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Recently, there has been considerable research interest in neural network control of robots, and satisfactory results have been obtained in solving some of the special issues associated with the problems of robot control in an “on-and-off” fashion. This book is dedicated to issues on adaptive control of robots based on neural networks. The text has been carefully tailored to (i) give a comprehensive study of robot dynamics, (ii) present structured network models for robots, and (iii) provide systematic approaches for neural network based adaptive controller design for rigid robots, flexible joint robots, and robots in constraint motion. Rigorous proof of the stability properties of adaptive neural network controllers is provided. Simulation examples are also presented to verify the effectiveness of the controllers, and practical implementation issues associated with the controllers are also discussed.
Contents:
- Mathematical Background
- Dynamic Modelling of Robots
- Structured Network Modelling of Robots
- Adaptive Neural Network Control of Robots
- Neural Network Model Reference Adaptive Control
- Flexible Joint Robots
- Task Space and Force Control
Readership: Electrical & electronic engineers, mechanical engineers and researchers in robotics, mechatronics, control and neural networks.
