Documents disponibles écrits par cet auteur

Integrated design and control under uncertainty / XinJiang Lu in Industrial & engineering chemistry research, Vol. 49 N° 3 (Fevrier 2010)

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 3 (Fevrier 2010) . - pp. 1312–1324 Titre : Integrated design and control under uncertainty : a fuzzy modeling approach Type de document : texte imprimé Auteurs : XinJiang Lu, Auteur ; Han-Xiong, Li, Auteur ; Ji-An Duan, Auteur Année de publication : 2010 Article en page(s) : pp. 1312–1324 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Integrated--Design--Control--Uncertainty--uzzy  Modeling--Approach Résumé : A novel integration of design and control is proposed for the nonlinear process under uncertainty. The fuzzy modeling method is first employed to approximate the process, upon which fuzzy control rules are developed to achieve the stability, robustness and feasibility. Then, the steady-state economic design and the control system design are integrated into a unified objective function, which can guarantee the desirable economic and dynamic performances. Finally, the proposed method is compared with the traditional sequential method and an existing integration method on controlling the temperature profile of a nonlinear curing process. The comparison demonstrates that the proposed method will have the better performances than the other two methods. ISSN : 0888-5885 [article] Integrated design and control under uncertainty : a fuzzy modeling approach [texte imprimé] / XinJiang Lu, Auteur ; Han-Xiong, Li, Auteur ; Ji-An Duan, Auteur . - 2010 . - pp. 1312–1324. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 3 (Fevrier 2010) . - pp. 1312–1324 Mots-clés : Integrated--Design--Control--Uncertainty--uzzy  Modeling--Approach Résumé : A novel integration of design and control is proposed for the nonlinear process under uncertainty. The fuzzy modeling method is first employed to approximate the process, upon which fuzzy control rules are developed to achieve the stability, robustness and feasibility. Then, the steady-state economic design and the control system design are integrated into a unified objective function, which can guarantee the desirable economic and dynamic performances. Finally, the proposed method is compared with the traditional sequential method and an existing integration method on controlling the temperature profile of a nonlinear curing process. The comparison demonstrates that the proposed method will have the better performances than the other two methods. ISSN : 0888-5885

Kernel-Based Spatiotemporal Multimodeling for Nonlinear Distributed Parameter Industrial Processes / Chenkun Qi in Industrial & engineering chemistry research, Vol. 51 N° 40 (Octobre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 40 (Octobre 2012) . - pp. 13205–13218 Titre : Kernel-Based Spatiotemporal Multimodeling for Nonlinear Distributed Parameter Industrial Processes Type de document : texte imprimé Auteurs : Chenkun Qi, Auteur ; Han-Xiong, Li, Auteur ; Shaoyuan Li, Auteur Année de publication : 2012 Article en page(s) : pp. 13205–13218 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Industrial  processes Résumé : Many industrial processes are nonlinear distributed parameter systems (DPS) that have significant spatiotemporal dynamics. Due to different production and working conditions, they often need to work at a large operating range with multiple working points. However, direct global modeling and persistently exciting experiment in a large working region are very costly in many cases. The complex spatiotemporal coupling and infinite-dimensional nature make the problem more difficult. In this study, a kernel-based spatiotemporal multimodeling approach is proposed for the nonlinear DPS with multiple working points. To obtain a reasonable operating space division, an iterative approach is proposed where the operating space division and local modeling are performed iteratively. The working range of the current local model will help to determine the next operating point required for modeling. Utilizing the potential of each local model, the number of regions can be reduced. In the local modeling, the Karhunen–Loève method is used for the space/time separation and dimension reduction, and after that unknown parameters of kernels are estimated. Due to consideration of time-scale properties in the dimension reduction, the modeling approach is particularly suitable for dissipative PDEs, particularly of parabolic type. The multimodeling and space/time separation techniques can largely reduce the complexity of global nonlinear spatiotemporal modeling. Finally, to guarantee a smooth transition between local spatiotemporal models, a scheduling integration method is used to provide a global spatiotemporal model. To design scheduling functions, a two-stage training method is proposed to reduce the design complexity. Compared with direct global modeling, the exciting experiment and modeling for each local region become easier. Compared with one local modeling, the multimodel integration will improve modeling accuracy. The effectiveness of the proposed modeling approach is verified by simulations. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie301593u [article] Kernel-Based Spatiotemporal Multimodeling for Nonlinear Distributed Parameter Industrial Processes [texte imprimé] / Chenkun Qi, Auteur ; Han-Xiong, Li, Auteur ; Shaoyuan Li, Auteur . - 2012 . - pp. 13205–13218. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 40 (Octobre 2012) . - pp. 13205–13218 Mots-clés : Industrial  processes Résumé : Many industrial processes are nonlinear distributed parameter systems (DPS) that have significant spatiotemporal dynamics. Due to different production and working conditions, they often need to work at a large operating range with multiple working points. However, direct global modeling and persistently exciting experiment in a large working region are very costly in many cases. The complex spatiotemporal coupling and infinite-dimensional nature make the problem more difficult. In this study, a kernel-based spatiotemporal multimodeling approach is proposed for the nonlinear DPS with multiple working points. To obtain a reasonable operating space division, an iterative approach is proposed where the operating space division and local modeling are performed iteratively. The working range of the current local model will help to determine the next operating point required for modeling. Utilizing the potential of each local model, the number of regions can be reduced. In the local modeling, the Karhunen–Loève method is used for the space/time separation and dimension reduction, and after that unknown parameters of kernels are estimated. Due to consideration of time-scale properties in the dimension reduction, the modeling approach is particularly suitable for dissipative PDEs, particularly of parabolic type. The multimodeling and space/time separation techniques can largely reduce the complexity of global nonlinear spatiotemporal modeling. Finally, to guarantee a smooth transition between local spatiotemporal models, a scheduling integration method is used to provide a global spatiotemporal model. To design scheduling functions, a two-stage training method is proposed to reduce the design complexity. Compared with direct global modeling, the exciting experiment and modeling for each local region become easier. Compared with one local modeling, the multimodel integration will improve modeling accuracy. The effectiveness of the proposed modeling approach is verified by simulations. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie301593u

