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Informationen zum Autor SHUAI LI, PhD, is Assistant Professor in the Department of Computing at the Hong Kong Polytechnic University. LONG JIN, PhD, is Postdoctoral Fellow in the Department of Computing at the Hong Kong Polytechnic University. MOHAMMED AQUIL MIRZA, M.S., is a Doctorate Research Scholar with Hong Kong Polytechnic University. Klappentext Presents pioneering and comprehensive work on engaging movement in robotic arms, with a specific focus on neural networks This book presents and investigates different methods and schemes for the control of robotic arms whilst exploring the field from all angles. On a more specific level, it deals with the dynamic-neural-network based kinematic control of redundant robot arms by using theoretical tools and simulations. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural Networks for Parallel Robot Control; and Neural Networks for Cooperative Control. The book starts by covering zeroing neural networks for control, and follows up with chapters on adaptive dynamic programming neural networks for control; projection neural networks for robot arm control; and neural learning and control co-design for robot arm control. Next, it looks at robust neural controller design for robot arm control and teaches readers how to use neural networks to avoid robot singularity. It then instructs on neural network based Stewart platform control and neural network based learning and control co-design for Stewart platform control. The book finishes with a section on zeroing neural networks for robot arm motion generation. Provides comprehensive understanding on robot arm control aided with neural networks Presents neural network-based control techniques for single robot arms, parallel robot arms (Stewart platforms), and cooperative robot arms Provides a comparison of using neural networks for control purposes rather than traditional control based methods Includes simulation and modelling tasks (e.g., MATLAB) for onward application for research and engineering development By focusing on robot arm control aided by neural networks whilst examining central topics surrounding the field, Kinematic Control of Redundant Robot Arms Using Neural Networks is an excellent book for graduate students and academic and industrial researchers studying neural dynamics, neural networks, analog and digital circuits, mechatronics, and mechanical engineering. Inhaltsverzeichnis List of Figures xiii List of Tables xix Preface xxi Acknowledgments xxv Part I Neural Networks for Serial Robot Arm Control 1 1 Zeroing Neural Networks for Control 3 1.1 Introduction 3 1.2 Scheme Formulation and ZNN Solutions 4 1.2.1 ZNN Model 4 1.2.2 Nonconvex Function Activated ZNN Model 8 1.3 Theoretical Analyses 9 1.4 Computer Simulations and Verifications 12 1.4.1 ZNN for Solving (1.13) at t = 1 12 1.4.2 ZNN for Solving (1.13) with Different Bounds 15 1.5 Summary 16 2 Adaptive Dynamic Programming Neural Networks for Control 17 2.1 Introduction 17 2.2 Preliminaries on Variable Structure Control of the Sensor-Actuator System 18 2.3 Problem Formulation 19 2.4 Model-Free Control of the Euler-Lagrange System 20 2.4.1 Optimality Condition 21 2.4.2 Approximating the Action Mapping and the Critic Mapping 21 2.5 Simulation Experiment 23 2.5.1 The Model 23 2.5.2 Experiment Setup and Results 24 2.6 Summary 25 3 Projection Neural Networks for Robot Arm Control 27 3.1 Introduction 27 3.2 Problem Formulation 29 3.3 A Modified Controller without Error Accumulation 30 3.3.1 Existing RNN Solutions 30 3.3.2 Limitations o...
