Documents disponibles écrits par cet auteur

Model predictive control tuning methods / Jorge L. Garriga in Industrial & engineering chemistry research, Vol. 49 N° 8 (Avril 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 8 (Avril 2010) . - pp. 3505–3515 Titre : Model predictive control tuning methods : A review Type de document : texte imprimé Auteurs : Jorge L. Garriga, Auteur ; Masoud Soroush, Auteur Année de publication : 2010 Article en page(s) : pp. 3505–3515 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Control  tuning  methods Résumé : This paper provides a review of the available tuning guidelines for model predictive control, from theoretical and practical perspectives. It covers both popular dynamic matrix control and generalized predictive control implementations, along with the more general state-space representation of model predictive control and other more specialized types, such as max-plus-linear model predictive control. Additionally, a section on state estimation and Kalman filtering is included along with auto (self) tuning. Tuning methods covered range from equations derived from simulation/approximation of the process dynamics to bounds on the region of acceptable tuning parameter values. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900323c [article] Model predictive control tuning methods : A review [texte imprimé] / Jorge L. Garriga, Auteur ; Masoud Soroush, Auteur . - 2010 . - pp. 3505–3515. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 8 (Avril 2010) . - pp. 3505–3515 Mots-clés : Control  tuning  methods Résumé : This paper provides a review of the available tuning guidelines for model predictive control, from theoretical and practical perspectives. It covers both popular dynamic matrix control and generalized predictive control implementations, along with the more general state-space representation of model predictive control and other more specialized types, such as max-plus-linear model predictive control. Additionally, a section on state estimation and Kalman filtering is included along with auto (self) tuning. Tuning methods covered range from equations derived from simulation/approximation of the process dynamics to bounds on the region of acceptable tuning parameter values. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900323c

On the effects of tunable parameters of model predictive control on the locations of closed-loop eigenvalues / Jorge L. Garriga in Industrial & engineering chemistry research, Vol. 49 N° 17 (Septembre 1, 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7951–7956 Titre : On the effects of tunable parameters of model predictive control on the locations of closed-loop eigenvalues Type de document : texte imprimé Auteurs : Jorge L. Garriga, Auteur ; Masoud Soroush, Auteur Année de publication : 2010 Article en page(s) : pp 7951–7956 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Tunable  parameters  Predictive  control. Résumé : This paper presents an analytical study of the effects of model predictive control (MPC) tunable parameters on the closed-loop performance quantified in terms of the location(s) of closed-loop eigenvalue(s) of several common, single-input single-output, linear plants with inactive constraints. Symbolic manipulation capabilities of MATHEMATICA are used to obtain analytical expressions describing the dependence of closed-loop eigenvalues on the tunable parameters. This work is first to investigate how MPC tuning parameters affect the locations of the eigenvalues of the closed-loop system of a plant in the discrete-time setting. It provides theoretical basis/justification for several existing qualitative MPC tuning rules and proposes new tuning guidelines. For example, as the prediction horizon is increased while other tunable parameters remain constant, a subset of the closed-loop eigenvalues (poles) move toward the open-loop eigenvalues (poles) of the plant, if the plant is asymptotically stable. If a prediction horizon much longer than the reference-trajectory time constant is used, the value of the reference-trajectory time constant has little effect on the closed-loop performance. As the weights on the magnitude or the rate of change of the manipulated input are increased, the closed-loop eigenvalues move toward the open-loop eigenvalues. As the control horizon is increased from one, the dominant eigenvalue of the closed-loop system initially moves toward the origin and then away from the origin to a location that does not change with a further increase in the control horizon. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100030e [article] On the effects of tunable parameters of model predictive control on the locations of closed-loop eigenvalues [texte imprimé] / Jorge L. Garriga, Auteur ; Masoud Soroush, Auteur . - 2010 . - pp 7951–7956. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7951–7956 Mots-clés : Tunable  parameters  Predictive  control. Résumé : This paper presents an analytical study of the effects of model predictive control (MPC) tunable parameters on the closed-loop performance quantified in terms of the location(s) of closed-loop eigenvalue(s) of several common, single-input single-output, linear plants with inactive constraints. Symbolic manipulation capabilities of MATHEMATICA are used to obtain analytical expressions describing the dependence of closed-loop eigenvalues on the tunable parameters. This work is first to investigate how MPC tuning parameters affect the locations of the eigenvalues of the closed-loop system of a plant in the discrete-time setting. It provides theoretical basis/justification for several existing qualitative MPC tuning rules and proposes new tuning guidelines. For example, as the prediction horizon is increased while other tunable parameters remain constant, a subset of the closed-loop eigenvalues (poles) move toward the open-loop eigenvalues (poles) of the plant, if the plant is asymptotically stable. If a prediction horizon much longer than the reference-trajectory time constant is used, the value of the reference-trajectory time constant has little effect on the closed-loop performance. As the weights on the magnitude or the rate of change of the manipulated input are increased, the closed-loop eigenvalues move toward the open-loop eigenvalues. As the control horizon is increased from one, the dominant eigenvalue of the closed-loop system initially moves toward the origin and then away from the origin to a location that does not change with a further increase in the control horizon. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100030e