Functional Adaptive Control

by Fabri, Simon G.; Kadirkamanathan, Visakan

• ISBN13: 9781852334383
• ISBN10: 185233438X
• Format: Hardcover
• Copyright: 2001-02-01
• Publisher: Springer Verlag

Summary

This book presents a wide range of techniques that lead to novel strategies for effecting intelligent control of complex systems that are typically characterised by uncertainty, nonlinear dynamics, component failure, unpredictable disturbances, multi-modality and high dimensional spaces. The underlying design philosophy is based on effecting closed-loop control in the presence of plant or environmental uncertainty and complexity by utilizing various types of neural network architectures, ranging from multilayer perceptron to radical basis function and modular network models. The uncertainty and complexity are typified by unknown nonlinear functionals, and temporal or spatial multi-modality. Deterministic and stochastic conditions, as well as continuous and discrete time dynamics are taken into consideration. The presented designs are firmly rooted in the techniques of adaptive control, reconfigurable control, multiple model control, stochastic adaptive control, lyapunov stability theory and neural networks. The techniques are shown to enhance the performance of the control system in the presence of the higher levels of complexity and uncertainty associated with modern plants, which demand superior intelligence and autonomy from the controller. The presented designs are supported both by theory and by numerous results from simulation experiments. The book also includes extensive reviews on general aspects concerning the fields of intelligent, nonlinear and stochastic control.

Table of Contents

List of Figures	xv
Acronyms	xix
Mathematical Notation	xxi
Part I. Introduction
Introduction	3	(20)
Intelligent Control Systems	3	(2)
Approaches to Intelligent Control	5	(5)
Contribution of Adaptive Control	5	(1)
Contribution of Artificial Intelligence	6	(2)
Confluence of Adaptive Control and AI: Intelligent Control	8	(2)
Enhancing the Performance of Intelligent Control	10	(6)
Multiple Model Schemes: Dealing with Complexity	10	(4)
Stochastic Adaptive Control: Dealing with Uncertainty	14	(2)
The Objectives and their Rationale	16	(7)
Part II. Deterministic Systems
Adaptive Control of Nonlinear Systems	23	(24)
Introduction	23	(1)
Continuous-time Systems	23	(13)
Control by Feedback Linearization	24	(3)
Control by Backstepping	27	(2)
Adaptive Control	29	(7)
Discrete-time Systems	36	(10)
Affine Approximations and Feedback Linearization	42	(1)
Adaptive Control	42	(4)
Summary	46	(1)
Dynamic Structure Networks for Stable Adaptive Control	47	(32)
Introduction	47	(2)
Problem Formulation	49	(1)
Fixed-structure Network Solutions	50	(4)
Dynamic Network Structure	54	(2)
The Control Law and Error Dynamics	56	(2)
The Adaptive System	58	(1)
Stability Analysis	59	(3)
Evaluation of Control Parameters and Implementation	62	(8)
The Disturbance Bound	62	(3)
Choice of the Boundary Layer	65	(2)
Comments	67	(1)
Implementation	68	(2)
Simulation Examples	70	(7)
Example 1	70	(3)
Example 2	73	(4)
Summary	77	(2)
Composite Adaptive Control of Continuous-Time Systems	79	(22)
Introduction	79	(2)
Problems Formulation	81	(1)
The Neural Networks	81	(2)
The Control Law	83	(1)
Composite Adaptation	84	(4)
The Indentification Model	84	(2)
The Adaptation Law	86	(2)
Stability Analysis	88	(3)
Determination of the Disturbance Bounds	91	(1)
Simulation Examples	92	(8)
Example 1	92	(6)
Example 2	98	(2)
Summary	100	(1)
Functional Adaptive Control of Discrete-Time Systems	101	(30)
Introduction	101	(1)
Problem Formulation	102	(1)
The Neural Network	103	(1)
The Control Law	104	(1)
The Adaptive System	104	(1)
Stability Analysis	105	(6)
Tracking Error Convergence	111	(1)
Simulation Examples	112	(5)
Example 1	113	(2)
Example 2	115	(2)
Extension to Adaptive Sliding Mode Control	117	(9)
Definitions of a Discrete-time Sliding Mode	117	(3)
Adaptive Sliding Mode Control	120	(1)
Problem Formulation	121	(1)
The Control Law	122	(1)
The Adaptive System	122	(1)
Stability Analysis	123	(1)
Sliding and Tracking Error Convergence	123	(1)
Simulation Example	124	(2)
Summary	126	(5)
Part III. Stochastic Systems
Stochastic Control	131	(16)
Introduction	131	(1)
Fundamental Principles	132	(2)
Classes of Stochastic Control Problems	134	(2)
Dual Control	136	(9)
Degrees of Interaction	137	(1)
Solutions to the Implementation Problem	137	(8)
Conclusions	145	(2)
Dual Adaptive Control of Nonlinear Systems	147	(18)
Introduction	147	(1)
Problem Formulation	148	(1)
Dual Controller Design	149	(9)
GaRBF Dual Controller	149	(4)
Sigmoidal MLP Dual Controller	153	(4)
Analysis of the Control Laws	157	(1)
Simulation Examples and Performance Evaluation	158	(5)
Example 1	158	(3)
Example 2	161	(2)
Summary	163	(2)
Multiple Model Approaches	165	(22)
Introduction	165	(1)
Basic Formulation	165	(13)
Multiple Model Adaptive Control	168	(2)
Jump Systems	170	(8)
Adaptive IO Models	178	(7)
Scheduled Mode Transitions	179	(6)
Summary	185	(2)
Multiple Model Dual Adaptive Control of Jump Nonlinear Systems	187	(26)
Introduction	187	(2)
Problem Formulation	189	(2)
The Estimation Problem	191	(6)
Known Mode Case	191	(2)
Unknown Mode Case	193	(4)
Self-organized Allocation of Local Models	197	(3)
The Control Law	200	(4)
Known Mode Case	200	(1)
Unknown Mode Case	201	(3)
Simulation Examples and Performance Evaluation	204	(8)
Example 1	205	(5)
Example 2	210	(2)
Summary	212	(1)
Multiple Model Dual Adaptive Control of Spatial Multimodal Systems	213	(32)
Introduction	213	(2)
Problem Formulation	215	(1)
The Modular Network	216	(1)
The Estimation Problem	217	(7)
Local Model Parameter Estimation	217	(3)
Validity Function Estimation	220	(4)
The Control Law	224	(7)
Known System Case	224	(3)
Unknown System Case	227	(4)
Simulation Examples and Performance Evaluation	231	(9)
Example 1	231	(5)
Example 2	236	(3)
Performance Evaluation	239	(1)
Summary	240	(5)
Part IV. Conclusions
Conclusions	245	(5)
References	250	(15)
Index	265