Automatic control engineering Books

Book SynopsisPart I: Visions and Overviews.- Future Directions in Robotic Surgery.- Military Robotic Combat Casualty Extraction and Care.- Telemedicine for the Battlefield: Present and Future Technologies.- Overcoming Barriers to Wider Adoption of Mobile Telerobotic Surgery: Engineering, Clinical and Business Challenges.- Part II: Systems.- Accurate Positioning for Intervention on the Beating Heart using a Crawling Robot.- Miniature In Vivo Robots for NOTES.- A Compact, Simple, and Robust Teleoperated Robotic Surgery System.- Raven - Optimization of a Surgical Robot and Experiments in Telesurgery.- The da Vinci Surgical System.- RIO: Robotic-Arm Interactive Orthopedic System MAKOplasty User Interactive Haptic Orthopedic Robotics.- Robotic Surgery: Enabling Technology. Enabling Medical Robotics for the Next Generation of Minimally Invasive Procedures: Minimally Invasive Cardiac Surgery with Single Port Access.- Wireless Intraocular Microrobots: Opportunities and Challenges.- Single and Multiple RobTrade ReviewFrom the reviews:“This book is great collection and well arranged. … the study results within this book is very useful for clinical surgeon. … every surgical department in any hospital should got this book.” (Amazon.com, May, 2011)Table of ContentsPart I: Visions and Overviews.- Future Directions in Robotic Surgery.- Military Robotic Combat Casualty Extraction and Care.- Telemedicine for the Battlefield: Present and Future Technologies.- Overcoming Barriers to Wider Adoption of Mobile Telerobotic Surgery: Engineering, Clinical and Business Challenges.- Part II: Systems.- Accurate Positioning for Intervention on the Beating Heart using a Crawling Robot.- Miniature In Vivo Robots for NOTES.- A Compact, Simple, and Robust Teleoperated Robotic Surgery System.- Raven - Optimization of a Surgical Robot and Experiments in Telesurgery.- The da Vinci Surgical System.- RIO: Robotic-Arm Interactive Orthopedic System MAKOplasty – User Interactive Haptic Orthopedic Robotics.- Robotic Surgery: Enabling Technology. Enabling Medical Robotics for the Next Generation of Minimally Invasive Procedures: Minimally Invasive Cardiac Surgery with Single Port Access.- Wireless Intraocular Microrobots: Opportunities and Challenges.- Single and Multiple Robotic Capsules for Endoluminal Diagnosis and Surgery.- Visual Guidance of an Active Handheld Microsurgical Tool.- Swimming Micro Robots for Medical Applications.- Flagellated Bacterial Nanorobots for Medical Interventions in the Human Body.- Part III: Engineering Developments.- Force Feedback and Sensory Substitution for Robot-Assisted Surgery.- Tactile Feedback in Surgical Robotics.- Robotic Techniques for Minimally Invasive Tumor Localization.- Motion Prediction and 3D Tracking of the Beating Heart.-Towards the Development of a Robotic System for Beating Heart Surgery.- Robotic Needle Steering: Design, Modeling, Planning, and Image Guidance.- Macro and Micro Soft-tissue Biomechanics and Tissue Damage.- Objective Assessment of Surgical Skills.- Part IV: Clinical Applications / Overviews.- Telesurgery – Translation Technology to Clinical Practice.- History of Robots in Orthopedics.- Robotic-Assisted Urologic Applications.- Applications of Surgical Robotics in Cardiac Surgery.- Robotics in Neurosurgery.- Applications of Surgical Robotics in Pediatric Surgery.

Read more less

Book Synopsis1 Introduction to the Monograph.- 1.1 Background and Motivation: Transient Response Control.- 1.2 Organization of the Monograph.- 2 Iterative Learning Control: An Overview.- 2.1 Introduction.- 2.2 Literature Review.- 2.3 Problem Formulation.- 3 Linear Time-Invariant Learning Control.- 3.1 Convergence with Zero Error.- 3.2 Convergence with Non-Zero Error.- 3.3 The Nature of the Solution.- 4 LTI Learning Control via Parameter Estimation.- 4.1 System Description.- 4.2 Main Result.- 4.3 Comments.- 5 Finite-Horizon Learning Control.- 5.1 l?-Optimal Learning Control with Memory.- 5.2 Learning Convergence in One Step.- 5.3 Learning Control with Multirate Sampling.- 5.4 Examples.- 5.5 Comments and Extensions.- 6 Nonlinear Learning Control.- 6.1 Learning Control for Nonlinear Systems.- 6.2 Learning Controller for a Class of Nonlinear Systems.- 7 Artificial Neural Networks for Iterative Learning Control.- 7.1 Neural Network Controllers.- 7.2 Static Learning Controller Using an ANN.- 7.3 Dynamical Learning Controller Using an ANN.- 7.4 Reinforcement Learning Controller Using an ANN.- 8 Conclusion.- 8.1 Summary.- 8.2 Directions for Future Research.- Appendix A: Some Basic Results on Multirate Sampling.- A.1 Introduction.- A.3 Basic Result.- Appendix B: Tutorial on Artificial Neural Networks.- B.1 An Introduction to Neural Networks.- B.1.1 Neurons.- B.1.2 Interconnection Topology.- B.1.3 Learning Laws.- B.2 Historical Background.- B.3 Properties of Neural Networks.- B.3.1 Pattern Classification and Associative Memory.- B.3.2 Self-Organization and Feature Extraction.- B.3.3 Optimization.- B.3.4 Nonlinear Mappings.- B.4 Neural Nets and Computers.- B.5 Derivation of Backpropagation.- B.6 Neural Network References.- References.Table of Contents1 Introduction to the Monograph.- 1.1 Background and Motivation: Transient Response Control.- 1.2 Organization of the Monograph.- 2 Iterative Learning Control: An Overview.- 2.1 Introduction.- 2.2 Literature Review.- 2.3 Problem Formulation.- 3 Linear Time-Invariant Learning Control.- 3.1 Convergence with Zero Error.- 3.2 Convergence with Non-Zero Error.- 3.3 The Nature of the Solution.- 4 LTI Learning Control via Parameter Estimation.- 4.1 System Description.- 4.1.1 Notation.- 4.1.2 Parameter Estimator and Learning Control Law.- 4.2 Main Result.- 4.3 Comments.- 5 Finite-Horizon Learning Control.- 5.1 l?-Optimal Learning Control with Memory.- 5.2 Learning Convergence in One Step.- 5.3 Learning Control with Multirate Sampling.- 5.4 Examples.- 5.4.1 DC-Motor.- 5.4.2 Non-Minimum Phase System.- 5.5 Comments and Extensions.- 6 Nonlinear Learning Control.- 6.1 Learning Control for Nonlinear Systems.- 6.2 Learning Controller for a Class of Nonlinear Systems.- 6.2.1 Preliminaries.- 6.2.2 Adaptive Gain Adjustment.- 6.2.3 Simulation Experiment.- 7 Artificial Neural Networks for Iterative Learning Control.- 7.1 Neural Network Controllers.- 7.2 Static Learning Controller Using an ANN.- 7.3 Dynamical Learning Controller Using an ANN.- 7.4 Reinforcement Learning Controller Using an ANN.- 7.4.1 Reinforcement Learning.- 7.4.2 Proposed Learning Control System.- 7.4.3 Example and Comments.- 8 Conclusion.- 8.1 Summary.- 8.2 Directions for Future Research.- Appendix A: Some Basic Results on Multirate Sampling.- A.1 Introduction.- A.3 Basic Result.- Appendix B: Tutorial on Artificial Neural Networks.- B.1 An Introduction to Neural Networks.- B.1.1 Neurons.- B.1.2 Interconnection Topology.- B.1.3 Learning Laws.- B.2 Historical Background.- B.3 Properties of Neural Networks.- B.3.1 Pattern Classification and Associative Memory.- B.3.2 Self-Organization and Feature Extraction.- B.3.3 Optimization.- B.3.4 Nonlinear Mappings.- B.4 Neural Nets and Computers.- B.5 Derivation of Backpropagation.- B.6 Neural Network References.- References.

