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001			85060
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007			cr nn 008mamaa
008			151201s2016 gw | s |||| 0|eng d
020			_a9783319248530
040			_aES-MaUEC
050		4	_aTJ217.6 _b.K68 2016 EB
082	0	4	_a629.8
100	1		_aKouvaritakis, Basil _0Local _0http://id.loc.gov/authorities/names/nb2001041610 _1http://viaf.org/viaf/269800911 _997736
245	1	0	_aModel Predictive Control : _bClassical, Robust and Stochastic _cby Basil Kouvaritakis, Mark Cannon
250			_a1st ed.
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XIII, 384 páginas) _b54 ilustraciones, 3 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aAdvanced Textbooks in Control and Signal Processing _x1439-2232
505	0		_aFrom the Contents: Introduction -- Classical Model Predictive Control -- Robust Model Predictive Control with Additive Uncertainty: Open-loop Optimization Strategies -- Robust Model Predictive Control with Additive Uncertainty: Closed-loop Optimization Strategies.
520			_aFor the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplicative and stochastic model uncertainty. The book provides: extensive use of illustrative examples; sample problems; and discussion of novel control applications such as resource allocation for sustainable development and turbine-blade control for maximized power capture with simultaneously reduced risk of turbulence-induced damage. Graduate students pursuing courses in model predictive control or more generally in advanced or process control and senior undergraduates in need of a specialized treatment will find Model Predictive Control an invaluable guide to the state of the art in this important subject. For the instructor it provides an authoritative resource for the construction of courses.
650		7	_aIngeniería mecánica _2embne _9146662
650		7	_aControl automático _2embne _9405125
700	1		_aCannon, Mark. _945111 _0http://id.loc.gov/authorities/names/nb91491554 _1http://viaf.org/viaf/16116043
710	2		_aSpringerLink (Online service) _0Local _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729 _9106996
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-24853-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319248530
907			_a.b12943381 _b10-10-17 _c21-11-16
942			_2lcc _cLE
945			_aEBOOK EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i1161318x _z30-06-17
988	0	0	_aEBOOK, EBSPRINGER, GOBI_sep2018
988			_aEbook_one2one
998			_aSI _a_alco _a_vill _b10/2018 _cm _dz _ea _feng _ggw _h0
999			_c85060 _d85060 _x1