{"product_id":"adaptive-approximation-based-control-unifying-neural-fuzzy-and-traditional-adaptive-approximation-approaches-9780471727880","title":"Adaptive Approximation Based Control  Unifying Neural Fuzzy and Traditional Adaptive Approximation Approaches","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA highly accessible and unified approach to the design and analysis of intelligent control systems    Adaptive Approximation Based Control is a tool every control designer should have in his or her control toolbox.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003cp\u003e\u003cb\u003e1. INTRODUCTION.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Systems and Control Terminology.\u003c\/p\u003e \u003cp\u003e1.2 Nonlinear Systems.\u003c\/p\u003e \u003cp\u003e1.3 Feedback Control Approaches.\u003c\/p\u003e \u003cp\u003e1.3.1 Linear Design.\u003c\/p\u003e \u003cp\u003e1.3.2 Adaptive Linear Design.\u003c\/p\u003e \u003cp\u003e1.3.3 Nonlinear Design.\u003c\/p\u003e \u003cp\u003e1.3.4 Adaptive Approximation Based Design.\u003c\/p\u003e \u003cp\u003e1.3.5 Example Summary.\u003c\/p\u003e \u003cp\u003e1.4 Components of Approximation Based Control.\u003c\/p\u003e \u003cp\u003e1.4.1 Control Architecture.\u003c\/p\u003e \u003cp\u003e1.4.2 Function Approximator.\u003c\/p\u003e \u003cp\u003e1.4.3 Stable Training Algorithm.\u003c\/p\u003e \u003cp\u003e1.5 Discussion and Philosophical Comments.\u003c\/p\u003e \u003cp\u003e1.6 Exercises and Design Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. APPROXIMATION THEORY.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Motivating Example.\u003c\/p\u003e \u003cp\u003e2.2 Interpolation.\u003c\/p\u003e \u003cp\u003e2.3 Function Approximation.\u003c\/p\u003e \u003cp\u003e2.3.1 Off-line (Batch) Function Approximation.\u003c\/p\u003e \u003cp\u003e2.3.2 Adaptive Function Approximation.\u003c\/p\u003e \u003cp\u003e2.4 Approximator Properties.\u003c\/p\u003e \u003cp\u003e2.4.1 Parameter (Non)Linearity.\u003c\/p\u003e \u003cp\u003e2.4.2 Classical Approximation Results.\u003c\/p\u003e \u003cp\u003e2.4.3 Network Approximators.\u003c\/p\u003e \u003cp\u003e2.4.4 Nodal Processors.\u003c\/p\u003e \u003cp\u003e2.4.5 Universal Approximator.\u003c\/p\u003e \u003cp\u003e2.4.6 Best Approximator Property.\u003c\/p\u003e \u003cp\u003e2.4.7 Generalization.\u003c\/p\u003e \u003cp\u003e2.4.8 Extent of Influence Function Support.\u003c\/p\u003e \u003cp\u003e2.4.9 Approximator Transparency.\u003c\/p\u003e \u003cp\u003e2.4.10 Haar Conditions.\u003c\/p\u003e \u003cp\u003e2.4.11 Multivariable Approximation by Tensor Products.\u003c\/p\u003e \u003cp\u003e2.5 Summary.\u003c\/p\u003e \u003cp\u003e2.6 Exercises and Design Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. APPROXIMATION STRUCTURES.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Model Types.\u003c\/p\u003e \u003cp\u003e3.1.1 Physically Based Models.\u003c\/p\u003e \u003cp\u003e3.1.2 Structure (Model) Free Approximation.\u003c\/p\u003e \u003cp\u003e3.1.3 Function Approximation Structures.\u003c\/p\u003e \u003cp\u003e3.2 Polynomials.\u003c\/p\u003e \u003cp\u003e3.2.1 Description.\u003c\/p\u003e \u003cp\u003e3.2.2 Properties.\u003c\/p\u003e \u003cp\u003e3.3 Splines.\u003c\/p\u003e \u003cp\u003e3.3.1 Description.