Read more less

Book Synopsis1 Introduction to Model Based Predictive Control.- 1.1 MPC Strategy.- 1.2 Historical Perspective.- 1.3 Outline of the chapters.- 2 Model Based Predictive Controllers.- 2.1 MPC Elements.- 2.2 Review of some MPC Algorithms.- 2.3 MPC Based on the Impulse Response.- 2.4 Generalized Predictive Control.- 2.5 Constrained Receding-Horizon Predictive Control.- 2.6 Stable GPC.- 2.7 Filter Polynomials for Improving Robustness.- 3 Simple Implementation of GPC for Industrial Processes.- 3.1 Plant Model.- 3.2 The Dead Time Multiple of Sampling Time Case.- 3.3 The Dead Time non Multiple of the Sampling Time Case.- 3.4 Integrating Processes.- 3.5 Consideration of Ramp Setpoints.- 4 Robustness Analysis in Precomputed GPC.- 4.1 Structured Uncertainties.- 4.2 Stability Limits with Structured Uncertainties.- 4.3 Unstructured Uncertainties.- 4.4 Relationship between the two Types of Uncertainties.- 4.5 General Comments.- 5 Multivariate GPC.- 5.1 Derivation of Multivariable GPC.- 5.2 Obtaining a Matrix FracTable of Contents1 Introduction to Model Based Predictive Control.- 1.1 MPC Strategy.- 1.2 Historical Perspective.- 1.3 Outline of the chapters.- 2 Model Based Predictive Controllers.- 2.1 MPC Elements.- 2.1.1 Prediction Model.- 2.1.2 Objective Function.- 2.1.3 Obtaining the Control Law.- 2.2 Review of some MPC Algorithms.- 2.3 MPC Based on the Impulse Response.- 2.3.1 Process Model and Prediction.- 2.3.2 Control Law.- 2.4 Generalized Predictive Control.- 2.4.1 Formulation of Generalized Predictive Control.- 2.4.2 The Coloured Noise Case.- 2.4.3 An example.- 2.5 Constrained Receding-Horizon Predictive Control.- 2.5.1 Computation of the Control Law.- 2.5.2 Properties.- 2.6 Stable GPC.- 2.6.1 Formulation of the control law.- 2.7 Filter Polynomials for Improving Robustness.- 2.7.1 Selection of the T polynomial.- 2.7.2 Relation with other Formulations.- 3 Simple Implementation of GPC for Industrial Processes.- 3.1 Plant Model.- 3.1.1 Plant Identification: The Reaction Curve Method.- 3.2 The Dead Time Multiple of Sampling Time Case.- 3.2.1 Discrete Plant Model.- 3.2.2 Problem Formulation.- 3.2.3 Computation of the Controller Parameters.- 3.2.4 Role of the Control-Weighting Factor.- 3.2.5 Implementation Algorithm.- 3.2.6 An Implementation Example.- 3.3 The Dead Time non Multiple of the Sampling Time Case.- 3.3.1 Discrete Model of the Plant.- 3.3.2 Controller Parameters.- 3.3.3 Example.- 3.4 Integrating Processes.- 3.4.1 Derivation of the Control Law.- 3.4.2 Controller parameters.- 3.4.3 Example.- 3.5 Consideration of Ramp Setpoints.- 3.5.1 Example.- 4 Robustness Analysis in Precomputed GPC.- 4.1 Structured Uncertainties.- 4.1.1 Parametric Uncertainties.- 4.1.2 Unmodelled Dynamics.- 4.1.3 Both Types of Uncertainties.- 4.2 Stability Limits with Structured Uncertainties.- 4.2.1 Influence of Parametric Uncertainties.- 4.2.2 Influence of Unmodelled Dynamics.- 4.2.3 Combined Effect.- 4.2.4 Influence of the Control Effort.- 4.3 Unstructured Uncertainties.- 4.3.1 Process Description.- 4.3.2 Measurement of the Robustness of the GPC.- 4.3.3 Robustness Limits.- 4.4 Relationship between the two Types of Uncertainties.- 4.4.1 Uncertainty in the Gain.- 4.4.2 Uncertainty in the Delay.- 4.5 General Comments.- 5 Multivariate GPC.- 5.1 Derivation of Multivariable GPC.- 5.1.1 White Noise Case.- 5.1.2 Coloured noise case.- 5.2 Obtaining a Matrix Fraction Description.- 5.2.1 Transfer Matrix Representation.- 5.2.2 Parametric Identification.- 5.3 State Space Formulation.- 5.3.1 Matrix Fraction and State Space Equivalences.- 5.4 Dead Time Problems.- 5.5 Example: Distillation Column.- 6 Constrained MPC.- 6.1 Constraints and GPC.- 6.1.1 Illustrative Examples.- 6.2 Revision of Main Quadratic Programming Algorithms.- 6.2.1 The Active Set Methods.- 6.2.2 Feasible Directions Methods.- 6.2.3 Initial Feasible Point.- 6.2.4 Pivoting Methods.- 6.3 Constraints Handling.- 6.3.1 Slew Rate Constraints.- 6.3.2 Amplitude Constraints.- 6.3.3 Output Constraints.- 6.3.4 Constraints Reduction.- 6.4 1-norm.- 6.5 Constrained MPC and Stability.- 7 Robust MPC.- 7.1 Process Models and Uncertainties.- 7.1.1 Truncated Impulse Response Uncertainties.- 7.1.2 Matrix Fraction Description Uncertainties.- 7.1.3 Global Uncertainties.- 7.2 Objective Functions.- 7.2.1 Quadratic Norm.- 7.2.2 ? — ? norm.- 7.2.3 1-norm.- 7.3 Illustrative Examples.- 7.3.1 Bounds on the Output.- 7.3.2 Uncertainties in the Gain.- 8 Applications.- 8.1 Solar Power Plant.- 8.1.1 Control Strategy.- 8.1.2 Plant Results.- 8.2 Composition Control in an Evaporator.- 8.2.1 Description of the Process.- 8.2.2 Obtaining the Linear Model.- 8.2.3 Controller Design.- 8.2.4 Results.- 8.3 Pilot Plant.- 8.3.1 Plant Description.- 8.3.2 Plant Control.- 8.3.3 Flow Control.- 8.3.4 Temperature Control at the Exchanger Output.- 8.3.5 Temperature Control in the Tank.- 8.3.6 Level Control.- 8.3.7 Remarks.- A Revision of the Simplex method.- A.1 Equality Constraints.- A.2 Finding an Initial Solution.- A.3 Inequality Constraints.- B Model Predictive Control Simulation Program.- References.