Neuro-fuzzy dynamic-inversion-based adaptive control for robotic manipulators—discrete time case / Sun, Fuchun in IEEE transactions on industrial electronics, Vol. 54 N°3 (Juin 2007) 

Public ISBD [article]  in IEEE transactions on industrial electronics > Vol. 54 N°3 (Juin 2007) . - 1342-1351 p. Titre : Neuro-fuzzy dynamic-inversion-based adaptive control for robotic manipulators—discrete time case Titre original : Neuro- inversion dynamique brouillée a basé la commande adaptative pour le cas de temps discret de robots manipulateurs Type de document : texte imprimé Auteurs : Sun, Fuchun, Auteur ; Li, Li, Auteur ; Han-Xiong, Li ; Huaping, Liu, Auteur Article en page(s) : 1342-1351 p. Note générale : Electronique Langues : Anglais (eng) Mots-clés : Adaptive  control  Dynamic-inversion  Neuro-fuzzy  (NF)  systems  NF  variable  structure  Robotic  manipulators  Commande  adaptative  Dynamique-inversion  Systèmes  neuro--brouillés  Neuro-structure  variable  brouillée  Robots  manipulateurs Index. décimale : 621 Ingénierie mécanique en général. Technologie nucléaire. Ingénierie électrique. Machinerie Résumé : In this paper, we present a stable discrete-time adaptive tracking controller using a neuro-fuzzy (NF) dynamic-inversion for a robotic manipulator with its dynamics approximated by a dynamic T-S fuzzy model. The NF dynamic-inversion constructed by a dynamic NF (DNF) system is used to compensate for the robot inverse dynamics for a better tracking performance. By assigning the dynamics of the DNF system, the dynamic performance of a robot control system can be guaranteed at the initial control stage, which is very important for enhancing system stability and adaptive learning. The discrete-time adaptive control composed of the NF dynamic-inversion and NF variable structure control (NF-VSC) is developed to stabilize the closed-loop system and ensure the high-quality tracking. The NF-VSC enhances the stability of the controlled system and improves the system dynamic performance during the NF learning. The system stability and the convergence of tracking errors are guaranteed by the Lyapunov stability theory, and the learning algorithm for the DNF system is obtained thereby. An example is given to show the viability and effectiveness of the proposed control approach. En cet article, nous présentons à un temps discret stable le contrôleur de cheminement adaptatif employant une neuro- (N-F) inversion dynamique brouillée pour un robot manipulateur avec sa dynamique rapprochée par un modèle brouillé de SOLIDES TOTAUX dynamiques. L'inversion dynamique de N-F construite par un système dynamique de N-F (DNF) est employée pour compenser la dynamique inverse de robot pour une meilleure exécution de cheminement. En assignant la dynamique du système de DNF, l'exécution dynamique d'un système de commande de robot peut être garantie à l'étape initiale de commande, qui est très importante pour augmenter la stabilité de système et l'étude adaptative. La commande adaptative de temps discret composée de commande variable dynamique de N-F inversion et de structure de N-F (NF-VSC) est développée pour stabiliser le système de boucle bloquée et pour assurer le cheminement de haute qualité. Le NF-VSC augmente la stabilité du système commandé et améliore l'exécution dynamique de système pendant l'étude de N-F. La stabilité de système et la convergence des erreurs de cheminement sont garanties par la théorie de stabilité de Lyapunov, et l'algorithme de étude pour le système de DNF est obtenu de ce fait. Un exemple est donné pour montrer la viabilité et