON-OFF 401 13.2 PROJECT 13.2 – LED Flashing with Variable Rate 410 13.3 Summary 418 Exercises 418 14 Using the Visual GLCD Software in GLCD Projects 419 14.1 PROJECT 14.1 – Toggle LED 420 14.2 PROJECT 14.2 – Toggle more than One LED 425 14.3 PROJECT 14.3 – Mini Electronic Organ 426 14.4 PROJECT 14.4 – Using the SmartGLCD 430 14.5 PROJECT 14.5 – Decimal to Hexadecimal Converter using the SmartGLCD 444 14.6 Summary 452 Exercises 452 15 Using the Visual TFT Software in Graphics Projects 453 15.1 PROJECT 15.1 – Countdown Timer 454 15.2 PROJECT 15.2 – Electronic Book 462 15.3 PROJECT 15.3 – Picture Show 467 15.4 Summary 472 Exercises 472 Bibliography 473 Index 475

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Book SynopsisEnhanced e-book includes videos Many books have been written on modelling, simulation and control of four-wheeled vehicles (cars, in particular). However, due to the very specific and different dynamics of two-wheeled vehicles, it is very difficult to reuse previous knowledge gained on cars for two-wheeled vehicles. Modelling, Simulation and Control of Two-Wheeled Vehicles presents all of the unique features of two-wheeled vehicles, comprehensively covering the main methods, tools and approaches to address the modelling, simulation and control design issues. With contributions from leading researchers, this book also offers a perspective on the future trends in the field, outlining the challenges and the industrial and academic development scenarios. Extensive reference to real-world problems and experimental tests is also included throughout. Key features: The first book to cover all aspects of two-wheeled vehicle dynamiTable of ContentsAbout the Editors xi List of Contributors xiii Series Preface xv Introduction xvii Part One TWO-WHEELED VEHICLES MODELLING AND SIMULATION 1 Motorcycle Dynamics 3 Vittore Cossalter, Roberto Lot, and Matteo Massaro 1.1 Kinematics 3 1.2 Tyres 6 1.3 Suspensions 13 1.4 In-Plane Dynamics 18 1.5 Out-of-Plane Dynamics 29 1.6 In-Plane and Out-of-Plane Coupled Dynamics 40 References 41 2 Dynamic Modelling of Riderless Motorcycles for Agile Manoeuvres 43 Yizhai Zhang, Jingang Yi, and Dezhen Song 2.1 Introduction 43 2.2 Related Work 44 2.3 Motorcycle Dynamics 45 2.4 Tyre Dynamics Models 51 2.5 Conclusions 55 Nomenclature 55 Appendix A: Calculation of Ms 56 Appendix B: Calculation of Acceleration ̇G 57 Acknowledgements 57 References 57 3 Identification and Analysis of Motorcycle Engine-to-Slip Dynamics 59 Matteo Corno and Sergio M. Savaresi 3.1 Introduction 59 3.2 Experimental Setup 60 3.3 Identification of Engine-to-Slip Dynamics 61 3.4 Engine-to-Slip Dynamics Analysis 73 3.5 Road Surface Sensitivity 78 3.6 Velocity Sensitivity 79 3.7 Conclusions 80 References 80 4 Virtual Rider Design: Optimal Manoeuvre Definition and Tracking 83 Alessandro Saccon, John Hauser, and Alessandro Beghi 4.1 Introduction 83 4.2 Principles of Minimum Time Trajectory Computation 86 4.3 Computing the Optimal Velocity Profile for a Point-Mass Motorcycle 90 4.4 The Virtual Rider 102 4.5 Dynamic Inversion: from Flatland to State-Input Trajectories 103 4.6 Closed-Loop Control: Executing the Planned Trajectory 107 4.7 Conclusions 115 4.8 Acknowledgements 116 References 116 5 The Optimal Manoeuvre 119 Francesco Biral, Enrico Bertolazzi, and Mauro Da Lio 5.1 The Optimal Manoeuvre Concept: Manoeuvrability and Handling 121 5.2 Optimal Manoeuvre as a Solution of an Optimal Control Problem 133 5.3 Applications of Optimal Manoeuvre to Motorcycle Dynamics 145 5.4 Conclusions 152 References 152 6 Active Biomechanical Rider Model for Motorcycle Simulation 155 Valentin Keppler 6.1 Human Biomechanics and Motor Control 156 6.2 The Model 161 6.3 Simulations and Results 167 6.4 Conclusions 179 References 180 7 A Virtual-Reality Framework for the Hardware-in-the-Loop Motorcycle Simulation 183 Roberto Lot and Vittore Cossalter 7.1 Introduction 183 7.2 Architecture of the Motorcycle Simulator 184 7.3 Tuning and Validation 188 7.4 Application Examples 191 References 194 Part Two TWO-WHEELED VEHICLES CONTROL AND ESTIMATION PROBLEMS 8 Traction Control Systems Design: A Systematic Approach 199 Matteo Corno and Giulio Panzani 8.1 Introduction 199 8.2 Wheel Slip Dynamics 202 8.3 Traction Control System Design 206 8.4 Fine tuning and Experimental Validation 212 8.5 Conclusions 218 References 219 9 Motorcycle Dynamic Modes and Passive Steering Compensation 221 Simos A. Evangelou and Maria Tomas-Rodriguez 9.1 Introduction 221 9.2 Motorcycle Main Oscillatory Modes and Dynamic Behaviour 222 9.3 Motorcycle Standard Model 224 9.4 Characteristics of the Standard Machine Oscillatory Modes and the Influence of Steering Damping 226 9.5 Compensator Frequency Response Design 228 9.6 Suppression of Burst Oscillations 233 9.7 Conclusions 240 References 240 10 Semi-Active Steering Damper Control for Two-Wheeled Vehicles 243 Pierpaolo De Filippi, Mara Tanelli, and Matteo Corno 10.1 Introduction and Motivation 243 10.2 Steering Dynamics Analysis 245 10.3 Control Strategies for Semi-Active Steering Dampers 252 10.3.1 Rotational Sky-Hook and Ground-Hook 253 10.4 Validation on Challenging Manoeuvres 257 10.5 Experimental Results 266 10.6 Conclusions 267 References 268 11 Semi-Active Suspension Control in Two-Wheeled Vehicles: a Case Study 271 Diego Delvecchio and Cristiano Spelta 11.1 Introduction and Problem Statement 271 11.2 The Semi-Active Actuator 272 11.3 The Quarter-Car Model: a Description of a Semi-Active Suspension System 275 11.4 Evaluation Methods for Semi-Active Suspension Systems 277 11.5 Semi-Active Control Strategies 279 11.6 Experimental Set-up 281 11.7 Experimental Evaluation 281 11.8 Conclusions 289 References 289 12 Autonomous Control of Riderless Motorcycles 293 Yizhai Zhang, Jingang Yi, and Dezhen Song 12.1 Introduction 293 12.2 Trajectory Tracking Control Systems Design 294 12.3 Path-Following Control System Design 305 12.4 Conclusion 315 Acknowledgements 317 Appendix A: Calculation of the Lie Derivatives 317 References 318 13 Estimation Problems in Two-Wheeled Vehicles 319 Ivo Boniolo, Giulio Panzani, Diego Delvecchio, Matteo Corno, Mara Tanelli, Cristiano Spelta, and Sergio M. Savaresi 13.1 Introduction 319 13.2 Roll Angle Estimation 320 13.3 Vehicle Speed Estimation 329 13.4 Suspension Stroke Estimation 337 13.5 Conclusions 342 References 342 Index 345

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Book SynopsisComprehensive guide on learning automata, introducing two variants to accelerate convergence and computational update speed Learning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles, algorithms, improvement directions, and applications. The text introduces two variants to accelerate the convergence speed and computational update speed, respectively; these two examples demonstrate how to design new learning automata for a specific field from the aspect of algorithm design to give full play to the advantage of learning automata. As noisy optimization problems exist widely in various intelligent systems, this book elaborates on how to employ learning automata to solve noisy optimization problems from the perspective of algorithm design and application. The existing and most representative applications of learning automata include classification, clustering, game, knapsack, network, optimization, r

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Book SynopsisThis companion book to MakerShed's Ultimate Arduino Microcontroller Pack provides 26 clearly explained projects that you can build with this top-selling kit right away--including multicolor flashing lights, timers, tools for testing circuits, sound effects, motor control, and sensor devices.

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Book SynopsisHow can you take advantage of feedback control for enterprise programming? With this book, author Philipp K. Janert demonstrates how the same principles that govern cruise control in your car also apply to data center management and other enterprise systems.

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Book SynopsisMaker Pro is a book of essays by more than a dozen prominent and up-and-coming professional makers (Maker Pros). Each essay includes advice and stories on topics such as starting a kit-making business, taking a hardware project open-source, and plenty of encouragement to "quit your day job."

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Book SynopsisWritten for intermediate to seasoned Raspberry Pi users, this book explores four projects from around the world, explained by their makers. These projects cover five major categories in the digital maker space: music, light, games, home automation, and the Internet of Things.

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Book SynopsisIt provides detailed derivations for attitude kinematics and dynamics and provides detailed description of the most widely used attitude parameterization, the quaternion.Trade Review“The present book presents the fundamental concepts and mathematical basis for spacecraft attitude description and control. … Every chapter and appendix contains a rich references. After reading this book, the reader will be convinced that the intended audience for it consists of graduate students, Ph.D. students and scientists with an interest in spacecraft attitude determination and control.” (Clementina Mladenova, zbMATH 1381.70006, 2018)“It is of exceptional quality in both the range of subjects covered and the detail of that coverage. … this book has a lot to offer to those of us involved in the developments and data processing for such projects, describing in detail external forces as well as the effects of non-rigidity. … this is a very worthwhile volume, highly recommended for anyone involved in astronomical or other satellite projects.” (Floor van Leeuwen, The Observatory, Vol. 135 (1246), June, 2015)“This is an excellent book. Markley … and Crassidis (Univ. of Buffalo) have succeeded in creating a work that is a good textbook for both upper-level undergraduate and graduate students as well as practitioners. … book ends with several appendixes that further support the rest of the book and also provide novice practitioners with a good resource to help them understand the more technical and complicated material. … Summing Up: Highly recommended. Aerospace engineering collections serving upper-division undergraduates through professionals/practitioners.” (D. B. Spencer, Choice, Vol. 52 (7), March, 2015)Table of ContentsAttitude Determination and Estimation.- Matrices, Vectors, Frames, Transforms.- Rotational Kinematics and Dynamics.- Sensors and Actuators.- Static Attitude Determination Methods.- Filtering for Attitude Determination.- Attitude Control.

