000 04152nam a22006015i 4500
001 978-3-319-53312-4
003 DE-He213
005 20220801222725.0
007 cr nn 008mamaa
008 170207s2017 sz | s |||| 0|eng d
020 _a9783319533124
_9978-3-319-53312-4
024 7 _a10.1007/978-3-319-53312-4
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aGarcia-Hernandez, Ramon.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_963009
245 1 0 _aDecentralized Neural Control: Application to Robotics
_h[electronic resource] /
_cby Ramon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma y. Alanis, Jose A. Ruz-Hernandez.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXV, 111 p. 54 illus., 3 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v96
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 _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 0 _aComputational intelligence.
_97716
650 0 _aControl engineering.
_931970
650 0 _aRobotics.
_92393
650 0 _aAutomation.
_92392
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aControl and Systems Theory.
_931972
650 2 4 _aControl, Robotics, Automation.
_931971
700 1 _aLopez-Franco, Michel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_963010
700 1 _aSanchez, Edgar N.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_963011
700 1 _aAlanis, Alma y.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_963012
700 1 _aRuz-Hernandez, Jose A.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_963013
710 2 _aSpringerLink (Online service)
_963014
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319533117
776 0 8 _iPrinted edition:
_z9783319533131
776 0 8 _iPrinted edition:
_z9783319851235
830 0 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v96
_963015
856 4 0 _uhttps://doi.org/10.1007/978-3-319-53312-4
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c81086
_d81086