l'efficacité de l'approche proposée de commande. DEWEY : 621 ISSN : 0278-0046 RAMEAU : Systèmes adaptatifs En ligne : fcsun@tsinghua.edu.cn [article] Neuro-fuzzy dynamic-inversion-based adaptive control for robotic manipulators—discrete time case = Neuro- inversion dynamique brouillée a basé la commande adaptative pour le cas de temps discret de robots manipulateurs [texte imprimé] / Sun, Fuchun, Auteur ; Li, Li, Auteur ; Han-Xiong, Li ; Huaping, Liu, Auteur . - 1342-1351 p. Electronique Langues : Anglais (eng) in IEEE transactions on industrial electronics > Vol. 54 N°3 (Juin 2007) . - 1342-1351 p. Mots-clés : Adaptive  control  Dynamic-inversion  Neuro-fuzzy  (NF)  systems  NF  variable  structure  Robotic  manipulators  Commande  adaptative  Dynamique-inversion  Systèmes  neuro--brouillés  Neuro-structure  variable  brouillée  Robots  manipulateurs Index. décimale : 621 Ingénierie mécanique en général. Technologie nucléaire. Ingénierie électrique. Machinerie Résumé : In this paper, we present a stable discrete-time adaptive tracking controller using a neuro-fuzzy (NF) dynamic-inversion for a robotic manipulator with its dynamics approximated by a dynamic T-S fuzzy model. The NF dynamic-inversion constructed by a dynamic NF (DNF) system is used to compensate for the robot inverse dynamics for a better tracking performance. By assigning the dynamics of the DNF system, the dynamic performance of a robot control system can be guaranteed at the initial control stage, which is very important for enhancing system stability and adaptive learning. The discrete-time adaptive control composed of the NF dynamic-inversion and NF variable structure control (NF-VSC) is developed to stabilize the closed-loop system and ensure the high-quality tracking. The NF-VSC enhances the stability of the controlled system and improves the system dynamic performance during the NF learning. The system stability and the convergence of tracking errors are guaranteed by the Lyapunov stability theory, and the learning algorithm for the DNF system is obtained thereby. An example is given to show the viability and effectiveness of the proposed control approach. En cet article, nous présentons à un temps discret stable le contrôleur de cheminement adaptatif employant une neuro- (N-F) inversion dynamique brouillée pour un robot manipulateur avec sa dynamique rapprochée par un modèle brouillé de SOLIDES TOTAUX dynamiques. L'inversion dynamique de N-F construite par un système dynamique de N-F (DNF) est employée pour compenser la dynamique inverse de robot pour une meilleure exécution de cheminement. En assignant la dynamique du système de DNF, l'exécution dynamique d'un système de commande de robot peut être garantie à l'étape initiale de commande, qui est très importante pour augmenter la stabilité de système et l'étude adaptative. La commande adaptative de temps discret composée de commande variable dynamique de N-F inversion et de structure de N-F (NF-VSC) est développée pour stabiliser le système de boucle bloquée et pour assurer le cheminement de haute qualité. Le NF-VSC augmente la stabilité du système commandé et améliore l'exécution dynamique de système pendant l'étude de N-F. La stabilité de système et la convergence des erreurs de cheminement sont garanties par la théorie de stabilité de Lyapunov, et l'algorithme de étude pour le système de DNF est obtenu de ce fait. Un exemple est donné pour montrer la viabilité et l'efficacité de l'approche proposée de commande. DEWEY : 621 ISSN : 0278-0046 RAMEAU : Systèmes adaptatifs En ligne : fcsun@tsinghua.edu.cn