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Book SynopsisEngineering systems operate through actuators, most of which will exhibit phenomena such as saturation or zones of no operation, commonly known as dead zones. These are examples of piecewise-affine characteristics, and they can have a considerable impact on the stability and performance of engineering systems. This book targets controller design for piecewise affine systems, fulfilling both stability and performance requirements.The authors present a unified computational methodology for the analysis and synthesis of piecewise affine controllers, taking an approach that is capable of handling sliding modes, sampled-data, and networked systems. They introduce algorithms that will be applicable to nonlinear systems approximated by piecewise affine systems, and they feature several examples from areas such as switching electronic circuits, autonomous vehicles, neural networks, and aerospace applications.Piecewise Affine Control: Continuous-Time, Sampled-Data, and Networked Systems is intended for graduate students, advanced senior undergraduate students, and researchers in academia and industry. It is also appropriate for engineers working on applications where switched linear and affine models are important.Trade ReviewPiecewise affine systems are widely used as modeling and design tools across a number of applications, ranging from robotics to systems biology. These systems require a delicate touch as they can exhibit complex and sometimes surprising features. This impressive book navigates the world of such systems with clarity, technical depth, and elegance.”- Professor Magnus Egerstedt, Georgia Institute of Technology

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Book SynopsisManaging technological innovations and related policy and strategy issues have been a central focus of the new millennium. This book series presents an interdisciplinary scholarship and dialogue on the management of innovation and technological change in a global context from a variety of perspectives, including strategic, managerial, behavioural, and policy issues. Papers selected in this volume have four prominent themes: the wide spread interests and the global application of the technological innovation; the practicality of the research on technological innovation implementation to foster success and financial growth; the socio-technical challenges behind innovation and creativity that might outweigh the benefits; and the new principles/practices/perspectives on our understanding of the technological innovation. Contributed by prominent scholars and practitioners from around the world in innovation, management and policy area, this book will become a very useful read for anyone who is interested in learning the most contemporary perspectives on the subject.

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Book SynopsisManaging technological innovations and related policy and strategy issues have been a central focus of the new millennium. This book series presents an interdisciplinary scholarship and dialogue on the management of innovation and technological change in a global context from a variety of perspectives, including strategic, managerial, behavioural, and policy issues. Papers selected in this volume have four prominent themes: the wide spread interests and the global application of the technological innovation; the practicality of the research on technological innovation implementation to foster success and financial growth; the socio-technical challenges behind innovation and creativity that might outweigh the benefits; and the new principles/practices/perspectives on our understanding of the technological innovation. Contributed by prominent scholars and practitioners from around the world in innovation, management and policy area, this book will become a very useful read for anyone who is interested in learning the most contemporary perspectives on the subject.

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Book SynopsisThis book provides a comprehensive guide to compressed sensing-based techniques, focusing primarily on their application to systems and control.

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Book SynopsisThis book presents the most important methods used for the design of digital controls implemented in industrial applications. The best modelling and identification techniques for dynamical systems are presented as well as the algorithms for the implementation of the modern solutions of process control. The proposed described methods are illustrated by various case studies for the main industrial sectors There exist a number of books related each one to a single type of control, yet usually without comparisons for various industrial sectors. Some other books present modelling and identification methods or signal processing. This book presents the methods to solve all the problems linked to the design of a process control without the need to find additional information.Table of ContentsPreface ix List of Acronyms and Notations xi Chapter 1 Introduction – Models and Dynamic Systems 1 1.1 Overview 1 1.2 Industrial process modeling 3 1.3 Model classes 5 1.3.1 State space models 5 1.3.2 Input–output models 12 Chapter 2 Linear Identification of Closed-Loop Systems 21 2.1 Overview of system identification 21 2.2 Framework 22 2.3 Preliminary identification of a CL process 27 2.3.1 Multivariable linear identification methods 27 2.3.2 Estimation of linear MIMO models using the LSM 30 2.3.3 Identifying CL processes using the MV-LSM 35 2.4 CLOE class of identification methods 39 2.4.1 Principle of CLOE methods 39 2.4.2 Basic CLOE method 41 2.4.3 Weighted CLOE method 46 2.4.4 Filtered CLOE method or adaptively filtered CLOE 56 2.4.5 Extended CLOE method 58 2.4.6 Generalized CLOE method 65 2.4.7 CLOE methods for systems with integrator 74 2.4.8 On the validation of CLOE identified models 78 2.5 Application: identification of active suspension 80 Chapter 3 Digital Control Design Using Pole Placement 93 3.1 Digital proportional-integral-derivative algorithm control 93 3.2 Digital polynomial RST control 96 3.3 RST control by pole placement 98 3.3.1 RST control for regulation dynamics 99 3.3.2 RST polynomial control for tracking dynamics (setpoint change) 100 3.3.3 RST control with independent objectives in tracking and regulation 101 3.4 Predictive RST control 104 3.4.1 Finite horizon predictive control 105 3.4.2 Predictive control with unitary horizon 107 Chapter 4 Adaptive Control and Robust Control 113 4.1 Adaptive polynomial control systems 113 4.1.1 Estimation of the parameters for closed-loop systems 114 4.1.2 Design of the adaptive control 115 4.2 Robust polynomial control systems 117 4.2.1 Robustness of closed-loop systems 118 4.2.2 Studying the stability–robustness connection 121 4.2.3 Study of the nonlinearity–robustness connection 123 4.2.4 Study of the performance–robustness connection 124 4.2.5 Analysis of robustness in the study of the sensitivity function 125 4.2.6 Design of the robust RST control 127 4.2.7 Calibrating the sensitivity function 128 Chapter 5 Multimodel Control 131 5.1 Construction of multimodels 132 5.1.1 Fuzzy logic: Mamdani models 132 5.1.2 Identification from input–output data: direct method 138 5.1.3 Identification from input–output data: neural approach 139 5.1.4 Linearization around various operating points 141 5.1.5 Convex polytopic transformation from an analytical model refined for the command 141 5.1.6 Calculation of the validity of base models 143 5.2 Stabilization and control of multimodels 144 5.3 Design of multimodel command: fuzzy approach 144 5.4 Trajectory tracking 145 Chapter 6 Ill-Defined and/or Uncertain Systems 147 6.1 Study of the stability of nonlinear systems from vector norms 147 6.1.1 Vector norms 147 6.1.2 Comparison systems and overvaluing systems 148 6.1.3 Determination of attractors 153 6.1.4 Nested attractors [GHA 15a] 156 6.2 Adaptation of control 156 6.2.1 Minimizing the size of attractors: direct approach 156 6.2.2 Minimizing the size of attractors by metaheuristics 157 6.3 Overvaluation of the maximum error for various applications 157 6.3.1 Control of nonlinear systems by pole placement 157 6.3.2 Diffeomorphism command of nonlinear processes 159 6.3.3 Determining the attractor for Lur’e Postnikov type processes [GHA 14] 161 6.3.4 Minimizing the attractor through tabu search 165 6.4 Fuzzy secondary loop control 171 Chapter 7 Modeling and Control of an Elementary Industrial Process 173 7.1 Modeling and control of fluid transfer processes 173 7.1.1 Modeling fluid flow processes 173 7.1.2 Designing flow control systems 178 7.2 Modeling and controlling liquid storage processes 180 7.2.1 Constant output flow 181 7.2.2 Variable output flow 183 7.2.3 Designing liquid level control systems 185 7.3 Modeling and controlling the storage process of a pneumatic capacitor 187 7.3.1 Modeling a pneumatic capacitor 187 7.3.2 Designing pneumatic capacitor control systems 190 7.4 Modeling and controlling heat transfer processes 191 7.4.1 Modeling a thermal transfer process 191 7.4.2 Designing temperature control systems 194 7.5 Modeling and control of component transfer processes 195 7.5.1 Modeling a chemical mixing process without reaction 195 7.5.2 Modeling a chemical reaction process 198 7.5.3 Designing systems for controlling the concentration of chemical components 200 Chapter 8 Industrial Applications – Case Studies 203 8.1 Digital control for an installation of air heating in a steel plant 203 8.1.1 Automation solution and design of the control algorithms 204 8.1.2 Optimization of the combustion process 207 8.2 Control and optimization of an ethylene installation 210 8.2.1 Automation solution and designing the control algorithms 211 8.2.2 Optimizing the pyrolysis process 217 8.3 Digital control of a thermoenergy plant 219 8.3.1 Solving the problem of automation of a thermal operating point 220 8.3.2 Optimization of thermal transfer and agent product 225 8.4 Extremal control of a photovoltaic installation 226 8.4.1 Extremal control of a photovoltaic panel 237 Appendix A 243 Appendix B 249 Appendix C 257 Appendix D 261 Bibliography 271 Index 279

