Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control)

A 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. 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. 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. Preface. 1. INTRODUCTION. 1.1 Systems and Control Terminology. 1.2 Nonlinear Systems. 1.3 Feedback Control Approaches. 1.4 Components of Approximation Based Control. 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. 2.4 Approximator Properties. 2.5 Summary. 2.6 Exercises and Design Problems. 3. APPROXIMATION STRUCTURES. 3.1 Model Types. 3.2 Polynomials. 3.3 Splines. 3.4 Radial Basis Functions. 3.5 Cerebellar Model Articulation Controller. 3.6 Multilayer Perceptron. 3.7 Fuzzy Approximation. 3.8 Wavelets. 3.9 Further Reading. 3.10 Exercises and Design Problems. 4. PARAMETER ESTIMATION METHODS. 4.1 Formulation for Adaptive Approximation. 4.2 Derivation of Parametric Models. 4.3 Design of On-Line Learning Schemes. 4.4 Continuous-Time Parameter Estimation. 4.5 On-Line Learning: Analysis. 4.6 Robust Learning Algorithms. 4.7 Concluding Summary. 4.8 Exercises and Design Problems. 5. NONLINEAR CONTROL ARCHITECTURES. 5.1 Small-Signal Linearization. 5.2 Feedback Linearization. 5.3 Backstepping. 5.4 Robust Nonlinear Control Design Methods. 5.5 Adaptive Nonlinear Control. 5.6 Concluding Summary. 5.7 Exercises and Design Problems. 6. ADAPTIVE APPROXIMATION: MOTIVATION AND ISSUES. 6.1 Perspective for Adaptive Approximation Based Control. 6.2 Stabilization of a Scalar System. 6.3 Adaptive Approximation Based Tracking. 6.4 Nonlinear Parameterized Adaptive Approximation. 6.5 Concluding Summary. 6.6 Exercises and Design Problems. 7. ADAPTIVE APPROXIMATION BASED CONTROL: GENERAL THEORY. 7.1 Problem Formulation. 7.2 Approximation Based Feedback Linearization. 7.3 Approximation Based Backstepping. 7.4 Concluding Summary. 7.5 Exercises and Design Problems. 8. ADAPTIVE APPROXIMATION BASED CONTROL FOR FIXED-WING AIRCRAFT. 8.1 Aircraft Model Introduction. 8.2 Angular Rate Control for Piloted Vehicles. 8.3 Full Control for Autonomous Aircraft. 8.4 Conclusions. 8.5 Aircraft Notation. Appendix A: Systems and Stability Concepts. A.1 Systems Concepts. A.2 Stability Concepts. A.3 General Results. A.4 Prefiltering. A.5 Other Useful Results. A.6 Problems. Appendix B: Recommended Implementation and Debugging Approach. References. Index.