Iterative Learning Control for Deterministic Systems

ISBN-13: 9781447119142 / Angielski / Miękka / 2011 / 152 str.

Kevin L. Moore

Opis

Iterative Learning Control for Deterministic Systems is part of the new Advances in Industrial Control series, edited by Professor M.J. Grimble and Dr. M.A. Johnson of the Industrial Control Unit, University of Strathclyde. The material presented in this book addresses the analysis and design of learning control systems. It begins with an introduction to the concept of learning control, including a comprehensive literature review. The text follows with a complete and unifying analysis of the learning control problem for linear LTI systems using a system-theoretic approach which offers insight into the nature of the solution of the learning control problem. Additionally, several design methods are given for LTI learning control, incorporating a technique based on parameter estimation and a one-step learning control algorithm for finite-horizon problems. Further chapters focus upon learning control for deterministic nonlinear systems, and a time-varying learning controller is presented which can be applied to a class of nonlinear systems, including the models of typical robotic manipulators. The book concludes with the application of artificial neural networks to the learning control problem. Three specific ways to neural nets for this purpose are discussed, including two methods which use backpropagation training and reinforcement learning. The appendices in the book are particularly useful because they serve as a tutorial on artificial neural networks.

Spis treści

1 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.