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Book SynopsisThis book covers various modern theoretical, technical, practical and technological aspects of computerized numerical control and control systems of deterministic and stochastic dynamical processes. Table of ContentsPreface xi Introduction xv Part 1: Advanced Elements and Test Bench of Computer-aided Feedback Control 1 Chapter 1: Canonical Discrete State Models of Dynamic Processes 3 1.1. Interest and construction of canonical state models 3 1.2. Canonical realizations of a transfer function G(z) 4 1.2.1. Jordan canonical realization 4 1.2.2. Controllable canonical realization7 1.2.3. Observable canonical realization 9 1.3. Canonical transformations of discrete state models 11 1.3.1. Jordan canonical transformation 12 1.3.2. Controllable canonical transformation 13 1.3.3. Observable canonical transformation 16 1.3.4. Kalman canonical transformation 19 1.4. Canonical decomposition diagram 19 1.5. Discretization and canonical transformations using Matlab 20 1.6. Exercises and solutions 21 Chapter 2: Design and Simulation of Digital State Feedback Control Systems 27 2.1. Principle of digital state feedback control 27 2.2. Calculation of the gain K using pole placement 28 2.3. State feedback with complete order observer 29 2.3.1. Problem statement 29 2.3.2. Structure of the complete or full state observer 29 2.3.3. Synthesis diagram of the state feedback with complete observer 31 2.4. Discrete state feedback with partial observer 34 2.4.1. Problem statement 34 2.4.2. Structure of the partial state observer 34 2.4.3. Diagram of discrete state feedback with partial observer 37 2.5. Discrete state feedback with set point tracking 40 2.6. Block diagram of a digital control system 40 2.7. Computer-aided simulation of a servomechanism 41 2.7.1. Simulation of a speed servomechanism 41 2.7.2. Computer-aided simulation of a position servomechanism 46 2.8. Exercises and solutions 49 Chapter 3: Multimedia Test Bench for Computer-aided Feedback Control 61 3.1. Context and interest 61 3.1.1. Context 61 3.1.2. Scientific/teaching interest 62 3.1.3. Platform presentation methodology 62 3.2. Hardware constituents of the platform 62 3.3. Design elements of the ServoSys software application 63 3.3.1. Fundamental elements 63 3.3.2. Elements of software programming 68 3.4. Design of the ServoSys software application 74 3.4.1. Architectural diagram of the software application 74 3.4.2. SFC of the ServoSys multimedia platform 75 3.5. Implementation of the ServoSys multimedia platform 80 3.5.1. Hardware implementation 80 3.5.2. Software implementation 81 3.6. Overall tests of the platform 84 3.6.1. Commissioning and procedures 84 3.6.2. Samples of results displayed on the Matlab/GUI panel 85 3.7. Exercises and solutions 90 Part 2: Deterministic and Stochastic Optimal Digital Feedback Control 97 Chapter 4: Deterministic Optimal Digital Feedback Control 99 4.1. Optimal control: context and historical background 99 4.1.1. Context 99 4.1.2. Historical background 99 4.2. General problem of discrete-time optimal control 102 4.2.1. Principle 102 4.2.2. Functional formulation 102 4.3. Linear quadratic regulator (LQR) 103 4.3.1. Definition, formulation and study methods 103 4.3.2. H–J–B equations 104 4.4. Translation in discrete time of continuous LQR problem 108 4.4.1. Discretization of state equation 109 4.4.2. Discretization of the cost function 109 4.4.3. Case study of a scalar LQR problem 110 4.5. Predictive optimal control 114 4.5.1. Basic principle 114 4.5.2. Recurrence equation of a process based on q–1 operator 116 4.5.3. General formulation of a prediction model 116 4.5.4. Solution and structure of predictive optimal control 118 4.6. Exercises and solutions 119 Chapter 5: Stochastic Optimal Digital Feedback Control 127 5.1. Introduction to stochastic dynamic processes 127 5.2. Stochastic LQR 128 5.2.1. Formulation 128 5.2.2. Resolution of the stochastic H–J–B equation 129 5.2.3. Block diagram of stochastic LQR 133 5.2.4. Properties of stochastic LQR 134 5.3. Discrete Kalman filter 136 5.3.1. Scientific context and hypotheses 136 5.3.2. Notations 136 5.3.3. Closed-loop algorithmic diagram 137 5.4. Linear Quadratic Gaussian regulator 139 5.4.1. Context 139 5.4.2. Separation principle 140 5.4.3. Algorithmic diagram of LQG regulator 141 5.5. Exercises and solutions 142 Chapter 6: Deployed Matlab/GUI Platform for the Design and Virtual Simulation of Stochastic Optimal Control Systems 145 6.1. Introduction to OPCODE (Optimal Control Design) platform 145 6.1.1. Scientific context 145 6.1.2. Detailed presentation methodology 145 6.2. Fundamental OPCODE design elements 146 6.2.1. Elements of deterministic optimal control 146 6.2.2. Elements of stochastic optimal control 149 6.3. Design of OPCODE using SFC 152 6.3.1. Architectural diagram 152 6.3.2. Implementation of SFC 155 6.4. Software implementation 157 6.5. Examples of OPCODE use 159 6.5.1. Design of deterministic optimal control systems 159 6.5.2. Design of stochastic optimal control systems 159 6.6. Production of deployed OPCODE.EXE application 161 6.6.1. Interest of Matlab/GUI application deployment 161 6.6.2. Deployment methodology 162 6.6.3. Tests of deployed OPCODE.EXE application 162 6.7. Exercises and solutions 164 Part 3: Remotely Operated Feedback Control Systems via the Internet 169 Chapter 7: Elements of Remotely Operated Feedback Control Systems via the Internet 171 7.1. Problem statement 171 7.2. Infrastructural topologies 172 7.2.1. Basic topology 172 7.2.2. Advanced topologies 173 7.3. Remotely operated laboratories via the Internet 176 7.3.1. Comparison between classical and remotely operated laboratories 176 7.3.2. Infrastructures on the server side of a remotely operated laboratory 178 7.3.3. Criteria for the creation of a remotely operated laboratory 180 7.4. Exercises and solutions 180 Chapter 8: Remotely Operated Automation Laboratory via the Internet 187 8.1. Introduction to remotely operated automation laboratory 187 8.1.1. Creation context 187 8.1.2. Didactic context 188 8.1.3. Specifications 188 8.2. Design and implementation of the experimental system 189 8.2.1. Descriptive diagrams 189 8.2.2. Dynamic model of the real power lighting system 191 8.2.3. Dynamic model of the PID controller for power lighting 191 8.2.4. MMMI-aided Labview application 192 8.3. Topology of the remotely operated automation laboratory 193 8.3.1. Hardware infrastructure 194 8.3.2. Specialized infrastructure on the server side 194 8.3.3. Infrastructure on the remote operator side 196 8.4. Use of a remotely operated laboratory via the Internet 196 8.4.1. Procedure instruction sheet 196 8.4.2. Samples of test results obtained with REOPAULAB 197 8.5. Exercises and solutions 200 Appendices 207 Appendix 1. Table of z-transforms 209 Appendix 2. Matlab Elements Used in this Book 211 Appendix 3. Discretization of Transfer Functions 215 Bibliography 217 Index 219

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Book SynopsisThis book presents recent advances in fault diagnosis strategies for complex dynamic systems. Its impetus derives from the need for an overview of the challenges of the fault diagnosis technique, especially for those demanding systems that require reliability, availability, maintainability and safety to ensure efficient operations. Moreover, the need for a high degree of tolerance with respect to possible faults represents a further key point, primarily for complex systems, as modeling and control are inherently challenging, and maintenance is both expensive and safety-critical.Diagnosis and Fault-tolerant Control 1 also presents and compares different diagnosis schemes using established case studies that are widely used in related literature. The main features of this book regard the analysis, design and implementation of proper solutions for the problems of fault diagnosis in safety critical systems. The design of the considered solutions involves robust data-driven, model-based approaches.Table of ContentsIntroduction ix Vicenç Puig and Silvio Simani Chapter 1 Mathematical Modeling and Fault Description 1 Silvio Simani 1.1. Introduction 1 1.2. Model-based FDI techniques 2 1.3. Modeling of faulty systems 3 1.3.1. Fault modeling and description 5 1.3.2. Mathematical description 6 1.4. Residual generation 11 1.5. Residual generation techniques 14 1.5.1. Residual generation via parameter estimation 15 1.5.2. Observer-based approaches 18 1.5.3. Fault detection via parity equations 24 1.6. Change detection and symptom evaluation 28 1.7. Residual generation robustness problem 30 1.7.1. FDI H∞ approach 32 1.7.2. Active and passive disturbance decoupling 35 1.8. Fault diagnosis technique integration 36 1.8.1. Fuzzy logic for residual generation 37 1.8.2. Neural networks for fault diagnosis 38 1.8.3. Neuro-fuzzy approaches to FDI 40 1.8.4. Fault detectability and isolability 42 1.8.5. NF model structure identification 43 1.8.6. NF residual generation for FDI 44 1.9. Conclusion 46 1.10. References 47 Chapter 2 Structural Analysis 57 Mattias Krysander and Erik Frisk 2.1. Introduction 57 2.2. Background 58 2.2.1. Structural models 58 2.2.2. Dulmage–Mendelsohn decomposition and matchings 60 2.2.3. Dulmage–Mendelsohn decomposition and simulation 63 2.3. Fault isolability analysis 64 2.3.1. Fault detectability analysis 64 2.3.2. Fault isolability analysis 65 2.3.3. Canonical isolability decomposition of the overdetermined part 67 2.4. Testable submodels 69 2.4.1. Basic definitions 69 2.4.2. MSO algorithm 71 2.4.3. Residual generation based on matching 72 2.5. Sensor placement 74 2.5.1. The basic sensor placement problem 74 2.5.2. A structural approach 75 2.6. Summary and discussion 80 2.7. References 81 Chapter 3 Set-based Fault Detection and Isolation 83 Ye Wang and Vicenç Puig 3.1. Introduction 83 3.2. Notations, definitions and properties 84 3.3. Problem statement 86 3.3.1. Uncertain discrete-time linear systems 86 3.3.2. Set-based methods 86 3.3.3. FDI problem statement 88 3.4. Proposed techniques 89 3.4.1. Set-membership approach 89 3.4.2. Zonotopic observer 90 3.4.3. Relationship between set-based methods 91 3.5. Design methods 92 3.5.1. Robustness conditions 92 3.5.2. Fault sensitivity condition 96 3.6. Fault detection and isolation procedures 99 3.6.1. Fault detection 99 3.6.2. Fault isolation 100 3.7. Application example: quadruple-tank system 101 3.7.1. Results with robustness condition 105 3.7.2. Results with robustness and fault sensitivity conditions 105 3.8. Conclusion 105 3.9. References 109 Chapter 4 Diagnosis of Stochastic Systems 111 Gregory Provan 4.1. Introduction 111 4.2. Stochastic diagnosis task 113 4.2.1. Notation 113 4.2.2. Problem formulation 113 4.2.3. Representing uncertainty 115 4.3. Inference methods for diagnosis task 116 4.3.1. Difference with other tasks 116 4.4. Model-based approach 117 4.4.1. Traditional FDD methods 117 4.4.2. Bayesian inversion/filtering 120 4.5. Data-driven approaches 122 4.5.1. ML methods 123 4.5.2. Statistical methods 124 4.6. Hybrid approaches: surrogate methods 125 4.6.1. Fitting surrogate models via sampling 125 4.7. Comparative analysis of approaches 126 4.8. Summary and conclusions 127 4.9. References 128 Chapter 5 Data-Driven Methods for Fault Diagnosis 131 Silvio Simani 5.1. Introduction 131 5.2. Models for linear system fault diagnosis 133 5.3. Parameter estimation methods for fault diagnosis 135 5.3.1. Data-driven method in ideal conditions 135 5.3.2. Data-driven methods in real scenarios 138 5.3.3. Algebraic Frisch scheme 139 5.3.4. Dynamic Frisch scheme 141 5.3.5. MIMO case Frisch scheme 145 5.4. Nonlinear dynamic system identification 146 5.4.1. Piecewise affine model 147 5.4.2. Hybrid model structure 148 5.4.3. Nonlinear system approximation 149 5.4.4. Model continuity and domain partitioning 151 5.4.5. Local affine model estimation 154 5.4.6. Multiple-model estimation 158 5.5. Fuzzy data-driven approach to fault diagnosis 164 5.5.1. Fuzzy model identification 165 5.5.2. Takagi–Sugeno prototypes 167 5.5.3. Data-driven fuzzy modeling 170 5.5.4. Clustering methods 170 5.5.5. Fuzzy c-means clustering algorithms 172 5.5.6. Gustafson–Kessel clustering algorithm 174 5.5.7. Optimal number of clusters 176 5.6. Fuzzy model identification 176 5.6.1. Nonlinear model identification 178 5.6.2. Product space clustering identification 181 5.6.3. Fuzzy clustering model identification 183 5.6.4. Antecedent membership function estimation 183 5.6.5. Estimating consequent parameters 185 5.7. Conclusion 189 5.8. References 189 Chapter 6 The Artificial Intelligence Approach to Model-based Diagnosis 197 Belarmino Pulido, Carlos J. Alonso-González and Anibal Bregon 6.1. Introduction 197 6.2. Case studies 199 6.3. Knowledge-based diagnosis systems 201 6.3.1. Diagnosis task and system model 203 6.3.2. Diagnosis of physical devices 206 6.3.3. Limits of KBS for diagnosis of physical devices 207 6.4. Model-based diagnosis 208 6.4.1. Formalization of consistency-based diagnosis and its first implementation, GDE 209 6.5. CBD for dynamic systems 217 6.5.1. Different approaches for CBD of dynamic systems 219 6.5.2. PCs for the three-tank system case study 222 6.6. Conclusion 224 6.7. References 226 List of Authors 231 Index 233 Summary of Volume 2 237

