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Kinematic Control of Redundant Robot Arms Using Neural Networks : a Theoretical Study.
"In this book, focusing on robot arm control aided with neural networks, we present and investigate different methods and schemes for the control of robot arms. The idea for this book on the redundancy resolution of robot manipulators via different methods and schemes was conceived during the r...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Newark :
John Wiley & Sons, Incorporated,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Intro; Title Page; Copyright Page; Contents; List of Figures; List of Tables; Preface; Acknowledgments; Part I Neural Networks for Serial Robot Arm Control; Chapter 1 Zeroing Neural Networks for Control; 1.1 Introduction; 1.2 Scheme Formulation and ZNN Solutions; 1.2.1 ZNN Model; 1.2.2 Nonconvex Function Activated ZNN Model; 1.3 Theoretical Analyses; 1.4 Computer Simulations and Verifications; 1.4.1 ZNN for Solving (1.13) at t = 1; 1.4.2 ZNN for Solving (1.13) with Different Bounds; 1.5 Summary; Chapter 2 Adaptive Dynamic Programming Neural Networks for Control; 2.1 Introduction
- 2.2 Preliminaries on Variable Structure Control of the Sensor-Actuator System2.3 Problem Formulation; 2.4 Model-Free Control of the Euler-Lagrange System; 2.4.1 Optimality Condition; 2.4.2 Approximating the Action Mapping and the Critic Mapping; 2.5 Simulation Experiment; 2.5.1 The Model; 2.5.2 Experiment Setup and Result; 2.6 Summary; Chapter 3 Projection Neural Networks for Robot Arm Control; 3.1 Introduction; 3.2 Problem Formulation; 3.3 A Modified Controller without Error Accumulation; 3.3.1 Existing RNN Solutions; 3.3.2 Limitations of Existing RNN Solutions; 3.3.3 The Presented Algorithm
- 3.3.4 Stability3.4 Performance Improvement Using Velocity Compensation; 3.4.1 A Control Law with Velocity Compensation; 3.4.2 Stability; 3.5 Simulations; 3.5.1 Regulation to a Fixed Position; 3.5.2 Tracking of Time-Varying References; 3.5.3 Comparisons; 3.6 Summary; Chapter 4 Neural Learning and Control Co-Design for Robot Arm Control; 4.1 Introduction; 4.2 Problem Formulation; 4.3 Nominal Neural Controller Design; 4.4 A Novel Dual Neural Network Model; 4.4.1 Neural Network Design; 4.4.2 Stability; 4.5 Simulations; 4.5.1 Simulation Setup; 4.5.2 Simulation Results; 4.5.2.1 Tracking Performance
- 4.5.2.2 With vs. Without Excitation Noises4.6 Summary; Chapter 5 Robust Neural Controller Design for Robot Arm Control; 5.1 Introduction; 5.2 Problem Formulation; 5.3 Dual Neural Networks for the Nominal System; 5.3.1 Neural Network Design; 5.3.2 Convergence Analysis; 5.4 Neural Design in the Presence of Noises; 5.4.1 Polynomial Noises; 5.4.1.1 Neural Dynamics; 5.4.1.2 Practical Considerations; 5.4.2 Special Cases; 5.4.2.1 Constant Noises; 5.4.2.2 Linear Noises; 5.5 Simulations; 5.5.1 Simulation Setup; 5.5.2 Nominal Situation; 5.5.3 Constant Noises; 5.5.4 Time-Varying Polynomial Noises
- 5.6 SummaryChapter 6 Using Neural Networks to Avoid Robot Singularity; 6.1 Introduction; 6.2 Preliminaries; 6.3 Problem Formulation; 6.3.1 Manipulator Kinematics; 6.3.2 Manipulability; 6.3.3 Optimization Problem Formulation; 6.4 Reformulation as a Constrained Quadratic Program; 6.4.1 Equation Constraint: Speed Level Resolution; 6.4.2 Redefinition of the Objective Function; 6.4.3 Set Constraint; 6.4.4 Reformulation and Convexification; 6.5 Neural Networks for Redundancy Resolution; 6.5.1 Conversion to a Nonlinear Equation Set; 6.5.2 Neural Dynamics for Real-Time Redundancy Resolution