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

Public ISBD [article]  inIndustrial & 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 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 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

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