Kinematic Control of Redundant Robot Arms Using Neural Networks

Shuai Li, Long Jin, Mohammed Aquil Mirza

• Publisher: Wiley-IEEE Press
• Year: 2019
• ISBN:
  9781119556992
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Robotics
• Index: Technology & Engineering

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, and the advantages 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.

Part I
Neural Networks for Serial Robot Arm Control

1
Zeroing Neural Networks for Control

1.1 Introduction

In addition to the remarkable features such as parallelism, distributed storage, and adaptive self‐learning capability, neural networks can be readily implemented by hardware, and have thus been applied widely in many fields [1–6]. The zeroing neural network (ZNN) as well as its variant (i.e. zeroing dynamic), as a systematic approach to the online solution of time‐varying problems with scalar situation included, has been applied to online matrix inversion [7], motion generation and control of redundant robot manipulators [3], and tracking control of nonlinear chaotic systems [8]. For example, a ZNN model with a nonlinear function activated is applied to the kinematic control of redundant robot manipulators via Jacobian matrix pseudoinversion in [9], which achieves high accuracy but cannot handle the bound constraints existing in the robots. In [10], present a finite‐time convergent ZNN model is presented for solving dynamic quadratic programs with application to robot tracking, which requires convex activation functions and cannot remedy the issue of joint‐limit avoidance. Such a ZNN method is further discretized to compute the solution to time‐varying nonlinear equations based on a new three‐step formula, which can be implemented on a digital computer directly. In addition, for the applications, ZNN is exploited ...