Probabilistic PCA - based spatiotemporal multimodeling for nonlinear distributed parameter processes / Chenkun Qi in Industrial & engineering chemistry research, Vol. 51 N° 19 (Mai 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 19 (Mai 2012) . - pp. 6811–6822 Titre : Probabilistic PCA - based spatiotemporal multimodeling for nonlinear distributed parameter processes Type de document : texte imprimé Auteurs : Chenkun Qi, Auteur ; Han-Xiong, Li, Auteur Année de publication : 2012 Article en page(s) : pp. 6811–6822 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Probabilistic Résumé : Many industrial processes are nonlinear distributed parameter systems (DPSs). Data-based spatiotemporal modeling is required for analysis and control when the first-principles model is unknown. Because a DPS is infinite-dimensional and time–space coupled, a low-order model is necessary for prediction and control in practice. For low-order modeling, traditional principal component analysis (PCA) is often used for dimension reduction and time–space separation. However, it is a linear method and leads to only one set of fixed spatial basis functions. Therefore, it might not be always effective for nonlinear systems. In this study, a spatiotemporal multimodeling approach is proposed for unknown nonlinear DPSs. First, multimodel decomposition is performed, where probabilistic PCA (PPCA) is used to obtain multiple sets of spatial basis functions from the experimental data by maximizing a likelihood function. Using these multiple sets of PCA spatial bases for time–space separation, the high-dimensionality spatiotemporal data can be reduced to multiple sets of low-dimensionality temporal series. Then, multiple low-order neural models can be easily established to model these local dynamics. Finally, the original spatiotemporal dynamics can be reconstructed by multimodel synthesis. Because the proposed spatiotemporal modeling approach involves a multimodeling mechanism, it can achieve better performance than the traditional PCA-based single-modeling for nonlinear DPSs, which is demonstrated by numerical simulations. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie202613t [article] Probabilistic PCA - based spatiotemporal multimodeling for nonlinear distributed parameter processes [texte imprimé] / Chenkun Qi, Auteur ; Han-Xiong, Li, Auteur . - 2012 . - pp. 6811–6822. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 19 (Mai 2012) . - pp. 6811–6822 Mots-clés : Probabilistic Résumé : Many industrial processes are nonlinear distributed parameter systems (DPSs). Data-based spatiotemporal modeling is required for analysis and control when the first-principles model is unknown. Because a DPS is infinite-dimensional and time–space coupled, a low-order model is necessary for prediction and control in practice. For low-order modeling, traditional principal component analysis (PCA) is often used for dimension reduction and time–space separation. However, it is a linear method and leads to only one set of fixed spatial basis functions. Therefore, it might not be always effective for nonlinear systems. In this study, a spatiotemporal multimodeling approach is proposed for unknown nonlinear DPSs. First, multimodel decomposition is performed, where probabilistic PCA (PPCA) is used to obtain multiple sets of spatial basis functions from the experimental data by maximizing a likelihood function. Using these multiple sets of PCA spatial bases for time–space separation, the high-dimensionality spatiotemporal data can be reduced to multiple sets of low-dimensionality temporal series. Then, multiple low-order neural models can be easily established to model these local dynamics. Finally, the original spatiotemporal dynamics can be reconstructed by multimodel synthesis. Because the proposed spatiotemporal modeling approach involves a multimodeling mechanism, it can achieve better performance than the traditional PCA-based single-modeling for nonlinear DPSs, which is demonstrated by numerical simulations. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie202613t

Robust optimal design with consideration of robust eigenvalue assignment / XinJiang Lu in Industrial & engineering chemistry research, Vol. 49 N° 7 (Avril 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 7 (Avril 2010) . - pp. 3306–3315 Titre : Robust optimal design with consideration of robust eigenvalue assignment Type de document : texte imprimé Auteurs : XinJiang Lu, Auteur ; Han-Xiong, Li, Auteur ; C. L. Philip Chen, Auteur Année de publication : 2010 Article en page(s) : pp. 3306–3315 Note générale : Industrial Chemistry Langues : Anglais (eng) Mots-clés : Robust  Consideration  Considbust  Eigenvalue  Assignment Résumé : In this paper, a novel robust design method is proposed for the process design under uncertainty. The key idea is to integrate the robust optimal design with the robust eigenvalue assignment to design the process with the desirable steady-state design performance and the satisfactory dynamic performance. The robust optimal design is developed to achieve the desirable steady-state design performance through minimizing the steady-state objective function and its variation caused by uncertainty. The robust eigenvalue assignment is proposed to maintain the system eigenvalues in the desirable domain and make these eigenvalues less sensitive to uncertainty so that the system has the satisfactory dynamic response. The particle swarm optimization is proposed to solve the nonconvex and nondifferential integration problem. Finally, a continuously stirred tank reactor (CSTR) example is applied to demonstrate the effectiveness of the proposed integration method. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901501x [article] Robust optimal design with consideration of robust eigenvalue assignment [texte imprimé] / XinJiang Lu, Auteur ; Han-Xiong, Li, Auteur ; C. L. Philip Chen, Auteur . - 2010 . - pp. 3306–3315. Industrial Chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 7 (Avril 2010) . - pp. 3306–3315 Mots-clés : Robust  Consideration  Considbust  Eigenvalue  Assignment Résumé : In this paper, a novel robust design method is proposed for the process design under uncertainty. The key idea is to integrate the robust optimal design with the robust eigenvalue assignment to design the process with the desirable steady-state design performance and the satisfactory dynamic performance. The robust optimal design is developed to achieve the desirable steady-state design performance through minimizing the steady-state objective function and its variation caused by uncertainty. The robust eigenvalue assignment is proposed to maintain the system eigenvalues in the desirable domain and make these eigenvalues less sensitive to uncertainty so that the system has the satisfactory dynamic response. The particle swarm optimization is proposed to solve the nonconvex and nondifferential integration problem. Finally, a continuously stirred tank reactor (CSTR) example is applied to demonstrate the effectiveness of the proposed integration method. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901501x