MPC constraint analysis / Seyi Akande in Industrial & engineering chemistry research, Vol. 48 N° 8 (Avril 2009)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 48 N° 8 (Avril 2009) . - pp. 3944–3954 Titre : MPC constraint analysis : Bayesian approach via a continuous-valued profit function Type de document : texte imprimé Auteurs : Seyi Akande, Auteur ; Biao Huang, Auteur ; Kwan Ho Lee, Auteur Année de publication : 2009 Article en page(s) : pp. 3944–3954 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Model predictive control Bayesian probabilistic framework Résumé : Model predictive control (MPC) is one of the most studied modern control technologies. Among the various subjects investigated, controller performance assessment of MPC has received considerable attention in recent time. Various approaches and algorithms have been proposed for the assessment of MPCs. In this work, we propose a novel approach to MPC constraint analysis by considering the economic objective function as a continuous-valued function within a Bayesian probabilistic framework. The analysis involves inference of the effect of a decision to adjust the limits of the constrained variables with regards to the achievable profits (decision evaluation) as well as inference of constraint limits that should be adjusted so as to achieve a specified profit value (decision making). The benefits of this approach include a more generalized definition of quality variables, the development of a more rigorous formulation of the problem to address linear and quadratic objective functions and thereby obtaining closed form solutions, and maximum-likelihood location determination of the quality variables in the decision making process. The approach is illustrated with the use of simulations and a pilot-scale experiment. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8011566 [article] MPC constraint analysis : Bayesian approach via a continuous-valued profit function [texte imprimé] / Seyi Akande, Auteur ; Biao Huang, Auteur ; Kwan Ho Lee, Auteur . - 2009 . - pp. 3944–3954. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 8 (Avril 2009) . - pp. 3944–3954 Mots-clés : Model predictive control Bayesian probabilistic framework Résumé : Model predictive control (MPC) is one of the most studied modern control technologies. Among the various subjects investigated, controller performance assessment of MPC has received considerable attention in recent time. Various approaches and algorithms have been proposed for the assessment of MPCs. In this work, we propose a novel approach to MPC constraint analysis by considering the economic objective function as a continuous-valued function within a Bayesian probabilistic framework. The analysis involves inference of the effect of a decision to adjust the limits of the constrained variables with regards to the achievable profits (decision evaluation) as well as inference of constraint limits that should be adjusted so as to achieve a specified profit value (decision making). The benefits of this approach include a more generalized definition of quality variables, the development of a more rigorous formulation of the problem to address linear and quadratic objective functions and thereby obtaining closed form solutions, and maximum-likelihood location determination of the quality variables in the decision making process. The approach is illustrated with the use of simulations and a pilot-scale experiment. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8011566

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