Dynamic Modeling and Neural Network-Based Intelligent Control of Flexible Systems: (IEEE Press Series on Control Systems Theory and Applications)

Comprehensive treatment of several representative flexible systems, ranging from dynamic modeling and intelligent control design through to stability analysis Fully illustrated throughout, Dynamic Modeling and Neural Network-Based Intelligent Control of Flexible Systems proposes high-efficiency modeling methods and novel intelligent control strategies for several representative flexible systems developed by means of neural networks. It discusses tracking control of multi-link flexible manipulators, vibration control of flexible buildings under natural disasters, and fault-tolerant control of bionic flexible flapping-wing aircraft and addresses common challenges like external disturbances, dynamic uncertainties, output constraints, and actuator faults. Expanding on its theoretical deliberations, the book includes many case studies demonstrating how the proposed approaches work in practice. Experimental investigations are carried out on Quanser Rotary Flexible Link, Quanser 2 DOF Serial Flexible Link, Quanser Active Mass Damper, and Quanser Smart Structure platforms. The book starts by providing an overview of dynamic modeling and intelligent control of flexible systems, introducing several important issues, along with modeling and control methods of three typical flexible systems. Other topics include: Foundational mathematical preliminaries including the Hamilton principle, model discretization methods, Lagrange’s equation method, and Lyapunov’s stability theorem Dynamic modeling of a single-link flexible robotic manipulator and vibration control design for a string with the boundary time-varying output constraint Unknown time-varying disturbances, such as earthquakes and strong winds, and how to suppress them and use MATLAB and Quanser to verify effectiveness of a proposed control Adaptive vibration control methods for a single-floor building-like structure equipped with an active mass damper (AMD) Dynamic Modeling and Neural Network-Based Intelligent Control of Flexible Systems is an invaluable resource for researchers and engineers seeking high-efficiency modeling methods and neural-network-based control solutions for flexible systems, along with industry engineers and researchers who are interested in control theory and applications and students in related programs of study.

