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000779470 020__ $$a9783319533124$$q(electronic book)
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000779470 0247_ $$a10.1007/978-3-319-53312-4$$2doi
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000779470 24500 $$aDecentralized neural control :$$bapplication to robotics /$$cRamon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma Y. Alanis, Jose A. Ruz-Hernandez.
000779470 264_1 $$aCham, Switzerland :$$bSpringer,$$c2017.
000779470 300__ $$a1 online resource.
000779470 336__ $$atext$$btxt$$2rdacontent
000779470 337__ $$acomputer$$bc$$2rdamedia
000779470 338__ $$aonline resource$$bcr$$2rdacarrier
000779470 347__ $$atext file$$bPDF$$2rda
000779470 4901_ $$aStudies in systems, decision and control ;$$vvolume 96
000779470 504__ $$aIncludes bibliographical references.
000779470 5050_ $$aIntroduction -- Foundations -- Decentralized Neural Block Control -- Decentralized Neural Backstepping Control -- Decentralized Inverse Optimal Control for Stabilization: a CLF Approach -- Decentralized Inverse Optimal Control for Trajectory Tracking -- Robotics Application -- Conclusions.
000779470 506__ $$aAccess limited to authorized users.
000779470 520__ $$aThis book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural inverse optimal control for stabilization. The fourth decentralized neural inverse optimal control is designed for trajectory tracking. This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work. .
000779470 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 14, 2017).
000779470 650_0 $$aNeural networks (Computer science)
000779470 650_0 $$aNonlinear control theory.
000779470 7001_ $$aGarcia-Hernandez, Ramon,$$eauthor.
000779470 7001_ $$aLopez-Franco, Michel,$$eauthor.
000779470 7001_ $$aSanchez, Edgar N.,$$eauthor.
000779470 7001_ $$aAlanis, Alma Y.,$$eauthor.
000779470 7001_ $$aRuz-Hernandez, Jose A.,$$eauthor.
000779470 77608 $$iPrint version:$$z3319533118$$z9783319533117$$w(OCoLC)967824093
000779470 830_0 $$aStudies in systems, decision and control ;$$vv. 96.
000779470 852__ $$bebk
000779470 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-53312-4$$zOnline Access$$91397441.1
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