000			03241cam a2200409Mi 4500
001			95087
003			ES-MaUEC
005			20240111050133.0
006			m o d
007			cr |n|||||||||
008			161227s2017 sz ob 000 0 eng d
020			_a3319507907 _q(electronic bk.)
020			_a9783319507903 _q(electronic bk.)
020			_z3319507893
020			_z9783319507897
035			_a(OCoLC)967266328 _z(OCoLC)967317883 _z(OCoLC)967721042 _z(OCoLC)967854601 _z(OCoLC)972461602 _z(OCoLC)972537874 _z(OCoLC)972743581 _z(OCoLC)974650885 _z(OCoLC)1005781065 _z(OCoLC)1011905100
040			_aYDX _cYDX _dN$T _dIDEBK _dAZU _dGW5XE _dOCLCO _dUAB _dOCLCF _dCOO _dOCLCQ _dN$T _dMERUC _dUPM _dIOG _dESU _dOTZ _dOCLCQ _dVT2 _dU3W _dES-MaUEC _bspa
050		4	_aQ172.5.V37 _bS345 2017 EB
100	1		_aScheinker, Alexander.
245	1	0	_aModel-free stabilization by extremum seeking _cAlexander Scheinker, Miroslav Krstić.
264		1	_aCham _bSpringer _c2017
300			_a1 recurso en línea
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aSpringerBriefs in electrical and computer engineering _x2191-8112
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
505	0		_aIntroduction -- Weak Limit Averaging for Studying the Dynamics of Extremum-Seeking-Stabilized Systems -- Minimization of Lyapunov Functions -- Control Affine Systems -- Non-C2 Extremum Seeking -- Bounded Extremum Seeking -- Extremum Seeking for Stabilization of Systems Not Affine in Control -- General Choice of Extremum-Seeking Dithers -- Application Study: Particle Accelerator Tuning.
520	3		_aWith this brief, the authors present algorithms for model-free stabilization of unstable dynamic systems. An extremum-seeking algorithm assigns the role of a cost function to the dynamic system's control Lyapunov function (clf) aiming at its minimization. The minimization of the clf drives the clf to zero and achieves asymptotic stabilization. This approach does not rely on, or require knowledge of, the system model. Instead, it employs periodic perturbation signals, along with the clf. The same effect is achieved as by using clf-based feedback laws that profit from modeling knowledge, but in a time-average sense. Rather than use integrals of the systems vector field, we employ Lie-bracket-based (i.e., derivative-based) averaging. The brief contains numerous examples and applications, including examples with unknown control directions and experiments with charged particle accelerators. It is intended for theoretical control engineers and mathematicians, and practitioners working in various industrial areas and in robotics.
650		7	_aInteligencia artificial _2embne _0(OCoLC)fst00817247 _0 _9413115
700	1		_aKrstić, Miroslav _963930
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-50790-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017B
998			_b02/2018 _dz _e- _zSI
999			_c95087 _d95087 _x1