Read more less

Book SynopsisRobust Control of Uncertain Dynamic SystemsTrade ReviewFrom the book reviews:“This book is a concise, clear and well-organized masterpiece on robust control theory for linear systems with parameter-uncertainty. … as a textbook for understanding the basic concepts and state-space methodologies about robust control, this book is one of the best ones I have ever read.” (Yun Zou, Mathematical Reviews, November, 2014)Table of ContentsIntroduction and Perspective.- Robust Stability Analysis of Linear State Space Systems.- Performance Robustness Analysis via Root Clustering (Robust D-Stability).- Robust Control Design for Linear Uncertain State Space Systems.- Applications to Engineering Systems.- Related and Emerging Topics.- A Appendix.

Read more less

Book SynopsisThis book includes selected contributions by lecturers at the third annual Formation d’Automatique de Paris. It provides a well-integrated synthesis of the latest thinking in nonlinear optimal control, observer design, stability analysis and structural properties of linear systems, without the need for an exhaustive literature review. The internationally known contributors to this volume represent many of the most reputable control centers in Europe.Table of ContentsIntroduction to Nonlinear Optimal Control.- Observer Design for Nonlinear Systems.- Sampled-data Control of Nonlinear Systems.- Stability Analysis of Time-delay Systems: A Lyapunov Approach.- Controllability of Partial Differential Equations.- Stability: Told by Its Developers.- Structural Properties of Linear Systems Part II: Structure at Infintiy: Appendix: On the Literature's Two Different Definitions of Uniform Global Asymptotic Stability for Nonlinear Systems.

Read more less

Book SynopsisResearch on humanoid robots has been mostly with the aim of developing robots that can replace humans in the performance of certain tasks. Motion planning for these robots can be quite difficult, due to their complex kinematics, dynamics and environment. It is consequently one of the key research topics in humanoid robotics research and the last few years have witnessed considerable progress in the field. Motion Planning for Humanoid Robots surveys the remarkable recent advancement in both the theoretical and the practical aspects of humanoid motion planning. Various motion planning frameworks are presented in Motion Planning for Humanoid Robots, including one for skill coordination and learning, and one for manipulating and grasping tasks. The problem of planning sequences of contacts that support acyclic motion in a highly constrained environment is addressed and a motion planner that enables a humanoid robot to push an object to a desired location on a cluttered table is described. The main areas of interest include: • whole body motion planning, • task planning, • biped gait planning, and • sensor feedback for motion planning. Torque-level control of multi-contact behavior, autonomous manipulation of moving obstacles, and movement control and planning architecture are also covered. Motion Planning for Humanoid Robots will help readers to understand the current research on humanoid motion planning. It is written for industrial engineers, advanced undergraduate and postgraduate students.Table of ContentsNavigation and Gait Planning.- Compliant Control of Whole-body Multi-contact Behaviors in Humanoid Robots.- Whole-body Motion Planning – Building Blocks for Intelligent Systems.- Planning Whole-body Humanoid Locomotion, Reaching, and Manipulation.- Efficient Motion and Grasp Planning for Humanoid Robots.- Multi-contact Acyclic Motion Planning and Experiments on HRP-2 Humanoid.- Motion Planning for a Humanoid Robot Based on a Biped Walking Pattern Generator.- Autonomous Manipulation of Movable Obstacles.- Multi-modal Motion Planning for Precision Pushing on a Humanoid Robot.- A Motion Planning Framework for Skill Coordination and Learning.

