Documents disponibles écrits par cet auteur

Effective tuning method for fuzzy PID with internal model control / X. Duan in Industrial & engineering chemistry research, Vol. 47 n°21 (Novembre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 n°21 (Novembre 2008) . - p. 8317–8323 Titre : Effective tuning method for fuzzy PID with internal model control Type de document : texte imprimé Auteurs : X. Duan, Auteur ; Han-Xiong Li, Auteur Année de publication : 2008 Article en page(s) : p. 8317–8323 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : The  lyapunov  stability  theory Résumé : An internal model control (IMC) based tuning method is proposed to autotune the fuzzy proportional integral derivative (PID) controller in this paper. An analytical model of the fuzzy PID controller is first derived, which consists of a linear PID controller and a nonlinear compensation item. The nonlinear compensation item can be considered as a process disturbance, and then parameters of the fuzzy PID controller can be analytically determined on the basis of the IMC structure. The stability of the fuzzy PID control system is analyzed using the Lyapunov stability theory. The simulation results demonstrate the effectiveness of the proposed tuning method. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800485j [article] Effective tuning method for fuzzy PID with internal model control [texte imprimé] / X. Duan, Auteur ; Han-Xiong Li, Auteur . - 2008 . - p. 8317–8323. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°21 (Novembre 2008) . - p. 8317–8323 Mots-clés : The  lyapunov  stability  theory Résumé : An internal model control (IMC) based tuning method is proposed to autotune the fuzzy proportional integral derivative (PID) controller in this paper. An analytical model of the fuzzy PID controller is first derived, which consists of a linear PID controller and a nonlinear compensation item. The nonlinear compensation item can be considered as a process disturbance, and then parameters of the fuzzy PID controller can be analytically determined on the basis of the IMC structure. The stability of the fuzzy PID control system is analyzed using the Lyapunov stability theory. The simulation results demonstrate the effectiveness of the proposed tuning method. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800485j

Hammerstein modeling with structure identification for multi - input multi - output nonlinear industrial processes / Chenkun Qi in Industrial & engineering chemistry research, Vol. 50 N° 19 (Octobre 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 19 (Octobre 2011) . - pp. 11153-11169 Titre : Hammerstein modeling with structure identification for multi - input multi - output nonlinear industrial processes Type de document : texte imprimé Auteurs : Chenkun Qi, Auteur ; Han-Xiong Li, Auteur ; Xianchao Zhao, Auteur Année de publication : 2011 Article en page(s) : pp. 11153-11169 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Modeling Résumé : Hammerstein modeling with structure identification for multi-input multi-output (MIMO) nonlinear industrial processes is investigated in this study. The structure identification of the Hammerstein model is very challenging because the model terms are vectors, and some model terms are inputs of other model terms (i.e., model term coupling). An efficient model structure selection algorithm for the Hammerstein model is proposed with the multi-output locally regularized orthogonal least-squares (LROLS), A-optimality design, and a vector model term selection. To enhance the well-posedness of the regressors, estimation robustness, and model adequacy, the A-optimality criterion is integrated into the model error reduction criterion in the multi-output LROLS. To handle the vector model term coupling problem, a vector model term selection rule is synthesized into the multi-output LROLS. After the model structure is determined, to improve the robustness of the parameter estimation, the regularized least-squares method with the singular value decomposition (RLS-SVD) is used. The simple or sparse Hammerstein model structure can be determined from the noisy process data. The structure identification algorithm only includes a few user-designed parameters which are easy to select. Therefore, the ability of automatic construction of the Hammerstein model is enhanced. Three application examples are used to illustrate the effectiveness of the proposed modeling approach, including the simple model structure, the satisfactory modeling accuracy, the robustness of the algorithm to the noise, and the easy selection of user-designed parameters. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24573314 [article] Hammerstein modeling with structure identification for multi - input multi - output nonlinear industrial processes [texte imprimé] / Chenkun Qi, Auteur ; Han-Xiong Li, Auteur ; Xianchao Zhao, Auteur . - 2011 . - pp. 11153-11169. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 19 (Octobre 2011) . - pp. 11153-11169 Mots-clés : Modeling Résumé : Hammerstein modeling with structure identification for multi-input multi-output (MIMO) nonlinear industrial processes is investigated in this study. The structure identification of the Hammerstein model is very challenging because the model terms are vectors, and some model terms are inputs of other model terms (i.e., model term coupling). An efficient model structure selection algorithm for the Hammerstein model is proposed with the multi-output locally regularized orthogonal least-squares (LROLS), A-optimality design, and a vector model term selection. To enhance the well-posedness of the regressors, estimation robustness, and model adequacy, the A-optimality criterion is integrated into the model error reduction criterion in the multi-output LROLS. To handle the vector model term coupling problem, a vector model term selection rule is synthesized into the multi-output LROLS. After the model structure is determined, to improve the robustness of the parameter estimation, the regularized least-squares method with the singular value decomposition (RLS-SVD) is used. The simple or sparse Hammerstein model structure can be determined from the noisy process data. The structure identification algorithm only includes a few user-designed parameters which are easy to select. Therefore, the ability of automatic construction of the Hammerstein model is enhanced. Three application examples are used to illustrate the effectiveness of the proposed modeling approach, including the simple model structure, the satisfactory modeling accuracy, the robustness of the algorithm to the noise, and the easy selection of user-designed parameters. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24573314

