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008			170613s2018 si | s |||| 0|eng d
020			_a9789811040801
024	7		_a10.1007/978-981-10-4080-1 _2doi
050		4	_aT57.83 _bW457 2018 EB
100	1		_aWei, Qinglai _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/nb2017014262 _1http://viaf.org/viaf/4773069/
245	1	0	_aSelf-Learning Optimal Control of Nonlinear Systems _bAdaptive Dynamic Programming Approach _cby Qinglai Wei, Ruizhuo Song, Benkai Li, Xiaofeng Lin.
264		1	_aSingapore _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XVIII, 230 páginas 86 ilustraciones, 73 ilustraciones a color.)
347			_atext file _bPDF
490	0		_aStudies in Systems, Decision and Control _x2198-4182 _v103
505	0		_aChapter 1. Principle of Adaptive Dynamic Programming -- Chapter 2. An Iterative ϵ-Optimal Control Scheme for a Class of Discrete-Time Nonlinear Systems With Unfixed Initial State -- Chapter 3. Discrete-Time Optimal Control of Nonlinear Systems Via Value Iteration-Based Q-Learning -- Chapter 4. A Novel Policy Iteration Based Deterministic Q-Learning for Discrete-Time Nonlinear Systems -- Chapter 5. Nonlinear Neuro-Optimal Tracking Control Via Stable Iterative Q-Learning Algorithm -- Chapter 6. Model-Free Multiobjective Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems with General Performance Index Functions -- Chapter 7. Multi-Objective Optimal Control for a Class of Unknown Nonlinear Systems Based on Finite-Approximation-Error ADP Algorithm -- Chapter 8. A New Approach for a Class of Continuous-Time Chaotic Systems Optimal Control by Online ADP Algorithm -- Chapter 9. Off-Policy IRL Optimal Tracking Control for Continuous-Time Chaotic Systems -- Chapter 10. ADP-Based Optimal Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks.
520	3		_aThis book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which quantitatively obtain the optimal control schemes of the systems. It analyzes the properties identified by the programming methods, including the convergence of the iterative value functions and the stability of the system under iterative control laws, helping to guarantee the effectiveness of the methods developed. When the system model is known, self-learning optimal control is designed on the basis of the system model; when the system model is not known, adaptive dynamic programming is implemented according to the system data, effectively making the performance of the system converge to the optimum. With various real-world examples to complement and substantiate the mathematical analysis, the book is a valuable guide for engineers, researchers, and students in control science and engineering.
650		7	_aProgramación dinámica _9154628
700	1		_aSong, Ruizhuo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9671302
700	1		_aLi, Benkai _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aLin, Xiaofeng _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/no2004009252 _1http://viaf.org/viaf/73861936/
776	0	8	_iEdición impresa: _z9789811040795
776	0	8	_iEdición impresa: _z9789811040818
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-10-4080-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b01/2019 _dz _ea _feng _ggw _h0
999			_c102818 _d102818 _x1