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Titlebook: Decentralized Neural Control: Application to Robotics; Ramon Garcia-Hernandez,Michel Lopez-Franco,Jose A. Book 2017 Springer International

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書目名稱Decentralized Neural Control: Application to Robotics
編輯Ramon Garcia-Hernandez,Michel Lopez-Franco,Jose A.
視頻videohttp://file.papertrans.cn/265/264149/264149.mp4
概述Presents recent research in decentralized neural control.Includes applications to robotics.Presents results in simulation and real time.Includes supplementary material:
叢書名稱Studies in Systems, Decision and Control
圖書封面Titlebook: Decentralized Neural Control: Application to Robotics;  Ramon Garcia-Hernandez,Michel Lopez-Franco,Jose A. Book 2017 Springer International
描述.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
出版日期Book 2017
關鍵詞Decentralized Neural Control; Robotics; Computational Intelligence; Intelligent Systems; Neural Control
版次1
doihttps://doi.org/10.1007/978-3-319-53312-4
isbn_softcover978-3-319-85123-5
isbn_ebook978-3-319-53312-4Series ISSN 2198-4182 Series E-ISSN 2198-4190
issn_series 2198-4182
copyrightSpringer International Publishing Switzerland 2017
The information of publication is updating

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https://doi.org/10.1007/978-3-663-09553-8training algorithm for a modified recurrent high order neural network (RHONN), in order to identify the plant model. Based on this model, a control law is derived, which combines discrete-time block control and sliding mode techniques. The block control approach is used to design a nonlinear sliding
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Schule und Rassismus in den USAng a suitable controller for each subsystem. Accordingly, each subsystem is approximated by an identifier using a discrete-time recurrent high order neural network (RHONN), trained with an extended Kalman filter (EKF) algorithm. The neural identifier scheme is used to model the uncertain nonlinear s
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