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020			_a9789811904646
024	7		_a10.1007/978-981-19-0464-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA402.3 _b2022 EB
100	1		_aChi, Ronghu _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684475
245	1	0	_aDiscrete-Time Adaptive Iterative Learning Control _bFrom Model-Based to Data-Driven _cby Ronghu Chi, Na Lin, Huimin Zhang, Ruikun Zhang.
250			_aFirst edition 2022
264		1	_aSingapore : _bSpringer Singapore : _bImprint: Springer, _c2022
300			_a1 recurso en línea (X, 206 páginas) _b83 ilustraciones, 72 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aIntelligent Control and Learning Systems _x2662-5466 _v1
505	0		_aChapter 1: Introduction -- Part 1: Model-based Discrete-time Adaptive ILC -- Chapter 2: Discrete-time Adaptive ILC for Linear Parametric Systems -- Chapter 3: Discrete-time Adaptive ILC for Higher order Parametric Systems -- Chapter 4: Data-weighted Discrete-time Adaptive ILC Chapter -- 5: Discrete-time Adaptive ILC for Nonparametric Nonlinear Systems Part 2: Data-driven Discrete-time Adaptive ILC Chapter -- 6: Neural Network based Discrete-time Adaptive ILC -- Chapter 7: Data-driven Discrete-time Adaptive ILC for Nonaffined Nonlinear Systems -- Chapter 8: Multi-input Enhanced Data-driven Discrete-time Adaptive ILC -- Chapter 9: High-order Internal Model based Data-driven Terminal Adaptive ILC -- Chapter 10: Conclusions Appendices.
520			_aThis book belongs to the subject of control and systems theory. The discrete-time adaptive iterative learning control (DAILC) is discussed as a cutting-edge of ILC and can address random initial states, iteration-varying targets, and other non-repetitive uncertainties in practical applications. This book begins with the design and analysis of model-based DAILC methods by referencing the tools used in the discrete-time adaptive control theory. To overcome the extreme difficulties in modeling a complex system, the data-driven DAILC methods are further discussed by building a linear parametric data mapping between two consecutive iterations. Other significant improvements and extensions of the model-based/data-driven DAILC are also studied to facilitate broader applications. The readers can learn the recent progress on DAILC with consideration of various 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.
988			_aSpringer_Robotics_2022
650		7	_2embne _9145606 _aControl, Teoría de
650		7	_2embne _9157265 _aSistemas de tiempo discreto
650		7	_2embne _9670888 _aMétodos iterativos
700	1		_aLin, Na _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684476
700	1		_aZhang, Huimin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684477
700	1		_aZhang, Ruikun _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684478
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789811904639
776	0	8	_iPrinted edition: _z9789811904653
776	0	8	_iPrinted edition: _z9789811904660
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-0464-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2022 _dz _eIG _zSI