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Book SynopsisThis book presents recent advances in fault diagnosis and fault-tolerant control of dynamic processes. Its impetus derives from the need for an overview of the challenges of the fault diagnosis technique and sustainable control, especially for those demanding systems that require reliability, availability, maintainability, and safety to ensure efficient operations. Moreover, the need for a high degree of tolerance with respect to possible faults represents a further key point, primarily for complex systems, as modeling and control are inherently challenging, and maintenance is both expensive and safety-critical.Diagnosis and Fault-tolerant Control 2 also presents and compares different fault diagnosis and fault-tolerant schemes, using well established, innovative strategies for modeling the behavior of the dynamic process under investigation. An updated treatise of diagnosis and fault-tolerant control is addressed with the use of essential and advanced methods including signal-based, model-based and data-driven techniques. Another key feature is the application of these methods for dealing with robustness and reliability.Table of ContentsChapter 1 Nonlinear Methods for Fault Diagnosis 1 Silvio Simani and Paolo Castaldi 1.1. Introduction 1 1.2. Fault diagnosis tasks 2 1.2.1. Residual generation task 5 1.2.2. Residual evaluation task 8 1.3. Model-based fault diagnosis 9 1.3.1. Parity space relations 9 1.3.2. Observer-based approaches 12 1.3.3. Nonlinear filtering methods 14 1.3.4. Nonlinear geometric approach strategy 17 1.4. Data-driven fault diagnosis 20 1.4.1. Online identification methods 21 1.4.2. Machine learning approaches to fault diagnosis 24 1.5. Model-based and data-driven integrated fault diagnosis 34 1.6. Robust fault diagnosis problem 42 1.7. Summary 47 1.8. References 48 Chapter 2 Linear Parameter Varying Methods 57 Mickael Rodrigues, Habib Hamdi and Didier Theilliol 2.1. Introduction 57 2.2. Preliminaries: a classical approach 60 2.3. Problem statement 62 2.4. Robust active fault-tolerant control design 65 2.4.1. Robust observer-based FTC design 65 2.4.2. Stability analysis 68 2.5. Application: an anaerobic bioreactor 75 2.6. Conclusion 81 2.7. References 81 Chapter 3 Fuzzy and Neural Network Approaches 85 Marcin Witczak, Marcin Pazera, Norbert Kukurowski and Marcin Mrugalski 3.1. Introduction 85 3.2. Fuzzy model design 87 3.2.1. Takagi–Sugeno systems 87 3.2.2. Generation of TS models via nonlinear embedding 88 3.3. Neural model design 90 3.3.1. Recurrent neural network 90 3.3.2. Identification of the neural model uncertainty 93 3.4. Fault estimation and diagnosis 94 3.4.1. Actuator fault estimation using neural networks 94 3.4.2. Sensor and actuator fault estimation using fuzzy logic 97 3.5. Fault-tolerant control 101 3.5.1. An overview of the fault-tolerant scheme 101 3.5.2. Robust fault estimation and control 103 3.5.3. Derivation of a robust invariant set 106 3.5.4. Efficient predictive FTC 106 3.6. Illustrative examples 110 3.6.1. Sensor and actuator fault estimation example 110 3.6.2. Fault-tolerant control example 113 3.7. Conclusion 115 3.8. Acknowledgment 116 3.9. References 116 Chapter 4 Model Predictive Control Methods 121 Krzysztof Patan 4.1. Introduction 121 4.2. Idea of MPC 122 4.3. Robustness of MPC 125 4.4. Neural-network-based robust MPC 126 4.4.1. Neural network models 127 4.4.2. Nonlinear MPC 130 4.4.3. Approximate MPC 130 4.4.4. Robust nonlinear MPC 132 4.4.5. Robust approximate MPC 132 4.5. Robust control of a pneumatic servo 134 4.5.1. Robust nonlinear neural-network-based MPC 135 4.5.2. Robust approximate neural-network-based MPC 139 4.6. Conclusion 140 4.7. References 140 Chapter 5 Nonlinear Modeling for Fault-tolerant Control 143 Silvio Simani and Paolo Castaldi 5.1. Introduction 143 5.1.1. Joint fault diagnosis and control 147 5.1.2. Nonlinear adaptive fault estimators 149 5.1.3. Fuzzy fault-tolerant control 161 5.1.4. Recursive adaptive control 164 5.1.5. Sustainable control 174 5.2. Fault-tolerant control strategies 175 5.2.1. Fault tolerance and compensation 177 5.3. Fault diagnosis and tolerant control 180 5.3.1. Fault-tolerant control design 183 5.4. Summary 186 5.5. References 187 Chapter 6 Virtual Sensors and Actuators 193 Damiano Rotondo and Vicenç Puig 6.1. Introduction 193 6.2. Problem statement 194 6.3. Virtual sensors and virtual actuators 198 6.4. LMI-based design 202 6.5. Additional considerations 205 6.6. Application example 208 6.6.1. Virtual actuator 209 6.6.2. Virtual sensors 210 6.7. Conclusion 212 6.8. References 212 Chapter 7 Conclusions 215 Vicenç Puig and Silvio Simani 7.1. Introduction 215 7.2. Closing remarks 219 7.3. References 229 Chapter 8 Open Research Issues 241 Vicenç Puig and Silvio Simani 8.1. Further works and open problems 241 8.1.1. Sustainable control design objectives 243 8.1.2. Sustainable control concepts and approaches 247 8.1.3. Sustainable control approaches and working methods 249 8.1.4. Sustainable control design ambition 253 8.1.5. Sustainable control innovation potentials 258 8.1.6. Sustainable control expected impacts 259 8.2. Summary 261 8.3. References 262 List of Authors 265 Index 267 Summary of Volume 1 271

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Book SynopsisThe Future of Creative Work provides a unique overview of the changing nature of creative work, examining how digital developments and the rise of intangible capital are causing an upheaval in the social institutions of work. It offers a profound insight into how this technological and social evolution will affect creative professions. Expert international contributors explore how robotics, artificial intelligence, blockchain, global digital platforms and autonomous systems will shape the design, production and consumption of culture. Taking a multidisciplinary approach incorporating creative industries studies, business, education and economics, the book analyses the technological drivers of disruption in the world of creative work. Chapters reveal how these changes will create new axes of power and inequality in the global sphere of creative work, predicting that conventional creative professions will be challenged and different species of creative work will evolve as a result. By charting the impact of digital and technological developments, The Future of Creative Work challenges traditional views of creative work, careers and education. This book will be a valuable resource for students and researchers undertaking creative industries studies. Its discussion of the application of creative careers across the economy will also be beneficial for scholars and practitioners interested in business, economics, and advertising and marketing studies.Trade Review'What do we know about the future of creative work? A lot more now, thanks to Greg Hearn's new book and the impressive list of contributors. This book, with fifteen chapters from a high quality, international authorship contributes to our knowledge through four sections: the evolution of creative work, digital disruption and creative work, changing contexts of creative work, and educating for the future. This book will be both a thought provoking and entertaining contribution for people interested in creative work and the teaching and researching in the area.' --Keith Townsend, Griffith University, Australia'This volume offers a multidisciplinary and global perspective on the many forms and functions of creative work and creative workers. Moreover, the work delves deeply into the technological innovations that are transforming creative work and the careers of creative workers. A key insight is how communication technologies are promoting spatially disaggregated collaborations amongst creative and non-creative work participants. These findings offer unique insights into how creative work practices may be applicable to a wider scope of employment disrupted by the global COVID pandemic.' --Robert DeFillippi, Suffolk University, US'The Future of Creative Work is a comprehensive exploration of issues that so many of us think about. The book looks into all the corners of creative work, old and new. In doing this, it considers changing technology, changing modes of work, and the production of social, cultural and economic value through creative work. 3D printers, robots and AI, teaching and learning in the arts like dance, the effects of casualisation and nomadism in the creative economy, and questions about whether creativity future-proofs students and workers are all important matters considered in the book. It is a perfect book for this time.' --David Rooney, Macquarie University, AustraliaTable of ContentsContents: 1 The future of creative work: creativity and digital disruption 1 Greg Hearn PART I THE EVOLUTION OF CREATIVE WORK 2 The creative economy: the rise and risks of intangible capital and the future of creative work 14 Greg Hearn and Marion McCutcheon 3 The relationship between creative employment and local economies outside capital cities 34 Greg Hearn, Stuart Cunningham, Marion McCutcheon and Mark David Ryan 4 A taxonomic structural change perspective on the economic impact of robots and artificial intelligence on creative work 57 Ben Vermeulen, Andreas Pyka and Pier Paolo Saviotti PART II DIGITAL DISRUPTION AND CREATIVE WORK 5 New economic infrastructures for creative work 78 Ellie Rennie and Jason Potts 6 Automated journalism: expendable or supplementary for the future of journalistic work? 99 Aljosha Karim Schapals 7 Robotics and artificial intelligence in architecture: what skills will architects need in 2050? 108 Cori Stewart, Glenda Amayo Caldwell, Müge Belek Fialho Teixeira and Jonathan Roberts 8 Museum curation in the digital age 123 Rui Oliveira Lopes PART III CHANGING CONTEXTS OF CREATIVE WORK 9 The role of casual creative environments for creative work in cities: implications for the future creative city 141 Ana Bilandzic, Onur Mengi and Greg Hearn 10 Digital nomadism: mobility, millennials and the future of work in the online gig economy 156 Beverly Yuen Thompson 11 Playing with TikTok: algorithmic culture and the future of creative work 172 Natalie Collie and Caroline Wilson-Barnao 12 Managing embedded creative work: the challenge of causal ambiguity 189 Cliff Bowman and Juani Swart PART IV EDUCATING FOR THE FUTURE OF CREATIVE WORK 13 Creativity 2.0: new approaches to creative economy work and education in the creative industries 212 Chris Bilton 14 When dancers learn to teach dance: how creatives acquire expertise in multiple domains to improve employability 229 Jose Hilario Pereira Rodrigues 15 Do creative skills future-proof your job? Creativity and the future of work in an age of exponential technological advancement 245 Ruth Bridgstock, Russell Tytler and Peta White Index 259

