Fuzzy Sliding Mode Control and Observation of Complex Dynamic Systems and Applications

　　In the design of a control system w：ith structural uncertainties， parameter perturbations and environmental disturbances， designers normally try to minimise the use of a detailed mathematical model whose ambiguities can never be modelled accurately. One suitable approach for this kind of nonlinear controller designs， avoiding the use of complicated models， is to employ variable structure system theory， which has been successfully applied to a wide variety of systems having uncertainties in the representative system model. In meeting the specifications of the closed loop system， those conventional controller design strategies usually fail to achieve the desired characteristics. In order to upgrade the performance of the controller and facilitate the design procedure， one should integrate artificial intelligence， most likely from expert linguistic knowledge， with conventional control algorithms.

暂缺《Fuzzy Sliding Mode Control and Observation of Complex Dynamic Systems and Applications》作者简介

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 network 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 law approach
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 fuzzylogic schemes
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 with sliding 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