Adaptive Approximation Based Control

Jay A. Farrell 지음

Wiley-Interscience 2006년 04월 14일 출간

작품소개

책소개

A highly accessible and unified approach to the design and analysis of intelligent control systemsAdaptive Approximation Based Control is a tool every control designer should have in his or her control toolbox.Mixing approximation theory, parameter estimation, and feedback control, this book presents a unified approach designed to enable readers to apply adaptive approximation based control to existing systems, and, more importantly, to gain enough intuition and understanding to manipulate and combine it with other control tools for applications that have not been encountered before.The authors provide readers with a thought-provoking framework for rigorously considering such questions as:* What properties should the function approximator have?* Are certain families of approximators superior to others?* Can the stability and the convergence of the approximator parameters be guaranteed?* Can control systems be designed to be robust in the face of noise, disturbances, and unmodeled effects?* Can this approach handle significant changes in the dynamics due to such disruptions as system failure?* What types of nonlinear dynamic systems are amenable to this approach?* What are the limitations of adaptive approximation based control?Combining theoretical formulation and design techniques with extensive use of simulation examples, this book is a stimulating text for researchers and graduate students and a valuable resource for practicing engineers.

목차

Preface.1. INTRODUCTION.1.1 Systems and Control Terminology.1.2 Nonlinear Systems.1.3 Feedback Control Approaches.1.3.1 Linear Design.1.3.2 Adaptive Linear Design.1.3.3 Nonlinear Design.1.3.4 Adaptive Approximation Based Design.1.3.5 Example Summary.1.4 Components of Approximation Based Control.1.4.1 Control Architecture.1.4.2 Function Approximator.1.4.3 Stable Training Algorithm.1.5 Discussion and Philosophical Comments.1.6 Exercises and Design Problems.2. APPROXIMATION THEORY.2.1 Motivating Example.2.2 Interpolation.2.3 Function Approximation.2.3.1 Off-line (Batch) Function Approximation.2.3.2 Adaptive Function Approximation.2.4 Approximator Properties.2.4.1 Parameter (Non)Linearity.2.4.2 Classical Approximation Results.2.4.3 Network Approximators.2.4.4 Nodal Processors.2.4.5 Universal Approximator.2.4.6 Best Approximator Property.2.4.7 Generalization.2.4.8 Extent of Influence Function Support.2.4.9 Approximator Transparency.2.4.10 Haar Conditions.2.4.11 Multivariable Approximation by Tensor Products.2.5 Summary.2.6 Exercises and Design Problems.3. APPROXIMATION STRUCTURES.3.1 Model Types.3.1.1 Physically Based Models.3.1.2 Structure (Model) Free Approximation.3.1.3 Function Approximation Structures.3.2 Polynomials.3.2.1 Description.3.2.2 Properties.3.3 Splines.3.3.1 Description.3.3.2 Properties.3.4 Radial Basis Functions.3.4.1 Description.3.4.2 Properties.3.5 Cerebellar Model Articulation Controller.3.5.1 Description.3.5.2 Properties.3.6 Multilayer Perceptron.3.6.1 Description.3.6.2 Properties.3.7 Fuzzy Approximation.3.7.1 Description.3.7.2 Takagi-Sugeno Fuzzy Systems.3.7.3 Properties.3.8 Wavelets.3.8.1 Multiresolution Analysis (MRA).3.8.2 MRA Properties.3.9 Further Reading.3.10 Exercises and Design Problems.4. PARAMETER ESTIMATION METHODS.4

작가정보

저자(글) Jay A. Farrell

저자 :

저자 :

저자 :

저자 :

저자 :
저자 :
저자 :
JAY A. FARRELL, PhD, is Professor and former chair of the Department of Electrical Engineering at the University of California at Riverside. He was also principal investigator on projects involving intelligent and learning control systems for autonomous vehiclesat the Charles Stark Draper Laboratory, where he was awarded the Engineering Vice President's Best Technical Publication Award. He is the author of one other book and over 130 articles for technical publications.MARIOS M. POLYCARPOU, PhD, is Professor and Interim Head of the Department of Electrical and Computer Engineering at the University of Cyprus. Dr. Polycarpou is the Editor in Chief of the IEEE Transactions on Neural Networks. He is an IEEE Fellow and has published more than 150 articles for journals, books, and conference proceedings. Dr. Polycarpou was also the recipient of the William H. Middendorf Research Excellence Award at the University of Cincinnati.