LEADER 04300nam  22006495  450 
001 9910163992203321
005 20200629215308.0
010   $a3-319-53312-6
024 7 $a10.1007/978-3-319-53312-4
035   $a(CKB)3710000001051595
035   $a(DE-He213)978-3-319-53312-4
035   $a(MiAaPQ)EBC4801155
035   $a(PPN)198871511
035   $a(EXLCZ)993710000001051595
100   $a20170207d2017      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDecentralized Neural Control: Application to Robotics /$fby Ramon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma y. Alanis, Jose A. Ruz-Hernandez
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (XV, 111 p. 54 illus., 3 illus. in color.) 
225 1 $aStudies in Systems, Decision and Control,$x2198-4182 ;$v96
311   $a3-319-53311-8 
320   $aIncludes bibliographical references at the end of each chapters.
327   $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.
330   $aThis book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural inverse optimal control for stabilization. The fourth decentralized neural inverse optimal control is designed for trajectory tracking. This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work. .
410  0$aStudies in Systems, Decision and Control,$x2198-4182 ;$v96
606   $aComputational intelligence
606   $aControl engineering
606   $aRobotics
606   $aAutomation
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aControl and Systems Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/T19010
606   $aRobotics and Automation$3https://scigraph.springernature.com/ontologies/product-market-codes/T19020
615  0$aComputational intelligence.
615  0$aControl engineering.
615  0$aRobotics.
615  0$aAutomation.
615 14$aComputational Intelligence.
615 24$aControl and Systems Theory.
615 24$aRobotics and Automation.
676   $a006.32
700   $aGarcia-Hernandez$b Ramon$4aut$4http://id.loc.gov/vocabulary/relators/aut$0868642
702   $aLopez-Franco$b Michel$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aSanchez$b Edgar N$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aAlanis$b Alma y$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aRuz-Hernandez$b Jose A$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910163992203321
996   $aDecentralized Neural Control: Application to Robotics$91939092
997   $aUNINA