Sliding-Mode Fuzzy Controllers (Studies in Systems, Decision and Control, 357)

✍ Scribed by Mojtaba Ahmadieh Khanesar, Okyay Kaynak, Erdal Kayacan

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 252
• Category: Library

✦ Synopsis

This book addresses some of the challenges suffered by the well-known and robust sliding-mode control paradigm. The authors show how the fusion of fuzzy systems with sliding-mode controllers can alleviate some of these problems and promote applicability.

Fuzzy systems used as soft switches eliminate high-frequency signal oscillations and can substantially lower the noise sensitivity of sliding-mode controllers. The amount of a priori knowledge required concerning the nominal structure and parameters of a nonlinear system is also shown to be much reduced by exploiting the general function-approximation property of fuzzy systems so as to use them as identifiers.

The main features of this book include:

• a review of various existing structures of sliding-mode fuzzy control;

• a guide to the fundamental mathematics of sliding-mode fuzzy controllers and their stability analysis;

• state-of-the-art procedures for the design of a sliding-mode fuzzy controller;

• source codes including MATLAB® and Simulink® codes illustrating the simulation of these controllers, particularly the adaptive controllers;

• a short bibliography for each chapter for readers interested in learning more on a particular subject; and

• illustrative examples and simulation results to support the main claims made in the text.

Academic researchers and graduate students interested in the control of nonlinear systems and particularly those working in sliding-mode controller design will find this book a valuable source of comparative information on existing controllers and ideas for the development of new ones.

