Multiple model predictive control strategy for disturbance rejection / Matthew Kuure-Kinsey in Industrial & engineering chemistry research, Vol. 49 N° 17 (Septembre 1, 2010)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7983–7989 Titre : Multiple model predictive control strategy for disturbance rejection Type de document : texte imprimé Auteurs : Matthew Kuure-Kinsey, Auteur ; B. Wayne Bequette, Auteur Année de publication : 2010 Article en page(s) : pp 7983–7989 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Predictive control Disturbance rejection. Résumé : Classical model-based control strategies assume a single disturbance model. In practice, the type of disturbance is often unknown or can change with time or multiple different disturbance types can occur simultaneously. In this paper, a multiple model predictive control strategy is developed to handle different disturbances, including multiple disturbances occurring simultaneously. A detailed discussion of disturbance model bank generation, state estimation, and disturbance model weighting is provided, and an unconstrained multiple model predictive control solution is formulated. Simulation results demonstrate successful estimation and control of single and multiple simultaneous disturbances. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100093c [article] Multiple model predictive control strategy for disturbance rejection [texte imprimé] / Matthew Kuure-Kinsey, Auteur ; B. Wayne Bequette, Auteur . - 2010 . - pp 7983–7989. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7983–7989 Mots-clés : Predictive control Disturbance rejection. Résumé : Classical model-based control strategies assume a single disturbance model. In practice, the type of disturbance is often unknown or can change with time or multiple different disturbance types can occur simultaneously. In this paper, a multiple model predictive control strategy is developed to handle different disturbances, including multiple disturbances occurring simultaneously. A detailed discussion of disturbance model bank generation, state estimation, and disturbance model weighting is provided, and an unconstrained multiple model predictive control solution is formulated. Simulation results demonstrate successful estimation and control of single and multiple simultaneous disturbances. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100093c

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