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020 _a3319533126
_q(electronic bk.)
020 _a9783319533124
_q(electronic bk.)
020 _z3319533118
020 _z9783319533117
_q(print)
035 _a(OCoLC)971891199
_z(OCoLC)972234259
_z(OCoLC)972449880
_z(OCoLC)972591591
_z(OCoLC)972803999
_z(OCoLC)972967213
_z(OCoLC)973116748
_z(OCoLC)981774866
_z(OCoLC)1005825035
_z(OCoLC)1012051447
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050 4 _aQA76.87
_bD434 2017 EB
245 0 0 _aDecentralized neural control :
_bapplication to robotics
_cRamon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma Y. Alanis, Jose A. Ruz-Hernandez.
264 1 _aCham, Switzerland
_bSpringer
_c2017
300 _a1 recurso en línea
336 _aTexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aStudies in systems, decision and control
_vvolume 96
500 _aSpringerLink
_bSpringer Engineering eBooks 2017 English+International
504 _aIncluye referencias bibliográficas
505 0 _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.
520 3 _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.
650 7 _aRedes neuronales artificiales
_2embne
_0(OCoLC)fst01036260
_0
_9678664
700 1 _aAlanis, Alma Y.,
_eautor
700 1 _aGarcia-Hernandez, Ramon,
_eautor
700 1 _aLopez-Franco, Michel,
_eautor
700 1 _aRuz-Hernandez, Jose A.,
_eautor
700 1 _aSanchez, Edgar N.,
_eautor
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-53312-4
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988 _aEBOOK, asignarmaterias, EBSPRINGER_2017B
998 _b02/2018
_dz
_e-
_zSI
999 _c95350
_d95350
_x1