000			03279nam a22003735i 4500
001			291393
003			MX-SnUAN
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007			cr nn 008mamaa
008			150903s2008 xxk| o |||| 0|eng d
020			_a9781848002333 _99781848002333
024	7		_a10.1007/9781848002333 _2doi
035			_avtls000344227
039		9	_a201509030405 _bVLOAD _c201405050304 _dVLOAD _y201402061251 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
100	1		_aHuang, Biao. _eautor _9322123
245	1	0	_aDynamic Modeling, Predictive Control and Performance Monitoring : _bA Data-driven Subspace Approach / _cby Biao Huang, Ramesh Kadali.
264		1	_aLondon : _bSpringer London, _c2008.
300			_brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aLecture Notes in Control and Information Sciences, _x0170-8643 ; _v374
500			_aSpringer eBooks
505	0		_aI Dynamic Modeling through Subspace Identification -- System Identification: Conventional Approach -- Open-loop Subspace Identification -- Closed-loop Subspace Identification -- Identification of Dynamic Matrix and Noise Model Using Closed-loop Data -- II Predictive Control -- Model Predictive Control: Conventional Approach -- Data-driven Subspace Approach to Predictive Control -- III Control Performance Monitoring -- Control Loop Performance Assessment: Conventional Approach -- State-of-the-art MPC Performance Monitoring -- Subspace Approach to MIMO Feedback Control Performance Assessment -- Prediction Error Approach to Feedback Control Performance Assessment -- Performance Assessment with LQG-benchmark from Closed-loop Data.
520			_aA typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aKadali, Ramesh. _eautor _9322664
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781848002326
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-84800-233-3 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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