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- Author: Sam Shuzhi Ge
- Publisher: World Scientific Publishing Company
- Year: 1998
- ISBN-10: 981023452X
- ISBN-13: 9789810234522
- Format: 16.1 x 22.5 x 2.5 cm, kieti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Adaptive Neural Network Control of Robotic Manipulators (e-book) (used book) | bookbook.eu
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Description
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.
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- Author: Sam Shuzhi Ge
- Publisher: World Scientific Publishing Company
- Year: 1998
- ISBN-10: 981023452X
- ISBN-13: 9789810234522
- Format: 16.1 x 22.5 x 2.5 cm, kieti viršeliai
- Language: English English
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.
Reviews