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Book SynopsisGiving an overview of the challenges in the control of bioprocesses, this comprehensive book presents key results in various fields, including: dynamic modeling; dynamic properties of bioprocess models; software sensors designed for the on-line estimation of parameters and state variables; control and supervision of bioprocesses.Table of ContentsChapter 1. What are the Challenges for the Control of Bioprocesses? 11 Denis DOCHAIN 1.1. Introduction . 11 1.2. Specific problems of bioprocess control 12 1.3. A schematic view of monitoring and control of a bioprocess 12 1.4. Modeling and identification of bioprocesses: some key ideas 13 1.5. Software sensors: tools for bioprocess monitoring 14 1.6. Bioprocess control: basic concepts and advanced control15 1.7. Bioprocess monitoring: the central issue 15 1.8. Conclusions 16 1.9. Bibliography 16 Chapter 2. Dynamic Models of Biochemical Processes: Properties of Models 17 Olivier BERNARD and Isabelle QUEINNEC 2.1. Introduction 17 2.2. Description of biochemical processes 18 2.2.1. Micro-organisms and their use 18 2.2.2. Types of bioreactors 19 2.2.3. Three operating modes 19 2.3. Mass balance modeling 21 2.3.1. Introduction 21 2.3.2. Reaction scheme 21 2.3.3. Choice of reactions and variables 23 2.3.4. Example 1 23 2.4. Mass balance models 24 2.4.1. Introduction 24 2.4.2. Example 2 24 2.4.3. Example 3 25 2.4.4. Matrix representation 25 2.4.4.1. Example 2 (continuation) 26 2.4.4.2. Example 1 (continuation) 26 2.4.5. Gaseous flow 27 2.4.6. Electroneutrality and affinity constants 27 2.4.7. Example 1 (continuation) 28 2.4.8. Conclusion 29 2.5. Kinetics 30 2.5.1. Introduction 30 2.5.2. Mathematical constraints 30 2.5.2.1. Positivity of variables 30 2.5.2.2. Variables necessary for the reaction 31 2.5.2.3. Example 1 (continuation) 31 2.5.2.4. Phenomenological knowledge 31 2.5.3. Specific growth rate 32 2.5.4. Representation of kinetics by means of a neural network 34 2.6. Validation of the model 35 2.6.1. Introduction 35 2.6.2. Validation of the reaction scheme 35 2.6.2.1. Mathematical principle 35 2.6.2.2. Example 4 36 2.6.3. Qualitative validation of model 37 2.6.4. Global validation of the model 39 2.7. Properties of the models 39 2.7.1. Boundedness and positivity of variables 39 2.7.2. Equilibrium points and local behavior 40 2.7.2.1. Introduction 40 2.8. Conclusion 42 2.9. Bibliography 43 Chapter 3. Identification of Bioprocess Models 47 Denis DOCHAIN and Peter VANROLLEGHEM 3.1. Introduction 47 3.2. Structural identifiability 48 3.2.1. Development in Taylor series 49 3.2.2. Generating series 50 3.2.3. Examples for the application of the methods of development in series 50 3.2.4. Some observations on the methods for testing structural identifiability 51 3.3. Practical identifiability 52 3.3.1. Theoretical framework 52 3.3.2. Confidence interval of the estimated parameters 54 3.3.3. Sensitivity functions 55 3.4. Optimum experiment design for parameter estimation (OED/PE) 57 3.4.1. Introduction 57 3.4.2. Theoretical basis for the OED/PE 59 3.4.3. Examples 61 3.5. Estimation algorithms 63 3.5.1. Choice of two datasets 63 3.5.2. Elements of parameter estimation: least squares estimation in the linear case 64 3.5.3. Overview of the parameter estimation algorithms 65 3.6. A case study: identification of parameters for a process modeled for anaerobic digestion 68 3.6.1. The model 69 3.6.2. Experiment design 70 3.6.3. Choice of data for calibration and validation 70 3.6.4. Parameter identification 71 3.6.5. Analysis of the results 75 3.7. Bibliography 75 Chapter 4. State Estimation for Bioprocesses 79 Olivier BERNARD and Jean-Luc GOUZÉ 4.1. Introduction 79 4.2. Notions on system observability 80 4.2.1. System observability: definitions 80 4.2.2. General definition of an observer 81 4.2.3. How to manage the uncertainties in the model or in the output 83 4.3. Observers for linear systems 84 4.3.1. Luenberger observer 85 4.3.2. The linear case up to an output injection 86 4.3.3. Local observation of a nonlinear system around an equilibrium point 86 4.3.4. PI observer 87 4.3.5. Kalman filter 87 4.3.6. The extended Kalman filter 89 4.4. High gain observers 89 4.4.1. Definitions, hypotheses 89 4.4.2. Change of variable 90 4.4.3. Fixed gain observer 91 4.4.4. Variable gain observers (Kalman-like observer) 91 4.4.5. Example: growth of micro-algae 92 4.5. Observers for mass balance-based systems 94 4.5.1. Introduction 94 4.5.2. Definitions, hypotheses 96 4.5.3. The asymptotic observer 96 4.5.4. Example 98 4.5.5. Improvements 99 4.6. Interval observers 101 4.6.1. Principle 102 4.6.2. The linear case up to an output injection 103 4.6.3. Interval estimator for an activated sludge process 105 4.6.4. Bundle of observers 107 4.7. Conclusion 110 4.8. Appendix: a comparison theorem 111 4.9. Bibliography 112 Chapter 5. Recursive Parameter Estimation 115 Denis DOCHAIN 5.1. Introduction 115 5.2. Parameter estimation based on the structure of the observer 116 5.2.1. Example: culture of animal cells 116 5.2.2. Estimator based on the structure of the observer 117 5.2.3. Example: culture of animal cells (continued) 119 5.2.4. Calibration of the estimator based on the structure of the observer: theory 119 5.2.5. Calibration of the estimator based on the structure of the observer: application to the culture of animal cells 124 5.2.6. Experimental results 127 5.3. Recursive least squares estimator 129 5.4. Adaptive state observer 133 5.4.1. Generalization 138 5.5. Conclusions 140 5.6. Bibliography 141 Chapter 6. Basic Concepts of Bioprocess_Control 143 Denis DOCHAIN and Jérôme HARMAND 6.1. Introduction 143 6.2.1. Biological system dynamics 144 6.2.2. Sources of uncertainties and disturbances of biological systems 146 6.3. Stability of biological processes 147 6.3.1. Basic concept of the stability of a dynamic system 147 6.3.2. Equilibrium point 148 6.3.3. Stability analysis 149 6.4. Basic concepts of biological process control 150 6.4.1. Regulation and tracking control 150 6.4.2. Strategy selection: direct and indirect control 151 6.2. Bioprocess control: basic concepts 144 6.4.3. Selection of synthesis method 152 6.5. Synthesis of biological process control laws 153 6.5.1. Representation of systems 153 6.5.2. Structure of control laws 154 6.6. Advanced control laws 160 6.6.1. A nonlinear PI controller 160 6.6.2. Robust control 162 6.7. Specific approaches 165 6.7.1. Pulse control: a dialog with bacteria 165 6.7.2. Overall process optimization: towards integrating the control objectives in the initial stage of bioprocess design 167 6.8. Conclusions and perspectives 170 6.9. Bibliography 170 Chapter 7. Adaptive Linearizing Control and Extremum-Seeking Control of Bioprocesses 173 Denis DOCHAIN, Martin GUAY, Michel PERRIER and Mariana TITICA 7.1. Introduction 173 7.2. Adaptive linearizing control of bioprocesses 174 7.2.1. Design of the adaptive linearizing controller 174 7.2.2. Example 1: anaerobic digestion 176 7.2.2.1. Model order reduction 177 7.2.2.2. Adaptive linearizing control design 179 7.2.3. Example 2: activated sludge process 183 7.3. Adaptive extremum-seeking control of bioprocesses 188 7.3.1. Fed-batch reactor model 189 7.3.2. Estimation and controller design 191 7.3.2.1. Estimation equation for the gaseous outflow rate y 191 7.3.2.2. Design of the adaptive extremum-seeking controller 192 7.3.2.3. Stability and convergence analysis 195 7.3.2.4. A note on dither signal design 196 7.3.3. Simulation results 197 7.4. Appendix: analysis of the parameter convergence 202 7.5. Bibliography 207 Chapter 8. Tools for Fault Detection and Diagnosis 211 Jean-Philippe STEYER, Antoine GÉNOVÉSI and Jérôme HARMAND 8.1. Introduction 211 8.2. General definitions 212 8.2.1. Terminology 212 8.2.2. Fault types 213 8.3. Fault detection and diagnosis 214 8.3.1. Methods based directly on signals 215 8.3.1.1. Hardware redundancy 215 8.3.1.2. Specific sensors 216 8.3.1.3. Comparison of thresholds 217 8.3.1.4. Spectral analysis 217 8.3.1.5. Statistical approaches 218 8.3.2. Model-based methods 218 8.3.2.1. Parity space 219 8.3.2.2. Observers 220 8.3.2.3. Parametric estimation 221 8.3.3. Methods based on expertise 222 8.3.3.1. AI models 223 8.3.3.2. Artificial neural networks 224 8.3.3.3. Fuzzy inference systems 225 8.3.4. Choice and combined use of diverse methods 227 8.4. Application to biological processes 227 8.4.1. “Simple” biological processes 228 8.4.2. Wastewater treatment processes 229 8.5. Conclusion 231 8.6. Bibliography 232 List of Authors 239 Index 241