Read more less

Book SynopsisThe classic text on robot manipulators now covers visual control, motion planning and mobile robots too!Based on the successful Modelling and Control of Robot Manipulators by Sciavicco and Siciliano (Springer, 2000), Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control. It has been expanded to include coverage of mobile robots, visual control and motion planning. A variety of problems is raised throughout, and the proper tools to find engineering-oriented solutions are introduced and explained.The text includes coverage of fundamental topics like kinematics, and trajectory planning and related technological aspects including actuators and sensors.To impart practical skill, examples and case studies are carefully worked out and interwoven through the text, with frequent resort to simulation. In addition, end-of-chapter exercises are proposed, and the book is accompanied by an electronic solutions manual containing the MATLAB® code for computer problems; this is available free of charge to those adopting this volume as a textbook for courses.Trade ReviewRobotics: Modelling, Planning and Control is a book that comprehensively covers all aspects of robotic fundamentals. It is particularly an excellent text for graduate educators, as it covers the fundamentals of the field with a rigorous formalism that is well blended with the technological aspects of robotics. The text covers in detail the theory of manipulators and wheeled robots starting with kinematics, dynamics and motion control, as well interaction with the environment through perception - force and vision sensors. The book is written by technical authorities in the field, and will be in invaluable addition to graduate education as well as a useful guide for industrial practitioners. Alexander Zelinsky, CSIRO, Australia Robotics is a diverse field bringing together disparate areas from computer science, electrical engineering and mechanical engineering. This book is an integrative but rigorous treatment of all the relevant concepts, with an eye toward modern, practical applications making it an excellent choice for a first year graduate course in robotics. Vijay Kumar, University of Pennsylvania This book provides rock-solid foundations for the study of classical mechanics and control of robots, with the authoritative character of a reference where you can surely find the correct expression and the rigorous derivation of the results you need. On top of this, new chapters on motion planning, visual servoing, and mobile robot control provide support to teaching wider and more interdisciplinary aspects of robotics, and open up vistas that will certainly inspire a new generation of scholars to embrace this incredibly rich and fertile research field. Antonio Bicchi, University of Pisa, Italy This book offers a well-balanced and intellectually satisfying treatment of robot mechanics, planning, and control – from the choice and sequence of topics, to the level of detail in the analysis, and the clear connections made between the latest technologies and the theoretical foundations of robotics, this book is an essential element in the library of every aspiring young robotics researcher. Frank Chongwoo Park, Seoul National University Robotics: Modeling, Planning and Control is a historiography from the materialistic view of robotics. Authors clearly explain physical and mathematical foundation to understand the most up-to-date robotics, so faithfully to bibliography and terminology in robotics. Unquestionably, the best textbook for senior students and graduate students and the closest reference book for engineers and scientists! Yoshihiko Nakamura, University of Tokyo Exceptional! A text with such a span of robotics fundamentals and advanced research in both manipulation and mobility, and a treatment that creatively balances mathematical depth and physical intuition – a fresh and certainly unique reference for researchers and engineers in the field of robotics. Oussama Khatib, Stanford University Certainly because of its youth, robotics is not always considered as a discipline as such. It is often introduced as a technological "area" integrating various aspects of mechanics, automatic control and computer science. Such a dispersed view is prejudicial for students. The book by Siciliano et al. achieves the introduction of the basic concepts in a coherent, self-contained and didactic way. In that sense, when reading Robotics: Modelling, Planning and Control the reader – from the undergraduate student to the researcher – understands that a new discipline is born, with its own foundations. Jean-Paul Laumond, LAAS-CNRSTable of ContentsKinematics.- Differential Kinematics and Statics.- Trajectory Planning.- Actuators and Sensors.- Control Architecture.- Dynamics.- Motion Control.- Force Control.- Visual Servoing.- Mobile Robots.- Motion Planning.

Read more less

Book SynopsisRecent developments in constrained control and estimation have created a need for this comprehensive introduction to the underlying fundamental principles. These advances have significantly broadened the realm of application of constrained control. - Using the principal tools of prediction and optimisation, examples of how to deal with constraints are given, placing emphasis on model predictive control. - New results combine a number of methods in a unique way, enabling you to build on your background in estimation theory, linear control, stability theory and state-space methods. - Companion web site, continually updated by the authors. Easy to read and at the same time containing a high level of technical detail, this self-contained, new approach to methods for constrained control in design will give you a full understanding of the subject.Trade ReviewFrom the reviews: This book provides a seminal foundation for unifying constrained control and constrained estimation within the framework of nonlinear programming… A lot of thought and years of work have gone into developing this complete picture of an extremely complex field… Important geometric intuition is given on every front. The presentation style is lucid and makes for fascinating reading and study. In this day and age, the mathermatical demonstrations and proofs are welcome, while the case studies show the practical relevance of the approach as laid out in this very welcome book. IEEE Transactions on Automatic Control 51 (2006) 176 – 177 (Reviewer: Frank L. Lewis) This is an excellent book dealing with the important topic of constrained control and estimation. Constraints are present in all real systems and have been somewhat neglected by most of the existing control techniques with only a few exceptions such as MPC. Although the book cannot be classified as an MPC book, a significant part of the text is dedicated to receding horizon control and this work comlements and is a valuable addition to the list of existing MPC books. The book is self-contained, with an extensive list of references, well-structured, and covers a wide range of topics required for constrained control and estimation. It is presented with a sufficient level of detail and mathematical rigor to be used as a textbook for research students or as the basis of a self-study programme for practitioners in constrained control and estimation. International Journal of Robust and Nonlinear Control 17 (2007) 347 – 354 (Reviewer: Eduardo F. Camacho) This book is a study on … problems for linear finite-dimensional discrete-time systems.… References and an index are included at the end.… this book is worth acquiring for those involved in control system design, and it produces a valuable contribution to the area of constrained control systems. Zentralblatt MATH 1078 (2006) (Reviewer: A. Akutowicz)Table of ContentsFoundations.- Overview of Optimisation Theory.- Fixed Horizon Optimal Control with Constraints.- Receding Horizon Optimal Control with Constraints.- Constrained Linear Quadratic Optimal Control.- Global Characterisation of Constrained Linear Quadratic Optimal Control.- Regional Characterisation of Constrained Linear Quadratic Optimal Control.- Computational Issues in Constrained Optimal Control.- Constrained Estimation.- Duality Between Constrained Estimation and Control.- Further Developments.- The Hessian in the QP Problem: Singular Value Structure and Related Algorithms.- Output Feedback Optimal Control with Constraints.- Finite Alphabet Controllers and Estimators.- Case Studies.- Rudder Roll Stabilisation of Ships.- Cross-Directional Control.- Control over Communication Networks.

