000			01962nam a22003135i 4500
001			91296
005			20231026104003.0
010			_a978-981-19-5950-9 _dcompra
090			_a91296
100			_a20231023d2022 k||y0pory50 ba
101	0		_aeng
102			_aSG
200	1		_aData-driven iterative learning control for discrete-time systems _bDocumento eletrónico _fby Ronghu Chi, Yu Hui, Zhongsheng Hou
210			_aSingapore _cSpringer Nature Singapore _cSpringer _d2022
215			_aX, 235 p. _cil.
225	2		_aIntelligent Control and Learning Systems _v2
303			_aThis book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system's output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.
606			_aControl engineering
606			_aStochastic processes
606			_aMathematics _xData processing
680			_aTJ212-225
700	1		_aChi _bRonghu
701	1		_aHui _bYu
701	1		_aHou _bZhongsheng
801		0	_aPT _gRPC
856	4		_uhttps://doi.org/10.1007/978-981-19-5950-9
942			_2lcc _cF _n0