Incremental modeling of nonlinear distributed parameter processes via spatiotemporal kernel series expansion / Han-Xiong Li 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. 3052–3058 Titre : Incremental modeling of nonlinear distributed parameter processes via spatiotemporal kernel series expansion Type de document : texte imprimé Auteurs : Han-Xiong Li, Auteur ; Chenkun Qi, Auteur Année de publication : 2009 Article en page(s) : pp. 3052–3058 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Incremental  modeling  Nonlinear  distributed  parameter  systems  Spatiotemporal  Volterra  kernels  Time-space  separation Résumé : In this article, an incremental modeling approach is proposed to model nonlinear distributed parameter systems, with the help of the newly constructed spatiotemporal Volterra kernels. The complex spatiotemporal process is first decomposed into a series of spatiotemporal kernels, upon which the time−space separation can be further conducted with the spatial Karhunen−Loève and temporal Laguerre basis function expansions. These two decompositions can gradually separate the nonlinear time/space coupled dynamics. Finally, the kernels in the spatiotemporal model are estimated from the experimental data incrementally, which can easily achieve satisfactory modeling performance. Simulations of two transport−reaction processes demonstrate the effectiveness of the proposed modeling approach. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801184a [article] Incremental modeling of nonlinear distributed parameter processes via spatiotemporal kernel series expansion [texte imprimé] / Han-Xiong Li, Auteur ; Chenkun Qi, Auteur . - 2009 . - pp. 3052–3058. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 6 (Mars 2009) . - pp. 3052–3058 Mots-clés : Incremental  modeling  Nonlinear  distributed  parameter  systems  Spatiotemporal  Volterra  kernels  Time-space  separation Résumé : In this article, an incremental modeling approach is proposed to model nonlinear distributed parameter systems, with the help of the newly constructed spatiotemporal Volterra kernels. The complex spatiotemporal process is first decomposed into a series of spatiotemporal kernels, upon which the time−space separation can be further conducted with the spatial Karhunen−Loève and temporal Laguerre basis function expansions. These two decompositions can gradually separate the nonlinear time/space coupled dynamics. Finally, the kernels in the spatiotemporal model are estimated from the experimental data incrementally, which can easily achieve satisfactory modeling performance. Simulations of two transport−reaction processes demonstrate the effectiveness of the proposed modeling approach. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801184a

A Karhunen-Loève decomposition-based Wiener modeling approach for nonlinear distributed parameter processes / Chenkun Qi in Industrial & engineering chemistry research, Vol. 47 n°12 (Juin 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 n°12 (Juin 2008) . - p. 4184–4192 Titre : A Karhunen-Loève decomposition-based Wiener modeling approach for nonlinear distributed parameter processes Type de document : texte imprimé Auteurs : Chenkun Qi, Auteur ; Han-Xiong Li, Auteur Année de publication : 2008 Article en page(s) : p. 4184–4192 Note générale : Bibliogr. p. 4191-4192 Langues : Anglais (eng) Mots-clés : Spatio-temporal  modeling  problem;  Wiener  modeling;  Karhunen−Loève  decomposition Résumé : The spatio-temporal modeling problem from the input and output measurements for distributed parameter processes under unknown circumstances is investigated. The traditional Wiener modeling is extended to nonlinear distributed parameter systems with the help of the Karhunen−Loève (KL) decomposition. The input is a finite-dimensional temporal variable, whereas the spatio-temporal output of the system is measured at a finite number of spatial locations. First, the measured output is used to construct a finite dimensional approximation of the system output which is expanded in terms of KL spatial basis functions. Subsequently, the temporal coefficients are used to identify a Wiener model. The identification algorithm is based on the least-squares estimation and the instrumental variables method. The simulations for parabolic and hyperbolic systems are presented to show the effectiveness of this spatio-temporal modeling method. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0710869 [article] A Karhunen-Loève decomposition-based Wiener modeling approach for nonlinear distributed parameter processes [texte imprimé] / Chenkun Qi, Auteur ; Han-Xiong Li, Auteur . - 2008 . - p. 4184–4192. Bibliogr. p. 4191-4192 Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°12 (Juin 2008) . - p. 4184–4192 Mots-clés : Spatio-temporal  modeling  problem;  Wiener  modeling;  Karhunen−Loève  decomposition Résumé : The spatio-temporal modeling problem from the input and output measurements for distributed parameter processes under unknown circumstances is investigated. The traditional Wiener modeling is extended to nonlinear distributed parameter systems with the help of the Karhunen−Loève (KL) decomposition. The input is a finite-dimensional temporal variable, whereas the spatio-temporal output of the system is measured at a finite number of spatial locations. First, the measured output is used to construct a finite dimensional approximation of the system output which is expanded in terms of KL spatial basis functions. Subsequently, the temporal coefficients are used to identify a Wiener model. The identification algorithm is based on the least-squares estimation and the instrumental variables method. The simulations for parabolic and hyperbolic systems are presented to show the effectiveness of this spatio-temporal modeling method. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0710869