000			04290cam a22006618i 4500
001			ocn965446716
003			OCoLC
005			20220711203337.0
006			m o d
007			cr |||||||||||
008			161205s2017 si ob 001 0 eng
010			_a 2016056133
040			_aDLC _beng _erda _cDLC _dOCLCO _dOCLCF _dDG1 _dOCLCQ _dN$T _dYDX _dIDEBK _dEBLCP
020			_a9781119189053 _q(electronic bk.)
020			_a1119189055 _q(electronic bk.)
020			_z9781119189060
020			_z1119189063
020			_z9781119189077
020			_z1119189071
020			_z9781119189046 _q(hardback)
035			_a(OCoLC)965446716
042			_apcc
050	1	0	_aTJ217.5
072		7	_aTEC _x009000 _2bisacsh
082	0	0	_a629.8/9 _223
084			_aTEC037000 _2bisacsh
049			_aMAIN
100	1		_aYang, Shiping, _d1987- _eauthor. _96721
245	1	0	_aIterative learning control for multi-agent systems coordination / _cby Shiping Yang, Jian-Xin Xu, Xuefang Li, Dong Shen.
263			_a1705
264		1	_aSingapore : _bJohn Wiley & Sons, Inc., _c2017.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bn _2rdamedia
338			_aonline resource _bnc _2rdacarrier
520			_a"This book gives a comprehensive overview of the intersection between ILC and MAS, the range of topics include basic to advanced theories, rigorous mathematics to engineering practice, and linear to nonlinear systems. It addresses the crucial multi-agent coordination and control challenges that can be solved by ILC methods. Through systematic discussion of network theory and intelligent control, the authors explore future research possibilities, develop new tools, and provide numerous applications such as the power grid, communication and sensor networks, intelligent transportation system, and formation control. Readers will gain a roadmap to the latest advances in the fields and use their newfound knowledge to design their own algorithms"-- _cProvided by publisher.
505	0		_aOptimal Iterative Learning Control for Multi-agent Consensus Tracking -- Iterative Learning Control for Multi-agent Coordination Under Iteration-Varying Graph -- Iterative Learning Control for Multi-agent Coordination with Initial State Error -- Multi-agent Consensus Tracking with Input Sharing by Iterative Learning Control -- A HOIM-Based Iterative Learning Control Scheme for Multi-agent Formation -- P-type Iterative Learning for Non-parameterized Systems with Uncertain Local Lipschitz Terms -- Synchronization for Nonlinear Multi-agent Systems by Adaptive Iterative Learning Control -- Distributed Adaptive Iterative Learning Control for Nonlinear Multi-agent Systems with State Constraints -- Synchronization for Networked Lagrangian Systems under Directed Graphs -- Generalized Iterative Learning for Economic Dispatch Problem in a Smart Grid -- Summary and Future Research Directions -- Appendix A: Graph Theory Revisit -- Appendix B: Detailed Proofs.
504			_aIncludes bibliographical references and index.
588	0		_aPrint version record and CIP data provided by publisher; resource not viewed.
650		0	_aIntelligent control systems. _93412
650		0	_aMultiagent systems. _94974
650		0	_aMachine learning. _91831
650		0	_aIterative methods (Mathematics) _96722
650		7	_aTECHNOLOGY & ENGINEERING _xRobotics. _2bisacsh _96723
650		7	_aIntelligent control systems. _2fast _0(OCoLC)fst00975911 _93412
650		7	_aIterative methods (Mathematics) _2fast _0(OCoLC)fst00980827 _96722
650		7	_aMachine learning. _2fast _0(OCoLC)fst01004795 _91831
650		7	_aMultiagent systems. _2fast _0(OCoLC)fst01749717 _94974
650		7	_aTECHNOLOGY & ENGINEERING / Engineering (General) _2bisacsh _96724
655		4	_aElectronic books. _93294
700	1		_aXu, Jian-Xin, _eauthor. _96725
700	1		_aLi, Xuefang, _d1985- _eauthor. _96726
700	1		_aShen, Dong, _d1982- _eauthor. _96727
776	0	8	_iPrint version: _aYang, Shiping, 1987- author. _tIterative learning control for multi-agent systems coordination. _dSingapore : John Wiley & Sons, Inc., 2017 _z9781119189046 _w(DLC) 2016052027
856	4	0	_uhttps://doi.org/10.1002/9781119189053 _zWiley Online Library
942			_cEBK
994			_a92 _bDG1
999			_c68728 _d68728