Documents disponibles écrits par cet auteur

Nonlinear MPC using an identified LPV model / Zuhua Xu in Industrial & engineering chemistry research, Vol. 48 N° 6 (Mars 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 6 (Mars 2009) . - pp. 3043–3051 Titre : Nonlinear MPC using an identified LPV model Type de document : texte imprimé Auteurs : Zuhua Xu, Auteur ; Jun Zhao, Auteur ; Jixin Qian, Auteur Année de publication : 2009 Article en page(s) : pp. 3043–3051 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Nonlinear  model  predictive  control  Linear  parameter-varying  model  Nonlinear  model  predictive  control Résumé : A method of nonlinear model predictive control based on an identified LPV model is proposed. In process identification, a linear parameter varying (LPV) model approach is used. First, typical working-points are selected and linear models are identified using data sets at various working-points; then the LPV model is identified by interpolating the linear models using total data that include transition test data. Further, nonlinear model predictive control based on the LPV model is proposed. The control action is computed via a multistep linearization method of nonlinear optimization problem. The method uses low cost tests and can reach higher control performance than linear MPC. Simulation studies are used to verify the effectiveness of the method. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801057q [article] Nonlinear MPC using an identified LPV model [texte imprimé] / Zuhua Xu, Auteur ; Jun Zhao, Auteur ; Jixin Qian, Auteur . - 2009 . - pp. 3043–3051. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 6 (Mars 2009) . - pp. 3043–3051 Mots-clés : Nonlinear  model  predictive  control  Linear  parameter-varying  model  Nonlinear  model  predictive  control Résumé : A method of nonlinear model predictive control based on an identified LPV model is proposed. In process identification, a linear parameter varying (LPV) model approach is used. First, typical working-points are selected and linear models are identified using data sets at various working-points; then the LPV model is identified by interpolating the linear models using total data that include transition test data. Further, nonlinear model predictive control based on the LPV model is proposed. The control action is computed via a multistep linearization method of nonlinear optimization problem. The method uses low cost tests and can reach higher control performance than linear MPC. Simulation studies are used to verify the effectiveness of the method. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801057q

Robust iterative learning control with quadratic performance index / Zuhua Xu in Industrial & engineering chemistry research, Vol. 51 N° 2 (Janvier 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 2 (Janvier 2012) . - pp. 872-881 Titre : Robust iterative learning control with quadratic performance index Type de document : texte imprimé Auteurs : Zuhua Xu, Auteur ; Jun Zhao, Auteur ; Yi Yang, Auteur Année de publication : 2012 Article en page(s) : pp. 872-881 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Learning Résumé : In this paper, a robust iterative learning control (ILC) designed through a linear matrix inequality (LMI) approach is proposed first, based on the worst-case performance index with ellipsoidal uncertainty and polytopic uncertainty, respectively. Since the design based on worst-case performance index is too conservative, a novel ILC design based on nominal performance index is further proposed, and its robust convergence properties are proven. The latter can give better performance when the nominal model is dose to the true process. Simulations have demonstrated the effectiveness and excellent performance of the proposed methods. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25476420 [article] Robust iterative learning control with quadratic performance index [texte imprimé] / Zuhua Xu, Auteur ; Jun Zhao, Auteur ; Yi Yang, Auteur . - 2012 . - pp. 872-881. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 2 (Janvier 2012) . - pp. 872-881 Mots-clés : Learning Résumé : In this paper, a robust iterative learning control (ILC) designed through a linear matrix inequality (LMI) approach is proposed first, based on the worst-case performance index with ellipsoidal uncertainty and polytopic uncertainty, respectively. Since the design based on worst-case performance index is too conservative, a novel ILC design based on nominal performance index is further proposed, and its robust convergence properties are proven. The latter can give better performance when the nominal model is dose to the true process. Simulations have demonstrated the effectiveness and excellent performance of the proposed methods. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25476420