\u003c\/p\u003e \u003cp\u003e3.3.2 Properties.\u003c\/p\u003e \u003cp\u003e3.4 Radial Basis Functions.\u003c\/p\u003e \u003cp\u003e3.4.1 Description.\u003c\/p\u003e \u003cp\u003e3.4.2 Properties.\u003c\/p\u003e \u003cp\u003e3.5 Cerebellar Model Articulation Controller.\u003c\/p\u003e \u003cp\u003e3.5.1 Description.\u003c\/p\u003e \u003cp\u003e3.5.2 Properties.\u003c\/p\u003e \u003cp\u003e3.6 Multilayer Perceptron.\u003c\/p\u003e \u003cp\u003e3.6.1 Description.\u003c\/p\u003e \u003cp\u003e3.6.2 Properties.\u003c\/p\u003e \u003cp\u003e3.7 Fuzzy Approximation.\u003c\/p\u003e \u003cp\u003e3.7.1 Description.\u003c\/p\u003e \u003cp\u003e3.7.2 Takagi-Sugeno Fuzzy Systems.\u003c\/p\u003e \u003cp\u003e3.7.3 Properties.\u003c\/p\u003e \u003cp\u003e3.8 Wavelets.\u003c\/p\u003e \u003cp\u003e3.8.1 Multiresolution Analysis (MRA).\u003c\/p\u003e \u003cp\u003e3.8.2 MRA Properties.\u003c\/p\u003e \u003cp\u003e3.9 Further Reading.\u003c\/p\u003e \u003cp\u003e3.10 Exercises and Design Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. PARAMETER ESTIMATION METHODS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Formulation for Adaptive Approximation.\u003c\/p\u003e \u003cp\u003e4.1.1 Illustrative Example.\u003c\/p\u003e \u003cp\u003e4.1.2 Motivating Simulation Examples.\u003c\/p\u003e \u003cp\u003e4.1.3 Problem Statement.\u003c\/p\u003e \u003cp\u003e4.1.4 Discussion of Issues in Parametric Estimation.\u003c\/p\u003e \u003cp\u003e4.2 Derivation of Parametric Models.\u003c\/p\u003e \u003cp\u003e4.2.1 Problem Formulation for Full-State Measurement.\u003c\/p\u003e \u003cp\u003e4.2.2 Filtering Techniques.\u003c\/p\u003e \u003cp\u003e4.2.3 SPR Filtering.\u003c\/p\u003e \u003cp\u003e4.2.4 Linearly Parameterized Approximators.\u003c\/p\u003e \u003cp\u003e4.2.5 Parametric Models in State Space Form.\u003c\/p\u003e \u003cp\u003e4.2.6 Parametric Models of Discrete-Time Systems.\u003c\/p\u003e \u003cp\u003e4.2.7 Parametric Models of Input-Output Systems.\u003c\/p\u003e \u003cp\u003e4.3 Design of On-Line Learning Schemes.\u003c\/p\u003e \u003cp\u003e4.3.1 Error Filtering On-Line Learning (EFOL) Scheme.\u003c\/p\u003e \u003cp\u003e4.3.2 Regressor Filtering On-Line Learning (RFOL) Scheme.\u003c\/p\u003e \u003cp\u003e4.4 Continuous-Time Parameter Estimation.\u003c\/p\u003e \u003cp\u003e4.4.1 Lyapunov Based Algorithms.\u003c\/p\u003e \u003cp\u003e4.4.2 Optimization Methods.\u003c\/p\u003e \u003cp\u003e4.4.3 Summary.\u003c\/p\u003e \u003cp\u003e4.5 On-Line Learning: Analysis.\u003c\/p\u003e \u003cp\u003e4.5.1 Analysis of LIP EFOL scheme with Lyapunov Synthesis Method.\u003c\/p\u003e \u003cp\u003e4.5.2 Analysis of LIP RFOL scheme with the Gradient Algorithm.\u003c\/p\u003e \u003cp\u003e4.5.3 Analysis of LIP RFOL scheme with RLS Algorithm.\u003c\/p\u003e \u003cp\u003e4.5.4 Persistency of Excitation and Parameter Convergence.\u003c\/p\u003e \u003cp\u003e4.6 Robust Learning Algorithms.\u003c\/p\u003e \u003cp\u003e4.6.1 Projection modification.\u003c\/p\u003e \u003cp\u003e4.6.2 σ-modification.\u003c\/p\u003e \u003cp\u003e4.6.3 \u0026amp;epsis;-modification.\u003c\/p\u003e \u003cp\u003e4.6.4 Dead-zone modification.\u003c\/p\u003e \u003cp\u003e4.6.5 Discussion and Comparison.\u003c\/p\u003e \u003cp\u003e4.7 Concluding Summary.