Read more less

Book SynopsisMobile Robotics: A Practical Introduction (2nd edition) is an excellent introduction to the foundations and methods used for designing completely autonomous mobile robots. A fascinating, cutting-edge, research topic, autonomous mobile robotics is now taught in more and more universities. In this book you are introduced to the fundamental concepts of this complex field via twelve detailed case studies that show how to build and program real working robots. Topics covered in clued learning, autonomous navigation in unmodified, noisy and unpredictable environments, and high fidelity robot simulation. This new edition has been updated to include a new chapter on novelty detection, and provides a very practical introduction to mobile robotics for a general scientific audience. It is essential reading for 2nd and 3rd year undergraduate students and postgraduate students studying robotics, artificial intelligence, cognitive science and robot engineering. The update and overview of core concepts in mobile robotics will assist and encourage practitioners of the field and set challenges to explore new avenues of research in this exiting field. The author is Senior Lecturer at the Department of Computer Science at the University of Essex. "A very fine overview over the relevant problems to be solved in the attempt to bring intelligence to a moving vehicle." Professor Dr. Ewald von Puttkamer, University of Kaiserslautern "Case studies show ways of achieving an impressive repertoire of kinds of learned behaviour, navigation and map-building. The book is an admirable introduction to this modern approach to mobile robotics and certainly gives a great deal of food for thought. This is an important and though-provoking book." Alex M. Andrew in Kybernetes Vol 29 No 4 and Robotica Vol 18Trade ReviewFrom the reviews of the second edition: "The book is an admirable introduction to its modern approach to mobile robotics and certainly gives a great deal of food for thought. … Like its first edition, this is an important and thought-provoking book." (Alex M. Andrew, Robotica, Vol. 22 (347), 2004) "Mobile Robotics: A Practical Introduction (2nd edition) is an excellent introduction to the foundations and methods used for designing completely autonomous mobile robots. … provides a very practical introduction to mobile robotics for a general scientific audience. It is essential reading for 2nd and 3rd year undergraduate students and postgraduate students studying robotics, artificial intelligence, cognitive science and robot engineering. … is an admirable introduction to this modern approach to mobile robotics and certainly gives a great deal of food for thought." (Alex M. Andrew, Artificial Life & Robotics, Vol. 7 (4), 2004) Table of ContentsIntroduction Foundations Robot Hardware Robot Learning: Making Sense of Raw Sensor Data Navigation Novelty Detection Simulation: Modelling Robot-Environment Interaction Analysis of Robot Behaviour Outlook

Read more less

Book Synopsis"Advanced Topics in Control Systems Theory" contains selected contributions written by lecturers at the second (annual) Formation d’Automatique de Paris (FAP) (Graduate Control School in Paris). It is addressed to graduate students and researchers in control theory with topics touching on a variety of areas of interest to the control community such as cascaded systems, flatness, optimal control, and Hamiltonian and infinite-dimensional systems. The reader is provided with a well-integrated synthesis of the latest thinking in these subjects without the need for an exhaustive literature review. "Advanced Topics in Control Systems Theory" can be used to support either a one-term general advanced course on nonlinear control theory, devoting a few lectures to each chapter, or for more focused and intensive courses at graduate level. The book’s concise but pedagogical manner will give an ideal start to researchers wishing to broaden their knowledge in aspects of modern control theory outside their own expertise.Table of ContentsNonlinear Adaptive Stabilization via System Immersion: Control Design and ApplicationsCascaded Nonlinear Time-varying Systems: Analysis and DesignControl of Mechanical Systems from Aerospace EngineeringCompositional Modelling of Distributed-parameter SystemsAlgebraic Analysis of Control Systems Defined by Partial Differential EquationsStructural Properties of Discrete and Continuous Linear Time-varying Systems: A Unified Approach.

Read more less

Book SynopsisThis book contains all refereed papers accepted during the ninth edition of the conference that took place at the Cité Internationale Universitaire de Paris on December 18-19, 2018.Mastering complex systems requires an integrated understanding of industrial practices as well as sophisticated theoretical techniques and tools. This explains the creation of an annual go-between forum in Paris dedicated to academic researchers & industrial actors working on complex industrial systems architecture, modeling & engineering.These proceedings cover the most recent trends in the emerging field of Complex Systems, both from an academic and a professional perspective. A special focus is put on “Products & services development in a digital world”. The CSD&M Paris 2018 conference is organized under the guidance of CESAM Community (http://cesam.community/en). CESAM Community has been developed since 2010 by the non-profit organization CESAMES Association to organize the sharing of good practices in Enterprise and Systems Architecture and to certify the level of knowledge and proficiency in this field through CESAM certification.

