Decentralized Neural Control: Application to Robotics: 96 (Studies in Systems, Decision and Control, 96) - Tapa dura
Libro 85 de 378: Studies in Systems, Decision and Control
Sinopsis
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
De la contraportada
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Decentralized Neural Control: Application to Robotics (eng)
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Decentralized Neural Control : Application to Robotics
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Decentralized Neural Control: Application to Robotics
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Berlin Springer International Publishing Springer Feb 2017, 2017
ISBN 10: 3319533118
ISBN 13: 9783319533117
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 111 pp. Englisch. Nº de ref. del artículo: 9783319533117
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Decentralized Neural Control: Application to Robotics (Studies in Systems, Decision and Control, 96)
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Decentralized Neural Control : Application to Robotics
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Decentralized Neural Control: Application to Robotics
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Springer International Publishing, 2017
ISBN 10: 3319533118
ISBN 13: 9783319533117
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent research in decentralized neural controlIncludes applications to roboticsPresents results in simulation and real timePresents recent research in decentralized neural controlIncludes applications to robotics. Nº de ref. del artículo: 136104604
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Decentralized Neural Control: Application to Robotics
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Springer, Berlin, Springer International Publishing, Springer, 2017
ISBN 10: 3319533118
ISBN 13: 9783319533117
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783319533117
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Decentralized Neural Control: Application to Robotics
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