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Book SynopsisIt is almost universally agreed that consciousness and possession of a conscience are essential characteristics of human intelligence. While some believe it to be impossible to create artificial beings possessing these traits, and conclude that ultimate major goal of Artificial Intelligence is hopeless, this book demonstrates that not only is it possible to create entities with capabilities in both areas, but that they demonstrate them in ways different from our own, thereby showing a new kind of consciousness. This latter characteristic affords such entities performance beyond the reach of humans, not for lack of intelligence, but because human intelligence depends on networks of neurons which impose processing restrictions which do not apply to computers. At the beginning of the investigation of the creation of an artificial being, the main goal was not to study the possibility of whether a conscious machine would possess a conscience. However, experimental data indicate that many characteristics implemented to improve efficiency in such systems are linked to these capacities. This implies that when they are present it is because they are essential to the desired performance improvement. Moreover, since the goal is not to imitate human behavior, some of these structural characteristics are different from those displayed by the neurons of the human brain - suggesting that we are at the threshold of a new scientific field, artificial cognition, which formalizes methods for giving cognitive capabilities to artificial entities through the full use of the computational power of machines.Table of ContentsAcknowledgements ix Note on the Terminology xi Chapter 1. Presenting the Actors 1 1.1. The book 1 1.2. Human and artificial beings 4 1.3. The computer 7 1.4. The author 9 1.5. CAIA, an artificial AI scientist 11 1.6. The research domains of CAIA 15 1.7. Further reading 19 Chapter 2. Consciousness and Conscience 21 2.1. Several meanings of “consciousness” 22 2.2. Extending the meaning of “conscience” for artificial beings 25 2.3. Why is it useful to build conscious artificial beings with a conscience? 29 2.4. Towards an artificial cognition 31 2.4.1. A new kind of consciousness 32 2.4.2. A new kind of conscience 33 Chapter 3. What Does “Itself” Mean for an Artificial Being? 35 3.1. Various versions of an individual 36 3.1.1. The concept of an individual for human beings 36 3.1.2. The boundaries of an artificial being 39 3.1.3. Passive and active versions of an individual 41 3.1.4. Reflexivity 47 3.2. Variants of an individual 49 3.2.1. An individual changes with time 50 3.2.2. Learning by comparing two variants 50 3.2.3. Genetic algorithms 52 3.2.4. The bootstrap 54 3.3. Cloning artificial beings 57 3.3.1. Cloning an artificial being is easy 57 3.3.2. Cloning artificial beings is useful 58 3.4. Dr. Jekyll and Mr. Hyde 61 3.5. The Society of Mind 63 3.6. More on the subject 65 Chapter 4. Some Aspects of Consciousness 67 4.1. Six aspects of consciousness 68 4.1.1. One is in an active state 68 4.1.2. One knows what one is doing 72 4.1.3. One examines his/its internal state 80 4.1.4. One knows what one knows 84 4.1.5. One has a model of oneself 87 4.1.6. One knows that one is different from the other individuals 90 4.2. Some limits of consciousness 92 4.2.1. Some limits of consciousness for man 93 4.2.2. Some limits of consciousness for artificial beings 100 Chapter 5. Why is Auto-observation Useful? 105 5.1. Auto-observation while carrying out a task 105 5.1.1. To guide toward the solution 106 5.1.2. To avoid dangerous situations 111 5.1.3. To detect mistakes 121 5.1.4. To find where one has been clumsy 125 5.1.5. To generate a trace 126 5.2. Auto-observation after the completion of a task 129 5.2.1. Creation of an explanation 130 5.2.2. Using an explanation 133 5.2.3. Finding anomalies 138 Chapter 6. How to Observe Oneself 143 6.1. Interpreting 146 6.2. Adding supplementary orders 150 6.3. Using timed interruptions 154 6.4. Using the interruptions made by the operating system 158 6.5. Knowing its own state 159 6.6. Examining its own knowledge 160 6.7. The agents of the Society of Mind. 165 6.8. The attention 166 6.9. What is “I” 169 Chapter 7. The Conscience 173 7.1. The conscience of human beings 174 7.2. The conscience of an artificial being 179 7.3. Laws for artificial beings 183 7.3.1. Asimov’s laws of robotics 183 7.3.1. How can moral laws be implemented? 184 7.3.3. The present situation 191 Chapter 8. Implementing a Conscience 195 8.1. Why is a conscience helpful? 197 8.1.1. The conscience helps to solve problems 197 8.1.2. The conscience helps to manage its life 198 8.1.3. Two ways to define moral knowledge 199 8.1.4. Who benefits from the conscience of an artificial being? 200 8.2. The conscience of CAIA. 201 8.3. Implicit principles 202 8.4. Explicit principles 206 8.5. The consciences in a society of individuals 215 8.5.1. The Society of Mind. 216 8.5.2. Genetic algorithms 217 Chapter 9. Around the Conscience 219 9.1. Emotions 220 9.2. Changing its conscience 223 9.3. A new human conscience for our relationships with artificial beings 228 Chapter 10. What is the Future for CAIA? 237 Appendices 239 1. Constraint Satisfaction Problems 239 2. How to implement some aspects of consciousness 253 Bibliography 263 Index 269

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Book SynopsisThe idea of autonomous systems that are able to make choices according to properties which allow them to experience, apprehend and assess their environment is becoming a reality. These systems are capable of auto-configuration and self-organization. This book presents a model for the creation of autonomous systems based on a complex substratum, made up of multiple electronic components that deploy a variety of specific features. This substratum consists of multi-agent systems which act continuously and autonomously to collect information from the environment which they then feed into the global system, allowing it to generate discerning and concrete representations of its surroundings. These systems are able to construct a so-called artificial corporeity which allows them to have a sense of self, to then behave autonomously, in a way reminiscent of living organisms.Table of ContentsIntroduction ix List of Algorithms xi Chapter 1 Systems and their Design 1 1.1 Modeling systems 1 1.1.1 Conventional systems 2 1.1.2 Complex systems 3 1.1.3 System of systems 3 1.2 Autonomous systems 5 1.3 Agents and multi-agent systems 6 1.3.1 The weak notion of agent 7 1.3.2 The strong notion of agent 7 1.3.3 Cognitive agents and reactive agents 8 1.3.4 Multi-agent systems 9 1.3.5 Reactive agent-based MAS 10 1.3.6 Cognitive agent-based MAS 11 1.4 Systems and organisms 13 1.5 The issue of modeling an autonomous system 13 Chapter 2 The Global Architecture of an Autonomous System 17 2.1 Introduction 17 2.2 Reactivity of a system 17 2.3 The basic structure of an autonomous system: the substratum 18 2.3.1 A detailed example: smoothing the flow or urban traffic 20 2.4 The membrane of autonomous systems 22 2.4.1 Membrane and information 25 2.5 Two types of proactivity and the notion of artificial organ 26 2.5.1 Weak proactivity 26 2.5.2 Strong proactivity 27 2.5.3 Measuring proactivity with dynamic graphs 30 2.6 Autonomy and current representation 31 2.6.1 Current representation in an autonomous system 32 2.7 The unifying system that generates representations 33 Chapter 3 Designing a Multi-agent Autonomous System 41 3.1 Introduction 41 3.2 The object layer on the substratum 41 3.3 The agent representation of the substratum: interface agents, organs and the notion of sensitivity 44 3.3.1 Artificial organs 46 3.3.2 Sensitivity of the corporeity 47 3.4 The interpretation system and the conception agents 47 3.4.1 The properties of a conception agent in the interpretation system 49 3.4.2 An example 52 3.4.3 Creating a conception agent 57 3.5 Aggregates of conception agents 58 3.6 The intent and the activity of conception agents 60 3.7 Agentifying conception agents 63 3.8 Activity of a conception agent 65 3.9 The three layers of conceptual agentification and the role of control 70 3.9.1 First guiding principle for the architecture of an autonomous system 74 3.10 Semantic lattices and the emergence of representations in the interpretation system 77 3.11 The general architecture of the interpretation system 84 3.12 Agentification of knowledge and organizational memory 86 3.13 Setting up the membrane network of an autonomous system 94 3.14 Behavioral learning of the autonomous system 96 Chapter 4 Generation of Current Representation and Tendencies 105 4.1 Introduction 105 4.2 Generation of current representation and semantic lattices 105 4.2.1 Openness and deployment: major properties of autonomous systems 106 4.2.2 Incentive-based control and evaluation agents 107 4.2.3 Evaluation agents’ access to organizational memory 110 4.2.4 The role of evaluation agents in the extracted lattice 110 4.2.5 The notion of dynamic lattices 110 4.2.6 Algorithms for generating representations 111 4.2.7 Mathematical interpretation 115 4.3 The cause leading the system to choose a concrete intent 116 4.3.1 Determination of intent 118 4.3.2 Intent and tendencies 120 4.4 Presentation of artificial tendencies 123 4.5 Algorithm for the generation of a stream of representations under tendencies 134 Chapter 5 The Notions of Point of View, Intent and Organizational Memory 137 5.1 Introduction 137 5.2 The notion of point of view in the generation of representations 137 5.3 Three organizational principles of the interpretation system for leading the intent 144 5.3.1 Principle of continuity engagement 145 5.3.2 The bifurcation principle 146 5.3.3 The principle of necessary reason and reliability 147 5.4 Algorithms for intent decisions 147 5.6 Organizational memory and the representation of artificial life experiences 151 5.7 Effective autonomy and the role of the modulation component 156 5.8 Degree of organizational freedom 159 Chapter 6 Towards the Minimal Self of an Autonomous System 161 6.1 Introduction 161 6.2 The need for tendencies when leading the system 161 6.3 Needs and desires of the autonomous system 164 6.4 A scaled-down autonomous system: the artificial proto-self 168 6.5 The internal choice of expressed tendencies and the minimal self 171 6.6 The incentive to produce representations 176 6.7 Minimal self affectivity: emotions and sensations 179 6.8 Algorithms for tendency activation 182 6.9 The feeling of generating representations 188 Chapter 7 Global Autonomy of Distributed Autonomous Systems 197 7.1 Introduction 197 7.2 Enhancement of an autonomous system by itself 197 7.3 Communication among autonomous systems in view of their union 201 7.4 The autonomous meta-system composed of autonomous systems 204 7.5 The system generating autonomous systems: the meta-level of artificial living 207 Conclusion 211 Bibliography 213 Index 215

