Adaptive Sliding Mode Neural Network Control for Nonlinear Systems

Description

Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields. It offers instructive examples and simulations, along with the source codes, and provides the basic architecture of control science and engineering. Cover Adaptive Sliding Mode Neural Network Control for Nonlinear Systems Copyright Author Biographies Preface Acknowledgments Introduction An Overview of Each Chapter 1. Basic Concepts 1.1 Lyapunov Stability 1.1.1 Lyapunov Stability Theory 1.1.1.1 Introduction 1.1.1.2 Autonomous System 1.1.1.3 Equilibrium State 1.1.1.4 Lyapunov Stability 1.1.2 Lyapunov Asymptotic Stability 1.1.2.1 Definition of Lyapunov Asymptotic Stability 1.1.2.2 Example of Lyapunov Asymptotic Stability 1.1.3 Lyapunov Uniform Asymptotic Stability 1.1.3.1 Definition of Lyapunov Uniform Asymptotic Stability 1.1.3.2 The Relationship Between Lyapunov Asymptotic Stability and Lyapunov Uniform Asymptotic Stability 1.1.4 Lyapunov Global Asymptotic Stability 1.1.4.1 Definition of Lyapunov Global Asymptotic Stability 1.1.4.2 The Relationship Between Lyapunov Asymptotic Stability and Lyapunov Global Asymptotic Stability 1.1.5 Lyapunov Instability 1.1.5.1 The Geometric Interpretation of Definition of Lyapunov Instability 1.1.5.2 The Mathematical Description of Lyapunov Instability 1.1.6 Positive Definite Function 1.1.6.1 Definition 1.1.6.2 The Relationship Between Positive Definite Function and Negative Definite Function 1.1.6.3 Example of Positive Definite Function 1.1.7 Lyapunov Function 1.1.7.1 Definition of Lyapunov Function 1.1.7.2 Construction of Lyapunov function 1.1.8 Lyapunov Stability Theorem and Lyapunov Global Uniform Asymptotic Stability Theorem 1.1.8.1 Lyapunov Local Uniform Asymptotic Stability Theorem 1.1.8.2 Example of Lyapunov Stability Theorem 1.1.9 Robust Stability 1.2 Advanced Nonlinear Systems Control 1.3 Intelligent Methodology References 2. Nonlinear Systems Analysis Approach 2.1 Exponential Stability Analysis of Cellular Neural Networks Based on Linear Matrix Inequality 2.1.1 System Formulation and Preliminaries 2.1.2 Main Results 2.1.3 Simulation Example 2.2 Robust Lyapunov Stability Analysis of the Cellular Neural Networks Based on Linear Matrix Inequality 2.2.1 System Formulation and Preliminaries 2.2.2 Main Results 2.2.3 Simulation Example References 3. Classical Nonlinear Systems Control Methods 3.1 Sliding Mode Control 3.1.1 Design of Supertwisting Control Systems 3.1.2 Design of U-Supertwisting Controller 3.1.3 Simulation Example 3.2 Backstepping Control 3.2.1 System Formulation and Preliminaries 3.2.2 Main Results 3.2.3 Simulation Example References 4. Advanced Nonlinear Systems Controller Design 4.1 Supertwisting Synchronization Control of Chaotic System-Based U-Model Method 4.1.1 Problem Description 4.1.2 Synchronization Control Based on Supertwisting Algorithm 4.1.3 Simulation Example 4.2 Supertwisting Sliding Mode Control of Nonlinear System-Based U-Model Method 4.2.1 Problem Description 4.2.2 One-Order Nonlinear System Control 4.2.3 N-order Nonlinear System Control 4.2.4 Simulation Example 4.3 Sliding Mode Controller Design for Nonlinear Systems With Matching Perturbations 4.3.1 Nonlinear Dynamic Plant Model 4.3.2 U-Model Method 4.3.3 U-Model 4.3.4 Design of U-Model Sliding Mode Control 4.3.5 Stability Analysis 4.3.6 Simulation Example References 5. Intelligent Methodology 5.1 Neural Network Identification and Control for Nonlinear Dynamic Systems 5.1.1 System Description and Preliminaries 5.1.2 System Identification With Neural Network 5.1.3 Indirect Adaptive Controller Design 5.1.4 Algorithm for Implement 5.1.5 Simulation Studies 5.2 Finite-Time Adaptive Neural Network Control 5.2.1 System Description and Preliminaries 5.2.2 Adaptive NNs Control 5.2.3 Simulation Example 5.2.3.1 Mathematical Example 5.2.3.2 Plate-Ball Example References 6. Applications 6.1 Aircraft Path Planning Based on Neural Networks 6.1.1 Path Model Based on Risk Assessment Model 6.1.2 Path Planning Based on PCNNs 6.1.3 Network Architecture 6.1.4 Path Planning Based on Neural Networks 6.1.5 Simulation Example 6.2 ADS-B for Multilateration System Using BP Neural Network 6.2.1 Multilateration 6.2.2 Multilateration System Theory and Positioning Algorithm 6.2.3 The Establishment and Process of BP Neural Network 6.2.4 The Simulation of BP Neural Network References Further Reading Index A B C E F G H I L M N O P R S T U Back Cover