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Decentralized Neural Control: Application to Robotics
Hardcover
Series: Studies in Systems, Decision and Control, Book 96
Technology & EngineeringGeneral Computers
ISBN13: 9783319533117
Publisher: Springer Nature
Published: Feb 13 2017
Pages: 111
Weight: 0.79
Height: 0.38 Width: 6.14 Depth: 9.21
Language: English
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.
1 different editions
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Decentralized Neural Control: Application to Robotics
Garcia-Hernandez, Ramon
Lopez-Franco, Michel
Sanchez, Edgar N.
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