Read more less

Book SynopsisThe International Workshop on Human-Friendly Robotics (HFR) is an annual meeting that brings together academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects related to the introduction of robots into everyday life. HFR collects contributions on current developments of a new generation of human-friendly robots, i.e., safe and dependable machines, operating in the close vicinity to humans or directly interacting with them in a wide range of domains. The papers contained in the book describe the newest and most original achievements in the field of human-robot-interaction coming from the work and ideas of young researchers. The contributions cover a wide range of topics related to human-robot interaction, both physical and cognitive, including theories, methodologies, technologies, empirical and experimental studies.Table of ContentsRobot Control and Manipulation.- Natural Human-Robot Interaction.- Human robot collaboration.- Robot Learning.

Read more less

Book SynopsisRecent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the control of single-input, single-output systems. This volume provides a definitive survey of the latest model-predictive control methods available to engineers and scientists today. The initial set of chapters present various methods for managing uncertainty in systems, including stochastic model-predictive control. With the advent of affordable and fast computation, control engineers now need to think about using “computationally intensive controls,” so the second part of this book addresses the solution of optimization problems in “real” time for model-predictive control. The theory and applications of control theory often influence each other, so the last section of Handbook of Model Predictive Control rounds out the book with representative applications to automobiles, healthcare, robotics, and finance. The chapters in this volume will be useful to working engineers, scientists, and mathematicians, as well as students and faculty interested in the progression of control theory. Future developments in MPC will no doubt build from concepts demonstrated in this book and anyone with an interest in MPC will find fruitful information and suggestions for additional reading.Trade Review“This handbook is designed for a wide audience. It is an excellent reference for graduate students, researchers, and practitioners in the field of control systems and numerical optimization who want to understand the potential, challenges, and benefits of MPC and its applications. … This handbook enables the reader to gain a panoramic viewpoint of MPC theory and practice as well as provides a state-of-the-art overview of new and exciting areas of application at the forefront of MPC research.” (Gabriele Pannocchia, IEEE Control Systems Magazine, Vol. 40 (5), October, 2020)Table of ContentsTheory.- The Essentials of Model Predictive Control (MPC).- Dynamic Programming and MPC.- Set-valued and Lyapunov Methods for MPC.- Stochastic Model Predictive Control.- Moving Horizon Estimation.- Probing and Duality in Stochastic Model Predictive Control.- Economic Model Predictive Control: Some Design Tools and Analysis Techniques.- Nonlinear Predictive Control for Trajectory Tracking and Path Following: An Introduction and Perspective.- Hybrid Model Predictive Control.- Model Predictive Control of Polynomial Systems.- Distributed MPC for Large-Scale Systems.- Scalable MPC Design.- Computations.- Efficient Convex Optimization for Linear MPC.- Implicit Non-Convex Model Predictive Control.- Convexification and Real-Time Optimization for MPC with Aerospace Applications.- Explicit (Offline) Optimization for MPC.- Real-Time Implementation of Explicit Model Predictive Control.- Robust Optimization for MPC.- Scenario Optimization for MPC.- Nonlinear Programming Formulations for Nonlinear and Economic Model Predictive Control.- Applications.- Automotive Applications of Model Predictive Control.- Applications of MPC in the Area of Health Care.- Model Predictive Control for Power Electronics Applications.- Learning-based Fast Nonlinear Model Predictive Control for Custom-made 3D Printed Ground and Aerial Robots.- Applications of MPC to Building HVAC Systems.- Toward Multi-Layered MPC for Complex Electric Energy Systems.- Applications of MPC to Finance.

Read more less

Book SynopsisThis book reports on findings at the intersection between two related fields, namely coastal hydrography and marine robotics. On one side, it shows how the exploration of the ocean can be performed by autonomous underwater vehicles; on the other side, it shows how some methods from hydrography can be implemented in the localization and navigation of such vehicles, e.g. for target identification or path finding. Partially based on contributions presented at the conference Quantitative Monitoring of Underwater Environment, MOQESM, held on October 11-12, 2016, Brest, France, this book includes carefully revised and extended chapters presented at the conference, together with original papers not related to the event. All in all, it provides readers with a snapshot of current methods for sonar track registration, multi-vehicles control, collective exploration of underwater environments, optimization of propulsion systems, among others. More than that, the book is aimed as source of inspiration and tool to promote further discussions and collaboration between hydrographers, robotic specialists and other related communities.Trade Review“I would definitely recommend it for any underwater vehicle research and development individuals or groups.” (Ron Lewis, Underwater Technology, Vol. 37 (3), 2020)Table of ContentsFrom the Content: Fast Fourier-Based Block-Matching Algorithm for Sonar Tracks Registration in a Multiresolution Framework.- Adaptive Sampling with a Fleet of Autonomous Sailing Boats Using Artificial Potential Fields.- Underwater Robots Equipped with Artificial Electric Sense for the Exploration of Unconventional Aquatic Niches.- Estimating the Trajectory of Low-cost Autonomous Robots Using Interval Analysis: Application to the euRathlon Competition.

Read more less

Book SynopsisThis book is devoted to embedded systems (ESs), which can now be found in practically all fields of human activity. Embedded systems are essentially a special class of computing systems designed for monitoring and controlling objects of the physical world. The book begins by discussing the distinctive features of ESs, above all their cybernetic-physical character, and how they can be designed to deliver the required performance with a minimum amount of hardware. In turn, it presents a range of design methodologies. Considerable attention is paid to the hardware implementation of computational algorithms. It is shown that different parts of complex ESs could be implemented using models of finite state machines (FSMs). Also, field-programmable gate arrays (FPGAs) are very often used to implement different hardware accelerators in ESs. The book pays considerable attention to design methods for FPGA-based FSMs, before the closing section turns to programmable logic controllers widely used in industry. This book will be interesting and useful for students and postgraduates in the area of Computer Science, as well as for designers of embedded systems. In addition, it offers a good point of departure for creating embedded systems for various spheres of human activity. Table of ContentsIntroduction into embedded systems.- Design of embedded systems.- Implementation of computational algorithms in embedded systems.- Field programmable gate arrays.- Implementing Control Algorithms with FPGAs.- Programmable Logic Controllers.- Conclusion.