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Book SynopsisThis book addresses the steps needed to monitor health assessment systems and the anticipation of their failures: choice and location of sensors, data acquisition and processing, health assessment and prediction of the duration of residual useful life. The digital revolution and mechatronics foreshadowed the advent of the 4.0 industry where equipment has the ability to communicate. The ubiquity of sensors (300,000 sensors in the new generations of aircraft) produces a flood of data requiring us to give meaning to information and leads to the need for efficient processing and a relevant interpretation. The process of traceability and capitalization of data is a key element in the context of the evolution of the maintenance towards predictive strategies.Table of ContentsIntroduction ix Chapter 1. PHM and Predictive Maintenance 1 1.1. Anticipative maintenance and prognostics 1 1.1.1. New challenges and evolution of the maintenance function 1 1.1.2. Towards an anticipation of failure mechanisms 3 1.2. Prognostics and estimation of the remaining useful life (RUL) 5 1.2.1. What is it? Definition and measures of prognostics 5 1.2.2. How? Prognostic approaches 6 1.3. From data to decisions: the PHM process 9 1.3.1. Detection, diagnostics and prognostics 9 1.3.2. CBM Architecture and PHM process 10 1.4. Scope of the book 12 Chapter 2. Acquisition: From System to Data 15 2.1. Motivation and content 15 2.2. Critical components and physical parameters 16 2.2.1. Choice of critical components – general approach 16 2.2.2. Dependability analysis of the system and related tools 17 2.2.3. Physical parameters to be observed 19 2.3. Data acquisition and storage 20 2.3.1. Choice of sensors 22 2.3.2. Data acquisition 23 2.3.3. Preprocessing and data storage 24 2.4. Case study: toward the PHM of bearings 25 2.4.1. From the “train” system to the critical component “bearing” 25 2.4.2. Experimental platform Pronostia 26 2.4.3. Examples of obtained signals 30 2.5. Partial synthesis 30 Chapter 3. Processing: From Data to Health Indicators 33 3.1. Motivation and content 33 3.2. Feature extraction 35 3.2.1. Mapping approaches 35 3.2.2. Temporal and frequency features 36 3.2.3. Time–frequency features 38 3.3. Feature reduction/selection 48 3.3.1. Reduction of the feature space 48 3.3.2. Feature selection . 54 3.4. Construction of health indicators 62 3.4.1. An approach based on the Hilbert-Huang transform 62 3.4.2. Approach description and illustrative elements 62 3.5. Partial synthesis 63 Chapter 4. Health Assessment, Prognostics and Remaining Useful Life – Part A 67 4.1. Motivation and content 67 4.2. Features prediction by means of connectionist networks 69 4.2.1. Long-term connectionist predictive systems 69 4.2.2. Prediction by means of “fast” neural networks 77 4.2.3. Applications in PHM problems and discussion 84 4.3. Classification of states and RUL estimation 88 4.3.1. Health state assessment without a priori information about the data 88 4.3.2. Toward increased performances: S-MEFC algorithm 93 4.3.3. Dynamic thresholding procedure 95 4.4. Application and discussion 97 4.4.1. Tests data and protocol 97 4.4.2. Illustration of the dynamic thresholding procedure 101 4.4.3. Performances of the approach 104 4.5. Partial synthesis 105 Chapter 5. Health Assessment, Prognostics, and Remaining Useful Life – Part B 109 5.1. Motivation and object 109 5.2. Modeling and estimation of the health state 111 5.2.1. Fundamentals: the Hidden Markov Models (HMM) 111 5.2.2. Extension: mixture of Gaussians HMMs 117 5.2.3. State estimation by means of Dynamic Bayesian Networks 118 5.3. Behavior prediction and RUL estimation 124 5.3.1. Approach: Prognostics by means of DBNs 124 5.3.2. Learning of state sequences 124 5.3.3. Health state detection and RUL estimation 126 5.4. Application and discussion 129 5.4.1. Data and protocol of the tests 129 5.4.2. Health state identification 131 5.4.3. RUL estimation 133 5.5. Partial synthesis 135 Conclusion and Open Issues 137 Bibliography 143 Index 163

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Book SynopsisIn order to solve a particular motion control problem the engineer is initially faced with the task of selecting an appropriate power transmission medium. This is frequently followed by modelling and simulation of the proposed solution in concert with control system analysis and design. The process involves both synthesis and analysis and, in some instances, can be particularly challenging to the engineer. This text covers all aspects of this power transmisison and motion control with particular emphasis placed on current international research and development. This collection of papers should be of value to researchers and practitioners working in the power transmision and motion control community.Table of ContentsDesign; simulation; applications; drives and controls; components and fluids; control - I; control - II.

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Book SynopsisThis two-volume book comprises a comprehensive up-to-date body of knowledge that provides a total in-depth insight into valve and actuator technology – looking not just at control valves, but a whole host of other types including: check valves, shut-off valves, solenoid valves, and pressure relief valves. Research studies within the process industry routinely indicate that the fluid control valve is responsible for 60 to 70% of poor-functioning control systems. Furthermore, valves in general are consistently wrongly selected, regularly misapplied, and often incorrectly installed. A methodology is presented to ensure the optimum selection of size, choice of body and trim materials, components, and ancillaries. Whilst studying the correct procedures for sizing, readers will also learn the correct procedures for calculating the spring 'wind-up' or 'bench set'. Maintenance issues also include: testing for deadband/hysteresis, stick-slip and non-linearity; on-line diagnostics; and signature analysis. Written in a detailed but understandable language, the two volumes are presented in a form suitable for both the beginner, with no prior knowledge of the subject, and the more advanced specialist.

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Book SynopsisThis two-volume book comprises a comprehensive up-to-date body of knowledge that provides a total in-depth insight into valve and actuator technology – looking not just at control valves, but a whole host of other types including: check valves, shut-off valves, solenoid valves, and pressure relief valves. Research studies within the process industry routinely indicate that the fluid control valve is responsible for 60 to 70% of poor-functioning control systems. Furthermore, valves in general are consistently wrongly selected, regularly misapplied, and often incorrectly installed. A methodology is presented to ensure the optimum selection of size, choice of body and trim materials, components, and ancillaries. Whilst studying the correct procedures for sizing, readers will also learn the correct procedures for calculating the spring 'wind-up' or 'bench set'. Maintenance issues also include: testing for deadband/hysteresis, stick-slip and non-linearity; on-line diagnostics; and signature analysis. Written in a detailed but understandable language, the two volumes are presented in a form suitable for both the beginner, with no prior knowledge of the subject, and the more advanced specialist.

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Book SynopsisThis volume contains the proceedings of the RAAD 2018 conference, covering major areas of research and development in robotics. It provides an overview on the advances in robotics, more specifically in novel design and applications of robotic systems; dexterous grasping, handling and intelligent manipulation; intelligent cooperating and service robots; advanced robot control; human-robot interfaces; robot vision systems and visual serving techniques; mobile robots; humanoid and walking robots; field and agricultural robotics; bio-inspired and swarm robotic systems; developments towards micro and nano-scale robots; aerial, underwater and spatial robots; robot integration in holonic manufacturing; personal robots for ambient assisted living; medical robots and bionic prostheses; intelligent information technologies for cognitive robots etc. The primary audience of the work are researchers as well as engineers in robotics and mechatronics. Table of Contents

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Book SynopsisThis book covers specific aspects of submarine hydrodynamics in a very practical manner. The author reviews basic concepts of ship hydrodynamics and goes on to show how they are applied to submarines, including a look at the use of physical model experiments. The book is intended for professionals working in submarine hydrodynamics, as well as for advanced students in the field.This revised edition includes updated information on empirical methods for predicting the hydrodynamic manoeuvring coefficients, and for predicting the resistance of a submarine. It also includes new material on how to assess propulsors, and includes measures of wake distortion, which has a detrimental influence on propulsor performance. Additional information on safe manoeuvring envelopes is also provided. The wide range of references has been updated to include the latest material in the field.Table of Contents1 Introduction.- 2 Hydrostatics and Control.- 3 Manoeuvring and Control.- 4 Resistance and Flow.- 5 Propulsion.- 6 Appendage Design.- Hydro-Acoustic Performance.

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Book SynopsisThis monograph is the first of its kind to present innovative research results on truncated predictor feedback (TPF) designs for general linear systems with input delay. Beginning with a brief review of time delay systems, the first half of the book focuses on TPF with a constant feedback parameter. Both state feedback and output feedback are considered. It is established that TPF achieves stabilization in the presence of an arbitrarily large bounded delay if the open loop system is not exponentially unstable. Examples are presented to illustrate that TPF may fail to stabilize an exponentially unstable system when the delay is sufficiently large. Bounds on the delay are then established under which stabilization can be achieved. The second half of the book explores variations of the TPF laws designed with a non-constant feedback parameter to accommodate unknown delays and improve closed-loop performance. The authors employ a step-by-step approach to presenting the ultimate result on a completely delay-independent feedback law. Truncated Predictor Based Feedback Designs for Linear Systems with Input Delay will appeal to control engineers, control theorists, and graduate students studying control systems. This volume will also be a valuable resource for engineers and applied mathematicians interested in dynamic systems with time delays.Table of ContentsPreface.- Notation.- Introduction.- Truncated Predictor Feedback for Continuous-Time Linear Systems.- Truncated Predictor Feedback for Continuous-Time Linear Systems for Discrete-Time Linear Systems.- Truncated Predictor Feedback for Exponentially Unstable Linear Systems.- Delay Independent Truncated Predictor Feedback.- Adaptive Feedback Laws to Accommodate Unknown Delay.- Conclusions.