Table of Contents:
About the Authors xi Preface xiii Acknowledgments xvii Acronyms xix 1 Introduction 1 1.1 Background and Motivation 1 1.2 Modeling and Control Strategies of Flexible Robotic Manipulators 5 1.3 Vibration Control Technologies of Flexible Building-like Structures 7 1.4 Modeling and Control Approaches of Bionic Flexible Flapping-wing Aircraft 8 1.5 Outline of the Book 9 2 Mathematical Preliminaries 13 2.1 Mathematical Preliminaries 13 2.1.1 Hamilton Principle 13 2.1.2 Model Discretization 14 2.1.2.1 Assumed Mode Method 14 2.1.2.2 Finite Rigid Element Method 14 2.1.3 Lagrange’s Equation Method 15 2.1.4 Neural Networks 15 2.1.5 Lyapunov Stability Theorem 16 2.1.6 Summary 18 3 Fuzzy Neural Network Control of the Single-Link Flexible Robotic Manipulator 19 3.1 Introduction 19 3.2 Problem Formulation 21 3.2.1 Dynamic Modeling 21 3.2.2 Model Discretization 22 3.3 Fuzzy Neural Network Control 24 3.3.1 Control Design 24 3.3.2 Stability Analysis 27 3.4 Numerical Simulations 30 3.4.1 Without Control 31 3.4.2 PD Control 32 3.4.3 Full-State Feedback 34 3.4.4 Output Feedback 34 3.5 Experimental Investigation 34 3.5.1 Experimental Testbed 34 3.5.2 Experimental Results 38 3.6 Summary 41 4 High-Gain Observer-Based Neural Network Control of the Two-Link Flexible Robotic Manipulator 43 4.1 Introduction 43 4.2 Problem Formulation 44 4.2.1 Dynamic Modeling 44 4.2.2 Model Discretization 47 4.3 High-Gain Observer-Based Neural Network Control 47 4.3.1 Control Design 47 4.3.2 Stability Analysis 49 4.4 Numerical Simulations 53 4.4.1 Simulation Results for Open-Loop System 53 4.4.2 Simulation Results for PD Control 54 4.4.3 Simulation Results for Neural Network Control 54 4.4.4 Comparison Between PD and NN Simulation Results 55 4.5 Experimental Investigation 58 4.5.1 Introduction of the Experimental Testbed 58 4.5.2 Experimental Results 58 4.5.3 Comparison Between PD and NN Experiment Results 61 4.6 Summary 62 5 Robust Adaptive Vibration Control for a String with Time-Varying Output Constraint 65 5.1 Introduction 65 5.2 Problem Formulation 67 5.2.1 Dynamics of the String System 67 5.2.2 Preliminaries 69 5.3 Control Design 69 5.3.1 Exact Model-Based Boundary Control 69 5.3.2 Robust Adaptive Boundary Control for System Parametric Uncertainty 72 5.4 The Solvability of the Inequality Equations 76 5.5 Numerical Simulations 81 5.6 Summary 84 6 Neural Network Vibration Control of a Stand-Alone Tall Building-Like Structure with an Eccentric Load 85 6.1 Introduction 85 6.2 Dynamic Modeling 88 6.2.1 Dynamic Modeling 88 6.2.2 Model Discretization 89 6.3 Neural Network Vibration Control 92 6.3.1 Control Design 92 6.3.2 Stability Analysis 93 6.4 Numerical Simulations 96 6.4.1 Simulation Parameters 96 6.4.2 Simulation Results 96 6.5 Experimental Investigation 100 6.5.1 Introduction of the Experimental Testbed 100 6.5.2 Experimental Results 101 6.6 Summary 105 7 Adaptive Vibration Control of a Flexible Structure Based on Hybrid Learning Controlled Active Mass Damping 107 7.1 Introduction 107 7.2 Dynamic Modeling 109 7.3 Hybrid Learning Control 113 7.3.1 Disturbance Observer Design 113 7.3.2 Hybrid Learning Control Design 115 7.3.3 Full-order State Observer 118 7.4 Simulation Verification and Comparative Analysis 118 7.5 Experimental Investigation 120 7.5.1 Experimental Results of Passive Mode 122 7.5.2 Experimental Results of PV Position Controller 124 7.5.3 Experimental Results of HL Controller 125 7.5.4 Comparisons and Discussions 128 7.6 Summary 129 8 Reinforcement Learning Control of a Single-Floor Building-Like Structure with Active Mass Damper 131 8.1 Introduction 131 8.2 Problem Formulation 132 8.2.1 Dynamic Modeling 132 8.2.2 Model Discretization 134 8.3 Reinforcement Learning Control 134 8.3.1 Control Design 134 8.3.2 Stability Analysis 136 8.4 Experimental Investigation 137 8.5 Summary 141 9 Disturbance Observer-Based Neural Network Control of a Flexible Flapping-Wing System 143 9.1 Introduction 143 9.2 Problem Formulation 144 9.2.1 Dynamic Modeling 144 9.2.2 Model Discretization 146 9.3 Disturbance Observer-Based Neural Network Control 148 9.3.1 Control Design 148 9.3.2 Stability Analysis 152 9.3.3 Simulation Results Without Control 155 9.3.4 Simulation Results for PD Control 155 9.3.5 Simulation Results for Full-State Feedback 155 9.3.6 Simulation Results for Output Feedback 158 9.4 Summary 159 10 Adaptive Finite-Time Control of a Bionic Flexible Flapping-Wing Aircraft with Actuator Failures 161 10.1 Introduction 161 10.2 Problem Formulation 163 10.2.1 Dynamic Modeling 164 10.2.2 Model Discretization 165 10.3 Adaptive Finite-Time Control 167 10.3.1 Control Design 167 10.3.2 Stability Analysis 169 10.4 Numerical Simulations 172 10.5 Summary 181 11 Adaptive Vibration Control for Two-Stage Bionic Flapping Wings Based on Neural Network Algorithm 183 11.1 Introduction 183 11.2 Problem Formulation 184 11.2.1 Dynamic Modeling 184 11.2.2 Model Discretization 185 11.3 Adaptive Vibration Control 186 11.3.1 Control Design 186 11.3.2 Stability Analysis 188 11.4 Numerical Simulations 191 11.5 Summary 195 12 Boundary Vibration Control of a Floating Wind Turbine System with Mooring Lines 197 12.1 Introduction 197 12.2 System Modeling and Preliminaries 199 12.2.1 Dynamical Model of Floating Wind Turbine Vibrations 200 12.2.2 Preliminaries 201 12.3 Controller Design 202 12.4 Numerical Simulations 206 12.5 Summary 215 13 Conclusions 217 References 219 Index 243

About the Author :
Hejia Gao, PhD, is an Associate Professor at the School of Artificial Intelligence, Anhui University, Hefei, China. Previously, she was a Visiting Researcher at the Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Canada. She has published over 30 international journal and conference papers. Her research interests include neural networks, reinforcement learning, flexible systems, and vibration control. Wei He, PhD, is a Full Professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing, China. He has co-authored three books and published over 100 international journal and conference papers. He was awarded a Newton Advanced Fellowship from the Royal Society, UK, in 2017. His research interests include adaptive control, vibration control, and bionic flapping wing aircraft. Changyin Sun, PhD, is a Professor at the School of Automation, Southeast University, Nanjing, China. He has co-authored four books and published over 160 international journal papers. Prof. Sun is a Chinese Association of Automation Fellow. His research interests include intelligent control, flight control, pattern recognition, and optimal theory.