Read more less

Book SynopsisThis graduate-level textbook elucidates low-risk and fail-safe systems in mathematical detail. It addresses, in particular, problems where mission-critical performance is paramount, such as in aircraft, missiles, nuclear reactors and weapons, submarines, and many other types of systems where “failure” can result in overwhelming loss of life and property. The book is divided into four parts: Fundamentals, Electronics, Software, and Dangerous Goods. The first part on Fundamentals addresses general concepts of system safety engineering that are applicable to any type of system. The second part, Electronics, addresses the detection and correction of electronic hazards. In particular, the Bent Pin Problem, Sneak Circuit Problem, and related electrical problems are discussed with mathematical precision. The third part on Software addresses predicting software failure rates as well as detecting and correcting deep software logical flaws (called defects). The fourth part on Dangerous Goods presents solutions to three typical industrial chemical problems faced by the system safety engineer during the design, storage, and disposal phases of a dangerous goods’ life cycle. Table of ContentsIntroduction.- Decomposition of the Failure Histogram.- Bounding the “Black Swan” Probability.- The Risk Surface.- The Bent Pin Problem-I: Computer Search Methods.- The Bent Pin Problem-II: Matrix Methods.- The Bent Pin Problem-III: Number Theory Methods.- The Bent Pin Problem-IV: Limit Methods.- The Bent Pin Problem-V: Experimental Methods .- “Sneak Circuits” and Related System Safety Electrical Problems-I: Matrix Methods .- “Sneak Circuits” and Related System Safety Electrical Problems-II: Computer Search.- Methods.- Predicting Software Performance.- Alternative Flowcharts for a Mathematical Analysis of Logic.- Fail-Safe Control Software.- Design Phase Elimination of Beryllium.- Accelerated Age Testing of Explosives and Propellants.- The Movement of Inorganic Cadmium Through the Environment.- Epilogue .- GLOSSARY.- APPENDIX A “Long Tailed” Distribution.- APPENDIX B.- APPENDIX C.- APPENDIX D.- APPENDIX E.- APPENDIX F.- APPENDIX G.- APPENDIX H.- INDEX.

Read more less

Book SynopsisThis book provides a broad overview of state-of-the-art research at the intersection of the Koopman operator theory and control theory. It also reviews novel theoretical results obtained and efficient numerical methods developed within the framework of Koopman operator theory.The contributions discuss the latest findings and techniques in several areas of control theory, including model predictive control, optimal control, observer design, systems identification and structural analysis of controlled systems, addressing both theoretical and numerical aspects and presenting open research directions, as well as detailed numerical schemes and data-driven methods. Each contribution addresses a specific problem. After a brief introduction of the Koopman operator framework, including basic notions and definitions, the book explores numerical methods, such as the dynamic mode decomposition (DMD) algorithm and Arnoldi-based methods, which are used to represent the operator in a finite-dimensional basis and to compute its spectral properties from data. The main body of the book is divided into three parts: theoretical results and numerical techniques for observer design, synthesis analysis, stability analysis, parameter estimation, and identification; data-driven techniques based on DMD, which extract the spectral properties of the Koopman operator from data for the structural analysis of controlled systems; and Koopman operator techniques with specific applications in systems and control, which range from heat transfer analysis to robot control. A useful reference resource on the Koopman operator theory for control theorists and practitioners, the book is also of interest to graduate students, researchers, and engineers looking for an introduction to a novel and comprehensive approach to systems and control, from pure theory to data-driven methods.Table of ContentsPart I: Control Design, Observation, and Identification.- Linear Observer Synthesis for Nonlinear Systems.- Linear Predictors for Nonlinear Dynamical Systems.- Global Stability Analysis.- Pulse-based Optimal Control.- Parameter Estimation and Identification of Nonlinear Systems.- Koopman Spectrum and Stability of Cascaded Dynamical Systems.- Open and Closed Loop Control of PDEs via Switched Systems and Koopman operator based reduced order models.- Part II: Data-Driven Analysis.- Data-driven Approximations of Dynamical Systems Operators for Control.- Operator Theoretic-based Data-driven Approach for Optimal Stabilization of Nonlinear System.- Manifold Learning for Data-Driven Dynamical Systems Analysis.- Use of Data-Driven Koopman Spectrum Computation and Delay Embedding.- Part III: Applications.- Modeling of Advective Heat Transfer in a Practical Building Atrium via Koopman Mode Decomposition.- Phase-amplitude Reduction of Limit-cycling Systems.- Exploiting Effects of Network Topology on Performance in Nonlinear Consensus Networks.- Koopman Operators in Embedded Control.

Read more less

Book SynopsisThis brief describes the basics of Riemannian optimization—optimization on Riemannian manifolds—introduces algorithms for Riemannian optimization problems, discusses the theoretical properties of these algorithms, and suggests possible applications of Riemannian optimization to problems in other fields.To provide the reader with a smooth introduction to Riemannian optimization, brief reviews of mathematical optimization in Euclidean spaces and Riemannian geometry are included. Riemannian optimization is then introduced by merging these concepts. In particular, the Euclidean and Riemannian conjugate gradient methods are discussed in detail. A brief review of recent developments in Riemannian optimization is also provided. Riemannian optimization methods are applicable to many problems in various fields. This brief discusses some important applications including the eigenvalue and singular value decompositions in numerical linear algebra, optimal model reduction in control engineering, and canonical correlation analysis in statistics.Trade Review“The author successfully presents all of this varied material using a consistent and modern notation. … The book meticulously provides references with a comprehensive list at the end. It includes information about software libraries that implement Riemannian optimization in MATLAB, Python, R, C++, and Julia. Both the proofs and calculations in the examples are given with sufficient detail using a consistent notation.” (Anders Linnér, Mathematical Reviews, October, 2022)“The book is a very nice introductory reference for students, engineers, and practitioners to get started in the field of Riemannian optimization. … A highlight of the book is that it reviews the most important work in the field and also mentions current research topics. Thus, I also highly recommended it to researchers getting a broad overview of what is currently studied in the field, without being too detailed or theoretical.” (Lena Sembach, SIAM Review, Vol. 64 (2), June, 2022)Table of ContentsIntroduction.- Preliminaries and Overview of Euclidean Optimization.- Unconstrained Optimization on Riemannian Manifolds.- Conjugate Gradient Methods on Riemannian Manifolds.- Applications of Riemannian Optimization.- Recent Developments in Riemannian Optimization.

