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Improving the performance of generalized predictive control for nonlinear processes / Ma'moun Abu-Ayyad in Industrial & engineering chemistry research, Vol. 49 N° 10 (Mai 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 10 (Mai 2010) . - pp. 4809–4816 Titre : Improving the performance of generalized predictive control for nonlinear processes Type de document : texte imprimé Auteurs : Ma'moun Abu-Ayyad, Auteur ; Rickey Dubay, Auteur Année de publication : 2010 Article en page(s) : pp. 4809–4816 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Nonlinear  Processes Résumé : This paper presents a unique method for improving the performance of the generalized predictive control (GPC) algorithm for controlling nonlinear systems which can be extended to other forms of predictive controllers. This method is termed adaptive generalized predictive control (AGPC) which uses a multidimensional workspace of the nonlinear plant to recalculate the controller parameters every sampling instant. This results in a more accurate process prediction and improved closed-loop performance over the original GPC algorithm. The AGPC controller was tested in simulation, and its control performance was compared to GPC on several nonlinear plants with different degrees of nonlinearity. Practical testing and comparisons were performed on a steel cylinder temperature control system. Simulation and experimental results show that the adaptive generalized predictive controller provided improved closed-loop performance over GPC. The formulation of the multidimensional workspace can be readily applied to other advanced control strategies making the methodology generic. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100133k [article] Improving the performance of generalized predictive control for nonlinear processes [texte imprimé] / Ma'moun Abu-Ayyad, Auteur ; Rickey Dubay, Auteur . - 2010 . - pp. 4809–4816. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 10 (Mai 2010) . - pp. 4809–4816 Mots-clés : Nonlinear  Processes Résumé : This paper presents a unique method for improving the performance of the generalized predictive control (GPC) algorithm for controlling nonlinear systems which can be extended to other forms of predictive controllers. This method is termed adaptive generalized predictive control (AGPC) which uses a multidimensional workspace of the nonlinear plant to recalculate the controller parameters every sampling instant. This results in a more accurate process prediction and improved closed-loop performance over the original GPC algorithm. The AGPC controller was tested in simulation, and its control performance was compared to GPC on several nonlinear plants with different degrees of nonlinearity. Practical testing and comparisons were performed on a steel cylinder temperature control system. Simulation and experimental results show that the adaptive generalized predictive controller provided improved closed-loop performance over GPC. The formulation of the multidimensional workspace can be readily applied to other advanced control strategies making the methodology generic. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100133k

A Wiener neural network-based identification and adaptive generalized predictive control for nonlinear SISO systems / Jinzhu Peng in Industrial & engineering chemistry research, Vol. 50 N° 12 (Juin 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 12 (Juin 2011) . - pp. 7388-7397 Titre : A Wiener neural network-based identification and adaptive generalized predictive control for nonlinear SISO systems Type de document : texte imprimé Auteurs : Jinzhu Peng, Auteur ; Rickey Dubay, Auteur ; Jose Mauricio Hernandez, Auteur Année de publication : 2011 Article en page(s) : pp. 7388-7397 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : SISO  system  Predictive  control  Neural  network Résumé : In this study, a Wiener-type neural network (WNN) is derived for identification and control of single-input and single-output (SISO) nonlinear systems. The nonlinear system is identified by the WNN, which consists of a linear dynamic block in cascade with a nonlinear static gain. The Lipschitz criteria for model order determination and back propagation for the adjustment of weights in the network are presented. Using the parameters of the Wiener model, the analytical expressions used in the controller, generalized predictive control (GPC) is modified every time step, to handle the nonlinear dynamics of the controlled variable. Finally, the proposed WNN-based GPC algorithm is tested in simulation on several nonlinear plants with different degrees of nonlinearity. Simulation results show that WNN identification approach has better accuracy, in comparison to other neural network identifiers. The WNN-based GPC has better control performance, in comparison to standard GPC. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24239054 [article] A Wiener neural network-based identification and adaptive generalized predictive control for nonlinear SISO systems [texte imprimé] / Jinzhu Peng, Auteur ; Rickey Dubay, Auteur ; Jose Mauricio Hernandez, Auteur . - 2011 . - pp. 7388-7397. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 12 (Juin 2011) . - pp. 7388-7397 Mots-clés : SISO  system  Predictive  control  Neural  network Résumé : In this study, a Wiener-type neural network (WNN) is derived for identification and control of single-input and single-output (SISO) nonlinear systems. The nonlinear system is identified by the WNN, which consists of a linear dynamic block in cascade with a nonlinear static gain. The Lipschitz criteria for model order determination and back propagation for the adjustment of weights in the network are presented. Using the parameters of the Wiener model, the analytical expressions used in the controller, generalized predictive control (GPC) is modified every time step, to handle the nonlinear dynamics of the controlled variable. Finally, the proposed WNN-based GPC algorithm is tested in simulation on several nonlinear plants with different degrees of nonlinearity. Simulation results show that WNN identification approach has better accuracy, in comparison to other neural network identifiers. The WNN-based GPC has better control performance, in comparison to standard GPC. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24239054