Decentralized Neural Control: Application to Robotics (Studies in Systems, Decision and Control, 96)
Garcia-Hernandez, Ramon; Lopez-Franco, Michel; Sanchez, Edgar N.; Alanis, Alma Y.; Ruz-Hernandez, Jose A.
Language: English
Published by Springer, 2017
Series: Book 85 of 378 - Studies in Systems, Decision and Control
- Hardcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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In English.
Seller Inventory # ria9783319533117_new
- Title
- Decentralized Neural Control: Application to Robotics (Studies in Systems, Decision and Control, 96)
- Author
- Garcia-Hernandez, Ramon; Lopez-Franco, Michel; Sanchez, Edgar N.; Alanis, Alma Y.; Ruz-Hernandez, Jose A.
- Publisher
- Springer
- Publication year
- 2017
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3319533118
- ISBN 13
- 9783319533117
- Item weight
- 481 grams
- Series
- Book 85 of 378: Studies in Systems, Decision and Control
This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors.
This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF).
The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold.
The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network.The thirdcontrol scheme applies a decentralized neural inverse optimal control for stabilization.
The fourth decentralized neural inverse optimal control is designed for trajectory tracking.
This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work.
"Synopsis" may belong to another edition of this title.
From the Back Cover
This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors.
This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF).
The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold.
The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network.
The third control scheme applies a decentralized neural inverse optimal control for stabilization.
The fourth decentralized neural inverse optimal control is designed for trajectory tracking.
This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work.
"About the title" may belong to another edition of this title.
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