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Book SynopsisThis textbook offers a comprehensive introduction to the methodological and technical knowledge necessary for the development of embedded systems. At first, the foundations of embedded systems from the fields of electronics, systems theory and control theory are introduced for computer scientists and engineers without extensive knowledge of electrical engineering. Subsequently, system components as well as digital communication between embedded system nodes are discussed. The book ends with procedures for the analysis of embedded systems and for real-time processing. It is aimed at students and users of computer science as well as engineers, physicists and mathematicians who are interested in the basics of developing embedded systems.Table of ContentsElectrical and electronic basics.- Electrotechnical basics.- Electrical networks.- Basic electronic circuits.- Systems theory.- Control theory.- Signal processing

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Book SynopsisThis monograph provides a comprehensive analysis of the control of singularly perturbed time delay systems. Expanding on the author’s previous work on controllability of linear systems with delays in the state and control variables, this volume’s comprehensive coverage makes it a valuable addition to the field. Each chapter is self-contained, allowing readers to study them independently or in succession.After a brief introduction, the book systematically examines properties of different classes of singularly perturbed time delay systems, including linear time-dependent systems with multiple point-wise and distributed state delays. The author then considers more general singularly perturbed systems with state and control delays. Euclidean space controllability for all of these systems is also discussed, using numerous examples from real-life models throughout the text to illustrate the results presented. More technically complicated proofs are presented in separate subsections. The final chapter includes a section dedicated to non-linear time delay systems.This book is ideal for researchers, engineers, and graduate students in systems science and control theory. Other applied mathematicians and researchers working in biology and medicine will also find this volume to be a valuable resource.Trade Review“This book is a comprehensive description of known results on the controllability of possibly non-autonomous two-time-scales singularly perturbed control systems with linear dynamics. … The results are based on a series of papers by the author himself. The proofs given in the book provide more details, and most results are obtained here in a generalized setting … . All results are richly illustrated by academic and application oriented examples.” (Mario Sigalotti, Mathematical Reviews, October, 2022)The book is rich in content and detailed in proof. One of the biggest features of this book is that theoretical results, obtained in the book, are illustrated by numerous examples … which is more convenient for readers to read and understand. The book can be helpful for researchers and engineers, working in … electrical engineering, mechanical and aerospace engineering … .” (Liping Chen, zbMATH 1472.93014, 2021)Table of ContentsIntroduction.- Singularly Perturbed Linear Time Delay Systems.- Euclidean Space Output Controllability of Linear Systems with State Delays.- Complete Euclidean Space Controllability of Linear Systems with State and Control Delays.- First-Order Euclidean Space Controllability Conditions for Linear Systems with Small State Delays.- Miscellanies

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Book SynopsisThis book addresses some of the challenges suffered by the well-known and robust sliding-mode control paradigm. The authors show how the fusion of fuzzy systems with sliding-mode controllers can alleviate some of these problems and promote applicability.Fuzzy systems used as soft switches eliminate high-frequency signal oscillations and can substantially lower the noise sensitivity of sliding-mode controllers. The amount of a priori knowledge required concerning the nominal structure and parameters of a nonlinear system is also shown to be much reduced by exploiting the general function-approximation property of fuzzy systems so as to use them as identifiers. The main features of this book include:• a review of various existing structures of sliding-mode fuzzy control;• a guide to the fundamental mathematics of sliding-mode fuzzy controllers and their stability analysis;• state-of-the-art procedures for the design of a sliding-mode fuzzy controller;• source codes including MATLAB® and Simulink® codes illustrating the simulation of these controllers, particularly the adaptive controllers;• a short bibliography for each chapter for readers interested in learning more on a particular subject; and• illustrative examples and simulation results to support the main claims made in the text.Academic researchers and graduate students interested in the control of nonlinear systems and particularly those working in sliding-mode controller design will find this book a valuable source of comparative information on existing controllers and ideas for the development of new ones.Table of ContentsMathematica Preliminaries.- Fuzzy and Fuzzy Neural-Network Systems: Type 1 and Type 2.- Sliding-Mode Control: Design, Advantages and Challenges.- Sliding-Mode Fuzzy-Logic Controllers.- Sliding-Mode Fuzzy Neural-Network Controllers.- Sliding-Mode Fuzzy Neural-Network Controllers to Control Systems over Networks.- Simulation Results.

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Book SynopsisThis open access book contains observations, outlines, and analyses of educational robotics methodologies and activities, and developments in the field of educational robotics emerging from the findings presented at FabLearn Italy 2019, the international conference that brought together researchers, teachers, educators and practitioners to discuss the principles of Making and educational robotics in formal, non-formal and informal education.The editors’ analysis of these extended versions of papers presented at FabLearn Italy 2019 highlight the latest findings on learning models based on Making and educational robotics. The authors investigate how innovative educational tools and methodologies can support a novel, more effective and more inclusive learner-centered approach to education. The following key topics are the focus of discussion: Makerspaces and Fab Labs in schools, a maker approach to teaching and learning; laboratory teaching and the maker approach, models, methods and instruments; curricular and non-curricular robotics in formal, non-formal and informal education; social and assistive robotics in education; the effect of innovative spaces and learning environments on the innovation of teaching, good practices and pilot projects. Table of ContentsIntroduction.- Chapter 1. Introduction to the Main Topics.- Chapter 2. Keynotes.- Chapter 3. Maker Spaces and Fablabs at school: a maker approach to teaching and learning.- Chapter 4. Laboratory Teaching with the makers approach: models, methods and instruments.- Chapter 5. Curricular and not curricular robotics in formal, non-formal and informal education.- Chapter 6. Educational technologies and assistive robotics.- Chapter 7. How innovative spaces and learning environment condition the transformation of teaching: good practices and pilot projects.- Conclusions

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Book SynopsisThis textbook offers a tutorial introduction to robotics and control which is light and easy to absorb. The practice of robotics and control both involve the application of computational algorithms to data. Over the fairly recent history of the fields of robotics and control a very large body of algorithms has been developed. However this body of knowledge is something of a barrier for anybody entering the field, or even looking to see if they want to enter the field — What is the right algorithm for a particular problem?, and importantly: How can I try it out without spending days coding and debugging it from the original research papers? The author has maintained two open-source MATLAB Toolboxes for more than 10 years: one for robotics and one for vision. The key strength of the Toolboxes provides a set of tools that allow the user to work with real problems, not trivial examples. For the student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used —instant gratification in just a couple of lines of MATLAB code. The code can also be the starting point for new work, for researchers or students, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. The purpose of this book is to expand on the tutorial material provided with the toolboxes, add many more examples, and to weave this into a narrative that covers robotics and control separately and together. The author shows how complex problems can be decomposed and solved using just a few simple lines of code, and hopefully to inspire up and coming researchers. The topics covered are guided by the real problems observed over many years as a practitioner of both robotics and control. It is written in a light but informative style, it is easy to read and absorb, and includes a lot of Matlab examples and figures. The book is a real walk through the fundamentals of robot kinematics, dynamics and joint level control, and covers both mobile robots (control, path planning, navigation, localization and SLAM) and arm robots (forward and inverse kinematics, Jacobians, dynamics and joint level control). “An authoritative book, reaching across fields, thoughtfully conceived and brilliantly accomplished!” Oussama Khatib, StanfordTrade Review“Written in a light but quite informative style, the book is intended for students and scientists and should be of interest to practicing and research engineers as well as Ph.D. students in the field of modeling and control of spatial mechanisms, manipulators and robots.” (Clementina Mladenova, zbMATH 1492.93004, 2022)Table of ContentsIntroduction.- Representing Position and Orientation.- Time and Motion.- Mobile Robots.- Navigation.- Localization.- Robot Arm Kinematics,- Manipulator Velocity.- Dynamics and Control.- Appendices.

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Book SynopsisThis book presents select proceedings of the International Conference on Intelligent Automation and Soft Computing (IASC2021). Various topics covered in this book include AI algorithm, neural networks, pattern recognition, machine learning, blockchain technology, system engineering, computer vision and image processing, adaptive control and robotics, big data and data processing, networking and security. The book is a valuable reference for beginners, researchers, and professionals interested in artificial intelligence, automation, and soft computing. Table of ContentsApplication of the Optimal Lntegrated Medical Intelligence System to Strengthen Physical Lmmunity.- Simultaneous Detection of Optic Disc and Macular Concave Center Using Artificial Intelligence Target Detection Algorithms.- Cuckoo Optimization Algorithm Visual Positioning System Based on Particle Swarm Algorithm.- An Improved Lion Swarm Algorithm Based on Reinforcement Learning.- Research on Intelligent Testing Method of Automobiles Fuel Consumption Based on Ultrasonic Technology.- Discovering the Coopetition Relationship Between Agents Using Clustering.- An Oxygen Forecasting Strategy for Waterless Live Fish Transportation Based on IPSO-GRU Method.- Damping Effect of A Controlled Nonlinear Meso-scale Beam under Periodic Excitation.- Weighted Histogram Block Detection Algorithm for Digital Trunking Terminal.- A GPU-based Energy Saving Algorithm for Smart Buildings.- Dynamic Event-Triggered Control of Networked Control Systems under Sensor and Actuator Faults.

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Book SynopsisThis book presents Proceedings of the 2021 Intelligent Systems Conference which is a remarkable collection of chapters covering a wider range of topics in areas of intelligent systems and artificial intelligence and their applications to the real world. The conference attracted a total of 496 submissions from many academic pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-review process. Of the total submissions, 180 submissions have been selected to be included in these proceedings. As we witness exponential growth of computational intelligence in several directions and use of intelligent systems in everyday applications, this book is an ideal resource for reporting latest innovations and future of AI. The chapters include theory and application on all aspects of artificial intelligence, from classical to intelligent scope.We hope that readers find the book interesting and valuable; it provides the state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of the future research. Table of ContentsLexDivPara: A Measure of Paraphrase Quality with Integrated Sentential Lexical Complexity.- The Potential of Machine Learning Algorithms for Sentiment Classification of Students' Feedback on MOOC.- Towards an Automated Language Acquisition System for Grounded Agency.- Automatic Monitoring and Analysis of Brands using Data Extracted from Twitter in Romanian.- Natural Language Processing in the Support of Business Organization Management.- Discovering Influence of Yelp Reviews using Hawkes Point Processes.- Automated Corpus Annotation for Cybersecurity Named Entity Recognition with Small Keyword Dictionary.- Benchmarking Virtual Reinforcement Learning Algorithms to Balance a Real Inverted Pendulum.- Local-Minimum-Free Artificial Potential Field Method for Obstacle Avoidance.- Experimental Design of Artificial Neural-network Solutions for Traffic Sign Recognition.- Spatial Modelling and Microstructural Modulation of Porous Pavement Materials for Seepage Control in Smart Cities.- ViewClassifier: Visual Analytics on Performance Analysis for Imbalanced Fatal Accident Data.

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