[内容简介]

In the design of a control system w:ith structuraluncertainties， parameter perturbations and environmentaldisturbances， designers normally try to minimise the use of adetailed mathematical model whose ambiguities can never be modelledaccurately. One suitable approach for this kind of nonlinearcontroller designs， avoiding the use of plicated models， is toemploy variable structure system theory， which has beensuccessfully applied to a wide variety of systems havinguncertainties in the representative system model. In meeting thespecifications of the closed loop system， those conventionalcontroller design strategies usually fail to achieve the desiredcharacteristics. In order to upgrade the performance of thecontroller and facilitate the design procedure， one shouldintegrate artificial intelligence， most likely from expertlinguistic knowledge， with conventional control algorithms.

[目录]

Preface
Table of Contents
Abbreviations
Symbols
1 Literature survey and background
1.1 Introduction
1.2 Variable structure systems
1.3 Discrete time variable structure control systems
1.4 Fusion ofartificialintelligence algorithms with SMC
1.4.1 Artiftcialintelligence
1.4.2 Fuzzy sliding mode contro
1.4.3 Adaptive fuzzy sliding mode control
1.4.4 Neural work based sliding mode control
1.4.5 Neural fuzzy based sliding mode control
1.5 Sliding mode observation
1.6 Applications and practice of sliding mode control
1.7 Summary

2 Prelinunarymethodologies
2.1 Introduction
2.2 Nonlinear systems and their contro
2.2.1 Nonlinear systems
2.2.2 Controlofnonlinear systems
2.3 Variable structure control
2.3.1 Variable structure systems
2.3.2 Sliding mode in variable structure systems
2.3.3 Sliding mode control design by the reaching lawapproach
2.4 Discrete time sliding mode control
2.4.1 Discrete time sliding mode control
2.4.2 DSMC control design by the reaching law approach
2.5 Fuzzylogiccontrol
2.5.1 Mamdani fuzzylogic systems
2.5.2 Takagi-sugeno fuzzylogic systems
2.6 Fuzzy adaptive control
2.7 Summary

3 Adaptive fuzzy sliding mode contro
3.1 Introduction
3.2 Fuzzyuruversalapproximatio
3.2.1 Fuzzy basis functions
3.2.2 Fuzzy universal approximation
3.3 AFSMC for SISO nonlinear systems
3.3.1 Problem statement
3.3.2 Conventional sliding mode control
3.3.3 Indirect adaptive controllaw based on fuzzylogicschemes
3.3.4 Lyapunov stability analysis
3.3.5 Simulation studies
3.4 AFSMC for MIMO nonlinear systems
3.4.1 Problem statement
3.4.2 Conventional sliding mode control
3.4.3 Adaptive fuzzy controllaw design
3.4.4 Simulation studies
3.5 Summary

4 Sliding mode observation
4.1 Introduction
4.2 State observation
4.3 Sliding mode observation
4.4 Nonlinear sliding mode observers for stochastic systems
4.4.1 Preliminaries and problem formulation
4.4.2 Adaptive sliding mode observer design
4.4.3 Convergence analysis of the observer
……
5 Adaptive observer based nonlinear stochastic system control withsliding mode schemes
6 Hovering control of a helicopter simulator
7 Adaptive control for robotic manipulators
8 Controller design for vehicle suspension systems
9 Fuzzy PID and sliding mode control