Read more less

Book SynopsisThis brief presents a suite of computationally efficient methods for bounding trajectories of dynamical systems with multi-dimensional intervals, or ‘boxes’. It explains the importance of bounding trajectories for evaluating the robustness of systems in the face of parametric uncertainty, and for verification or control synthesis problems with respect to safety and reachability properties. The methods presented make use of: interval analysis; monotonicity theory; contraction theory; and data-driven techniques that sample trajectories. The methods are implemented in an accompanying open-source Toolbox for Interval Reachability Analysis. This brief provides a tutorial description of each method, focusing on the requirements and trade-offs relevant to the user, requiring only basic background on dynamical systems. The second part of the brief describes applications of interval reachability analysis. This makes the brief of interest to a wide range of academic researchers, graduate students, and practising engineers in the field of control and verification. Trade Review“The motivation of this book is to provide to the readers tutorial presentations of several approaches for interval reachability analysis, without requiring any previous knowledge and experience of reachability analysis. Two parts, Part I and Part II, are used for this purpose. Part I describes six main methods for interval reachability analysis and in Part II several applications are presented.” (Takashi Amemiya, Mathematical Reviews, October, 2022)Table of ContentsChapter 1. Introduction.- Part 1: Reachability Methods.- Chapter 2. Interval Analysis.- Chapter 3. Monotonicity.- Chapter 4. Mixed-Monotonicity.- Chapter 5. Sampled-Data Mixed-Monotonicity.- Chapter 6. Growth Bounds.- Chapter 7. Sampling-Based Methods.- Part 2: Applications.- Chapter 8. Safety and Reachability Verification.- Chapter 9. Interval Volume as a Robustness Measure.- Chapter 10. Abstraction-Based Control Synthesis.

Read more less

Book SynopsisThis book provides an overview of the nonlinear model predictive control (NMPC) concept for application to innovative combustion engines. Readers can use this book to become more expert in advanced combustion engine control and to develop and implement their own NMPC algorithms to solve challenging control tasks in the field. The significance of the advantages and relevancy for practice is demonstrated by real-world engine and vehicle application examples. The author provides an overview of fundamental engine control systems, and addresses emerging control problems, showing how they can be solved with NMPC. The implementation of NMPC involves various development steps, including: • reduced-order modeling of the process; • analysis of system dynamics; • formulation of the optimization problem; and • real-time feasible numerical solution of the optimization problem.Readers will see the entire process of these steps, from the fundamentals to several innovative applications. The application examples highlight the actual difficulties and advantages when implementing NMPC for engine control applications. Nonlinear Model Predictive Control of Combustion Engines targets engineers and researchers in academia and industry working in the field of engine control. The book is laid out in a structured and easy-to-read manner, supported by code examples in MATLAB®/Simulink®, thus expanding its readership to students and academics who would like to understand the fundamental concepts of NMPC.Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.Table of ContentsPart I: Fundamentals for Engine Control Based on Nonlinear Model Predictive Control.- Introduction.- Nonlinear Model Predictive Control.- NMPC-Based Engine Control.- Part II: Airpath Control.- Introduction to Airpath Control.- Reduced-Order Modeling.- In-Depth Case Study I: Two-Stage Turbocharging for SI Engines.- In-Depth Case Study II: Dual-Loop EGR for Compression Ignition Engines.- Part III: Combustion Control for Premixed Charge Compression Ignition Engines.- Reduced-Order Modeling.- In-Depth Case Study III: Premixed Compression Ignition Engines with Diesel-like Fuels.- In-Depth Case Study IV: Premixed Compression Ignition Engines with Gasoline-like Fuels.

Read more less

Book SynopsisOver the past century, mechanization has been an important means for optimizing resource utilization, improving worker health and safety and reducing labor requirements in farming while increasing productivity and quality of 4F (Food, Fuel, Fiber, Feed). Recognizing this contribution, agricultural mechanization was considered as one of the top ten engineering achievements of 20th century by the National Academy of Engineering. Accordingly farming communities have adopted increasing level of automation and robotics to further improve the precision management of crops (including input resources), increase productivity and reduce farm labor beyond what has been possible with conventional mechanization technologies. It is more important than ever to continue to develop and adopt novel automation and robotic solutions into farming so that some of the most complex agricultural tasks, which require huge amount of seasonal labor such as fruit and vegetable harvesting, could be automated while meeting the rapidly increasing need for 4F. In addition, continual innovation in and adoption of agricultural automation and robotic technologies is essential to minimize the use of depleting resources including water, minerals and other chemicals so that sufficient amount of safe and healthy food can be produced for current generation while not compromising the potential for the future generation. This book aims at presenting the fundamental principles of various aspects of automation and robotics as they relate to production agriculture (the branch of agriculture dealing with farming operations from field preparation to seeding, to harvesting and field logistics). The building blocks of agricultural automation and robotics that are discussed in the book include sensing and machine vision, control, guidance, manipulation and end-effector technologies. The fundamentals and operating principles of these technologies are explained with examples from cutting-edge research and development currently going on around the word. This book brings together scientists, engineers, students and professionals working in these and related technologies to present their latest examples of agricultural automation and robotics research, innovation and development while explaining the fundamentals of the technology. The book, therefore, benefits those who wish to develop novel agricultural engineering solutions and/or to adopt them in the future. Table of Contents

Read more less