000 03861nam a22003615i 4500
001 292018
003 MX-SnUAN
005 20160429154933.0
007 cr nn 008mamaa
008 150903s2005 xxk| o |||| 0|eng d
020 _a9781846282478
_99781846282478
024 7 _a10.1007/1846282470
_2doi
035 _avtls000343762
039 9 _a201509030750
_bVLOAD
_c201404121000
_dVLOAD
_c201404090738
_dVLOAD
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_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
100 1 _aCodrons, Benoît.
_eautor
_9323549
245 1 0 _aProcess Modelling for Control :
_bA Unified Framework Using Standard Black-box Techniques /
_cby Benoît Codrons.
264 1 _aLondon :
_bSpringer London,
_c2005.
300 _axxxiii, 229 páginas 74 ilustraciones
_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 _aAdvances in Industrial Control,
_x1430-9491
500 _aSpringer eBooks
505 0 _aPreliminary Material -- Identification in Closed Loop for Better Control Design -- Dealing with Controller Singularities in Closed-loop Identification -- Model and Controller Validation for Robust Control in a Prediction-error Framework -- Control-oriented Model Reduction and Controller Reduction -- Some Final Words.
520 _aMany process control books focus on control design techniques, taking the construction of a process model for granted. Process Modelling for Control concentrates on the modelling steps underlying a successful design, answering questions like: How should I carry out the identification of my process in order to obtain a good model? How can I assess the quality of a model with a view to using it in control design? How can I ensure that a controller will stabilise a real process and achieve a pre-specified level of performance before implementation? What is the most efficient method of order reduction to facilitate the implementation of high-order controllers? Different tools, namely system identification, model/controller validation and order reduction are studied in a framework with a common basis: closed-loop identification with a controller that is close to optimal will deliver models with bias and variance errors ideally tuned for control design. As a result, rules are derived, applying to all the methods, that provide the practitioner with a clear way forward despite the apparently unconnected nature of the modelling tools. Detailed worked examples, representative of various industrial applications, are given: control of a mechanically flexible structure; a chemical process; and a nuclear power plant. Process Modelling for Control uses mathematics of an intermediate level convenient to researchers with an interest in real applications and to practising control engineers interested in control theory. It will enable working control engineers to improve their methods and will provide academics and graduate students with an all-round view of recent results in modelling for control. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781852339180
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/1-84628-247-0
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c292018
_d292018