\u003c\/p\u003e \u003cp\u003e4.8 Exercises and Design Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. NONLINEAR CONTROL ARCHITECTURES.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Small-Signal Linearization.\u003c\/p\u003e \u003cp\u003e5.1.1 Linearizing Around an Equilibrium Point.\u003c\/p\u003e \u003cp\u003e5.1.2 Linearizing Around a Trajectory.\u003c\/p\u003e \u003cp\u003e5.1.3 Gain Scheduling.\u003c\/p\u003e \u003cp\u003e5.2 Feedback Linearization.\u003c\/p\u003e \u003cp\u003e5.2.1 Scalar Input-State Linearization.\u003c\/p\u003e \u003cp\u003e5.2.2 Higher-Order Input-State Linearization.\u003c\/p\u003e \u003cp\u003e5.2.3 Coordinate Transformations and Diffeomorphisms.\u003c\/p\u003e \u003cp\u003e5.2.4 Input-Output Feedback Linearization.\u003c\/p\u003e \u003cp\u003e5.3 Backstepping.\u003c\/p\u003e \u003cp\u003e5.3.1 Second order system.\u003c\/p\u003e \u003cp\u003e5.3.2 Higher Order Systems.\u003c\/p\u003e \u003cp\u003e5.3.3 Command Filtering Formulation.\u003c\/p\u003e \u003cp\u003e5.4 Robust Nonlinear Control Design Methods.\u003c\/p\u003e \u003cp\u003e5.4.1 Bounding Control.\u003c\/p\u003e \u003cp\u003e5.4.2 Sliding Mode Control.\u003c\/p\u003e \u003cp\u003e5.4.3 Lyapunov Redesign Method.\u003c\/p\u003e \u003cp\u003e5.4.4 Nonlinear Damping.\u003c\/p\u003e \u003cp\u003e5.4.5 Adaptive Bounding Control.\u003c\/p\u003e \u003cp\u003e5.5 Adaptive Nonlinear Control.\u003c\/p\u003e \u003cp\u003e5.6 Concluding Summary.\u003c\/p\u003e \u003cp\u003e5.7 Exercises and Design Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. ADAPTIVE APPROXIMATION: MOTIVATION AND ISSUES.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Perspective for Adaptive Approximation Based Control.\u003c\/p\u003e \u003cp\u003e6.2 Stabilization of a Scalar System.\u003c\/p\u003e \u003cp\u003e6.2.1 Feedback Linearization.\u003c\/p\u003e \u003cp\u003e6.2.2 Small-Signal Linearization.\u003c\/p\u003e \u003cp\u003e6.2.3 Unknown Nonlinearity with Known Bounds.\u003c\/p\u003e \u003cp\u003e6.2.4 Adaptive Bounding Methods.\u003c\/p\u003e \u003cp\u003e6.2.5 Approximating the Unknown Nonlinearity.\u003c\/p\u003e \u003cp\u003e6.2.6 Combining Approximation with Bounding Methods.\u003c\/p\u003e \u003cp\u003e6.2.7 Combining Approximation with Adaptive Bounding Methods.\u003c\/p\u003e \u003cp\u003e6.2.8 Summary.\u003c\/p\u003e \u003cp\u003e6.3 Adaptive Approximation Based Tracking.\u003c\/p\u003e \u003cp\u003e6.3.1 Feedback Linearization.\u003c\/p\u003e \u003cp\u003e6.3.2 Tracking via Small-Signal Linearization.\u003c\/p\u003e \u003cp\u003e6.3.3 Unknown Nonlinearities with Known Bounds.\u003c\/p\u003e \u003cp\u003e6.3.4 Adaptive Bounding Design.\u003c\/p\u003e \u003cp\u003e6.3.5 Adaptive Approximation of the Unknown Nonlinearities.\u003c\/p\u003e \u003cp\u003e6.3.6 Robust Adaptive Approximation.\u003c\/p\u003e \u003cp\u003e6.3.7 Combining Adaptive Approximation with Adaptive Bounding.\u003c\/p\u003e \u003cp\u003e6.3.8 Some Adaptive Approximation Issues.\u003c\/p\u003e \u003cp\u003e6.4 Nonlinear Parameterized Adaptive Approximation.\u003c\/p\u003e \u003cp\u003e6.5 Concluding Summary.\u003c\/p\u003e \u003cp\u003e6.6 Exercises and Design Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. ADAPTIVE APPROXIMATION BASED CONTROL: GENERAL THEORY.