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Design of a robust nonlinear model predictive controller based on a hybrid model and comparison to other approaches / Rosendo Diaz-Mendoza in Industrial & engineering chemistry research, Vol. 49 N° 22 (Novembre 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 22 (Novembre 2010) . - pp. 11482–11490 Titre : Design of a robust nonlinear model predictive controller based on a hybrid model and comparison to other approaches Type de document : texte imprimé Auteurs : Rosendo Diaz-Mendoza, Auteur ; Hector Budman, Auteur Année de publication : 2011 Article en page(s) : pp. 11482–11490 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Hybrid  Robust  Nonlinear Résumé : A methodology to systematically design a model-based nonlinear model predictive controller is presented. The controller is referred to as hybrid since it uses the first-principles model to calculate the value of the controlled variables along the prediction and control horizons whereas uses the empirical model to ensure a terminal condition that accounts for model errors. The empirical Volterra series model was split into nominal and uncertain parts that were then used to formulate a structured singular value based robustness test. The proposed hybrid controller was compared against a robust empirical that uses solely an empirical model and to a nonrobust first principles model based nonlinear model predictive controller. To show the benefits of considering robustness in the controller formulation, extensive simulation studies were conducted that considered mismatch between the real process parameters and the model parameters. It is shown that in some case the performance of the hybrid controller can be superior to the purely empirical and to the first principles based controllers. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie1016283 [article] Design of a robust nonlinear model predictive controller based on a hybrid model and comparison to other approaches [texte imprimé] / Rosendo Diaz-Mendoza, Auteur ; Hector Budman, Auteur . - 2011 . - pp. 11482–11490. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 22 (Novembre 2010) . - pp. 11482–11490 Mots-clés : Hybrid  Robust  Nonlinear Résumé : A methodology to systematically design a model-based nonlinear model predictive controller is presented. The controller is referred to as hybrid since it uses the first-principles model to calculate the value of the controlled variables along the prediction and control horizons whereas uses the empirical model to ensure a terminal condition that accounts for model errors. The empirical Volterra series model was split into nominal and uncertain parts that were then used to formulate a structured singular value based robustness test. The proposed hybrid controller was compared against a robust empirical that uses solely an empirical model and to a nonrobust first principles model based nonlinear model predictive controller. To show the benefits of considering robustness in the controller formulation, extensive simulation studies were conducted that considered mismatch between the real process parameters and the model parameters. It is shown that in some case the performance of the hybrid controller can be superior to the purely empirical and to the first principles based controllers. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie1016283

Methodology for designing and comparing robust linear versus gain-scheduled model predictive controllers / Rosendo Diaz-Mendoza in Industrial & engineering chemistry research, Vol. 48 N° 22 (Novembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 22 (Novembre 2009) . - pp. 9985–9998 Titre : Methodology for designing and comparing robust linear versus gain-scheduled model predictive controllers Type de document : texte imprimé Auteurs : Rosendo Diaz-Mendoza, Auteur ; Jianying Gao, Auteur ; Hector Budman, Auteur Année de publication : 2010 Article en page(s) : pp. 9985–9998 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Gain-Scheduled  Model  Predictive  Control  Nonlinear  state-affine  model  Stability Résumé : A methodology is proposed to design a robust Gain-Scheduled Model Predictive Control (MPC) strategy and to quantify the relative advantages of this controller versus a Linear MPC strategy. For the purpose of analysis and controller design, the process is represented by a nonlinear state-affine model identified from input−output data. This model can be split in linear and nonlinear terms where the linear part is used for controller design and the nonlinear part is accounted for as model uncertainty. Then, robust stability and robust performance tests are formulated based on linear matrix inequalities where the manipulated variables weight of the controllers is tuned to maximize performance. The uncertainty bounds used for the robustness tests are obtained in an iterative fashion by using the frequency response of the manipulated variable with respect to the feedback error. The control strategy performance is quantified by the ratio between the error norm and the disturbance norm. Finally, a case study involving a multiple-input−multiple-output bioreactor is presented. The study is able to predict for which range of operation the Gain-Scheduling MPC surpasses the performance of the Linear MPC. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900309s [article] Methodology for designing and comparing robust linear versus gain-scheduled model predictive controllers [texte imprimé] / Rosendo Diaz-Mendoza, Auteur ; Jianying Gao, Auteur ; Hector Budman, Auteur . - 2010 . - pp. 9985–9998. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 22 (Novembre 2009) . - pp. 9985–9998 Mots-clés : Gain-Scheduled  Model  Predictive  Control  Nonlinear  state-affine  model  Stability Résumé : A methodology is proposed to design a robust Gain-Scheduled Model Predictive Control (MPC) strategy and to quantify the relative advantages of this controller versus a Linear MPC strategy. For the purpose of analysis and controller design, the process is represented by a nonlinear state-affine model identified from input−output data. This model can be split in linear and nonlinear terms where the linear part is used for controller design and the nonlinear part is accounted for as model uncertainty. Then, robust stability and robust performance tests are formulated based on linear matrix inequalities where the manipulated variables weight of the controllers is tuned to maximize performance. The uncertainty bounds used for the robustness tests are obtained in an iterative fashion by using the frequency response of the manipulated variable with respect to the feedback error. The control strategy performance is quantified by the ratio between the error norm and the disturbance norm. Finally, a case study involving a multiple-input−multiple-output bioreactor is presented. The study is able to predict for which range of operation the Gain-Scheduling MPC surpasses the performance of the Linear MPC. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900309s