✦ Table of Contents

Preface
Contents
About the Authors
Abbreviations
1 Preliminaries
1.1 Introduction
1.2 Mathematical Tools
1.2.1 Lie Derivative
1.2.2 Lie Bracket
1.2.3 Diffeomorphism
1.2.4 Change in Coordination
1.3 Input/State Linearization
1.4 Input–Output Linearization
1.5 Definition of Stability
1.6 Stability Analysis
References
2 Classical Sliding-Mode Controllers
2.1 Introduction
2.2 SMC of Second-Order Nonlinear Systems
2.2.1 Constant Control Signal Coefficient Case
2.2.2 Nonlinear System with a Function as the Gain of Control Signal
2.3 Integral Sliding Surface
2.4 SMC for Higher-Order Nonlinear Systems
2.5 Adaptive Sliding-Mode Approaches
2.5.1 Adaptive Tuning of the Controller Parameters
2.5.2 Online Identification of System Parameters
2.5.3 Adding Robustness to the Adaptation Laws
2.6 Nonlinear and Time-Varying Sliding Surfaces
2.7 Terminal SMC
2.8 SMC with Mismatched Uncertainties
References
3 Fuzzy Logic Systems
3.1 Introduction
3.1.1 Fuzzy Logic and Control
3.1.2 Boolean Versus Fuzzy Sets
3.2 Type-1 Fuzzy Logic Systems
3.2.1 The Fuzzifier
3.3 Type-2 Fuzzy Sets and Systems
3.3.1 Existing IT2MFs
3.3.2 Output Processing Unit
3.3.3 Popular Existing Output Processing Units
3.3.4 Center-of-Set Type-Reducer Without Sorting Requirement Algorithm
3.3.5 Family of Non-Iterative Output Processing Units
References
4 Rule-Based Sliding-Mode Fuzzy Logic Control
4.1 Introduction
4.2 Fuzzy Logic System to Tune Sliding-Mode Controller Parameters
4.2.1 Boundary Layer with Constant Boundary Width
4.2.2 Boundary Layer with Adaptive Boundary Width
4.3 Direct Sliding-Mode Fuzzy Logic Systems
References
5 Adaptive Sliding-Mode Fuzzy Control Systems: Gradient Descent Method
5.1 Introduction
5.2 The Concept of the Gradient Descent Method
5.2.1 Newton and Gauss–Newton Optimization Algorithm
5.2.2 Levenberg–Marquardt Optimization Algorithm
5.3 Sliding-Mode Theory-Based Cost Functions
5.4 Gradient Descent-Based Sliding-Mode Fuzzy Control of a DC–DC Converter
5.4.1 The Model of DC–DC Converter and its Computer Simulation
5.4.2 Design of IT2FLS for DC–DC Converter
5.4.3 Simulation Results
5.5 Application to Control an IM
5.5.1 Field-Oriented Control of IM
5.5.2 Interval Type-2 Fuzzy Neural Network Controller
References
6 Adaptive Sliding-Mode Fuzzy Control Systems: Lyapunov Approach
6.1 Sliding-Mode Adaptive Type-1 Fuzzy Controller Design
6.1.1 Constant Control Signal Coefficient Case
6.1.2 Nonlinear Control Signal Coefficient Case
6.1.3 Adding PI to Sliding-Mode Fuzzy Controller
6.1.4 Sliding-Mode Direct Adaptive Fuzzy Control
6.1.5 Tuning Antecedent Part Parameters
6.1.6 Terminal Sliding-Mode Adaptive Fuzzy Controller
6.2 Interval Type-2 Fuzzy Control
6.2.1 Indirect Case
6.2.2 Direct Controller
6.3 Robustness Issues
6.3.1 Modification of the Adaptation Law Using a σ Term
6.3.2 Modification of the Adaptation Law Using a ε Term
6.4 Guaranteed Cost Controller Design
6.4.1 Constant Control Signal Coefficient Case
6.5 Type-2 Feedback Error Learning Controller
6.6 The Proposed Controller Structure
6.6.1 PD Controller
6.6.2 Interval Type-2 Fuzzy Logic Systems
6.6.3 Sliding-Mode-Based Training Method
6.6.4 Implementation of the Proposed Approach on a 2-DOF Helicopter
References
7 Adaptive Network Sliding-Mode Fuzzy Logic Control Systems
7.1 Introduction
7.2 Applications of Network Control Systems
7.2.1 Automotive Industry
7.2.2 Process Control Systems
7.2.3 Fly-by-Wire
7.2.4 Teleoperation
7.2.5 Smart Grids
7.3 Common Challenges of Direct Digital Control Systems and Network Counterparts
7.3.1 Quantization
7.3.2 Sample Time
7.3.3 Delays
7.4 Main Constraints Imposed When Controlling over Network
7.4.1 Network-Induced Time Delays
7.4.2 Packet Losses and Disorder
7.4.3 Variable Transmission and Sample Time
7.5 Sliding-Mode Fuzzy Logic Control Techniques …
7.5.1 Design of an Adaptive Sliding-Mode Fuzzy Logic Controller with State Prediction
7.5.2 Design of an Adaptive Sliding-Mode Fuzzy Controller
7.5.3 Simulation Results and Discussions
7.5.4 Time-Varying Network-Induced Time Delay Case
References
8 Sliding-Mode Fuzzy Logic Teleoperation Controllers
8.1 Sliding-Mode Fuzzy Logic Teleoperation of Robotic Manipulator
8.1.1 The Dynamics of Teleoperation Systems
8.1.2 Controller Design
8.1.3 Simulation Results
8.1.4 Discussions
References
9 Intelligent Optimization of Sliding-Mode Fuzzy Logic Controllers
9.1 Single-Objective Optimization Algorithms
9.1.1 Evolutionary Single-Objective Algorithms
9.1.2 Swarm Intelligence
9.2 Multi-objective Optimization
9.2.1 Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II)
9.3 Strength Pareto Evolutionary Algorithm (SPEA2)
9.3.1 Multi-objective Particle Swarm Optimization (MOPSO)
9.4 Multi-objective Optimization of Sliding-Mode Fuzzy Logic Controllers
9.4.1 Sliding-Mode Fuzzy Controller for Rotary Inverted Pendulum
9.4.2 Multi-objective Tuning of the Parameters of Sliding-Mode Fuzzy Logic Controller
9.4.3 Parameters Used in MOEAs
9.4.4 Performance Metrics
9.4.5 Simulation Results
References
Index