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Problem Formulation.\u003c\/p\u003e \u003cp\u003e7.1.1 Trajectory Tracking.\u003c\/p\u003e \u003cp\u003e7.1.2 System.\u003c\/p\u003e \u003cp\u003e7.1.3 Approximator.\u003c\/p\u003e \u003cp\u003e7.1.4 Control Design.\u003c\/p\u003e \u003cp\u003e7.2 Approximation Based Feedback Linearization.\u003c\/p\u003e \u003cp\u003e7.2.1 Scalar System.\u003c\/p\u003e \u003cp\u003e7.2.2 Input-State.\u003c\/p\u003e \u003cp\u003e7.2.3 Input-Output.\u003c\/p\u003e \u003cp\u003e7.2.4 Control Design Outside the Approximation Region D.\u003c\/p\u003e \u003cp\u003e7.3 Approximation Based Backstepping.\u003c\/p\u003e \u003cp\u003e7.3.1 Second Order Systems.\u003c\/p\u003e \u003cp\u003e7.3.2 Higher Order Systems.\u003c\/p\u003e \u003cp\u003e7.3.3 Command Filtering Approach.\u003c\/p\u003e \u003cp\u003e7.3.4 Robustness Considerations.\u003c\/p\u003e \u003cp\u003e7.4 Concluding Summary.\u003c\/p\u003e \u003cp\u003e7.5 Exercises and Design Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. ADAPTIVE APPROXIMATION BASED CONTROL FOR FIXED-WING AIRCRAFT.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Aircraft Model Introduction.\u003c\/p\u003e \u003cp\u003e8.1.1 Aircraft Dynamics.\u003c\/p\u003e \u003cp\u003e8.1.2 Non-dimensional Coefficients.\u003c\/p\u003e \u003cp\u003e8.2 Angular Rate Control for Piloted Vehicles.\u003c\/p\u003e \u003cp\u003e8.2.1 Model Representation.\u003c\/p\u003e \u003cp\u003e8.2.2 Baseline Controller.\u003c\/p\u003e \u003cp\u003e8.2.3 Approximation Based Controller.\u003c\/p\u003e \u003cp\u003e8.2.4 Simulation Results.\u003c\/p\u003e \u003cp\u003e8.3 Full Control for Autonomous Aircraft.\u003c\/p\u003e \u003cp\u003e8.3.1 Airspeed and Flight Path Angle Control.\u003c\/p\u003e \u003cp\u003e8.3.2 Wind-axes Angle Control.\u003c\/p\u003e \u003cp\u003e8.3.3 Body Axis Angular Rate Control.\u003c\/p\u003e \u003cp\u003e8.3.4 Control Law and Stability Properties.\u003c\/p\u003e \u003cp\u003e8.3.5 Approximator Definition.\u003c\/p\u003e \u003cp\u003e8.3.6 Simulation Analysis.\u003c\/p\u003e \u003cp\u003e8.4 Conclusions.\u003c\/p\u003e \u003cp\u003e8.5 Aircraft Notation.\u003c\/p\u003e \u003cp\u003eAppendix A: Systems and Stability Concepts.\u003c\/p\u003e \u003cp\u003eA.1 Systems Concepts.\u003c\/p\u003e \u003cp\u003eA.2 Stability Concepts.\u003c\/p\u003e \u003cp\u003eA.2.1 Stability Definitions.\u003c\/p\u003e \u003cp\u003eA.2.2 Stability Analysis Tools.\u003c\/p\u003e \u003cp\u003eA.3 General Results.\u003c\/p\u003e \u003cp\u003eA.4 Prefiltering.\u003c\/p\u003e \u003cp\u003eA.5 Other Useful Results.\u003c\/p\u003e \u003cp\u003eA.5.1 Smooth Approximation of the Signum function.\u003c\/p\u003e \u003cp\u003eA.6 Problems.\u003c\/p\u003e \u003cp\u003eAppendix B: Recommended Implementation and Debugging Approach.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"Wiley-Blackwell","offers":[{"title":"Default Title","offer_id":53515432034647,"sku":"9780471727880","price":121.46,"currency_code":"GBP","in_stock":true}],"url":"https:\/\/bookcurl.com\/products\/adaptive-approximation-based-control-unifying-neural-fuzzy-and-traditional-adaptive-approximation-approaches-9780471727880","provider":"Book Curl","version":"1.0","type":"link"}