Approximation Based Control

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

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.