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]  inIndustrial & 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

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