The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article propose un schéma de contrôle PID flou adaptatif robuste augmenté d'un contrôleur de supervision pour les systèmes inconnus. Dans ce schéma, un modèle flou généralisé est utilisé pour décrire une classe de systèmes inconnus. La stratégie de contrôle permet à chaque partie de la loi de contrôle, c'est-à-dire un contrôleur de supervision, un compensateur et un contrôleur PID flou adaptatif, d'être conçue progressivement selon différentes directives. Le contrôleur de supervision dans la boucle externe vise à améliorer la robustesse du système face à des perturbations supplémentaires, à la variation des paramètres du système et à la dérive des paramètres dans la loi d'adaptation. Par ailleurs, un H∞ Une méthode de conception de contrôle utilisant la fonction floue de Lyapunov est présentée pour la conception des gains de contrôle initiaux qui garantissent les performances transitoires au début du contrôle en boucle fermée, ce qui est généralement négligé dans de nombreux systèmes de contrôle adaptatif. Cette conception des gains de contrôle initiaux est une stratégie de recherche composée appelée approche d'inégalité matricielle linéaire conditionnelle (CLMI) avec IROA (algorithme optimal aléatoire amélioré), elle conduit à des conceptions moins complexes qu'une méthode LMI standard par fonction de Lyapunov floue. Des études numériques du contrôle de suivi d'un système à pendule inversé incertain démontrent l'efficacité de la stratégie de contrôle. À partir des résultats de cette simulation, le modèle flou généralisé réduit effectivement le nombre de règles du modèle flou TS.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
Jiing-Dong HWANG, Zhi-Ren TSAI, "Adaptive Tracker Design with Identifier for Pendulum System by Conditional LMI Method and IROA" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 9, pp. 2266-2274, September 2009, doi: 10.1587/transfun.E92.A.2266.
Abstract: This paper proposes a robust adaptive fuzzy PID control scheme augmented with a supervisory controller for unknown systems. In this scheme, a generalized fuzzy model is used to describe a class of unknown systems. The control strategy allows each part of the control law, i.e., a supervisory controller, a compensator, and an adaptive fuzzy PID controller, to be designed incrementally according to different guidelines. The supervisory controller in the outer loop aims at enhancing system robustness in the face of extra disturbances, variation in system parameters, and parameter drift in the adaptation law. Furthermore, an H∞ control design method using the fuzzy Lyapunov function is presented for the design of the initial control gains that guarantees transient performance at the start of closed-loop control, which is generally overlooked in many adaptive control systems. This design of the initial control gains is a compound search strategy called conditional linear matrix inequality (CLMI) approach with IROA (Improved random optimal algorithm), it leads to less complex designs than a standard LMI method by fuzzy Lyapunov function. Numerical studies of the tracking control of an uncertain inverted pendulum system demonstrate the effectiveness of the control strategy. From results of this simulation, the generalized fuzzy model reduces the rule number of T-S fuzzy model indeed.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.2266/_p
Copier
@ARTICLE{e92-a_9_2266,
author={Jiing-Dong HWANG, Zhi-Ren TSAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Adaptive Tracker Design with Identifier for Pendulum System by Conditional LMI Method and IROA},
year={2009},
volume={E92-A},
number={9},
pages={2266-2274},
abstract={This paper proposes a robust adaptive fuzzy PID control scheme augmented with a supervisory controller for unknown systems. In this scheme, a generalized fuzzy model is used to describe a class of unknown systems. The control strategy allows each part of the control law, i.e., a supervisory controller, a compensator, and an adaptive fuzzy PID controller, to be designed incrementally according to different guidelines. The supervisory controller in the outer loop aims at enhancing system robustness in the face of extra disturbances, variation in system parameters, and parameter drift in the adaptation law. Furthermore, an H∞ control design method using the fuzzy Lyapunov function is presented for the design of the initial control gains that guarantees transient performance at the start of closed-loop control, which is generally overlooked in many adaptive control systems. This design of the initial control gains is a compound search strategy called conditional linear matrix inequality (CLMI) approach with IROA (Improved random optimal algorithm), it leads to less complex designs than a standard LMI method by fuzzy Lyapunov function. Numerical studies of the tracking control of an uncertain inverted pendulum system demonstrate the effectiveness of the control strategy. From results of this simulation, the generalized fuzzy model reduces the rule number of T-S fuzzy model indeed.},
keywords={},
doi={10.1587/transfun.E92.A.2266},
ISSN={1745-1337},
month={September},}
Copier
TY - JOUR
TI - Adaptive Tracker Design with Identifier for Pendulum System by Conditional LMI Method and IROA
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2266
EP - 2274
AU - Jiing-Dong HWANG
AU - Zhi-Ren TSAI
PY - 2009
DO - 10.1587/transfun.E92.A.2266
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2009
AB - This paper proposes a robust adaptive fuzzy PID control scheme augmented with a supervisory controller for unknown systems. In this scheme, a generalized fuzzy model is used to describe a class of unknown systems. The control strategy allows each part of the control law, i.e., a supervisory controller, a compensator, and an adaptive fuzzy PID controller, to be designed incrementally according to different guidelines. The supervisory controller in the outer loop aims at enhancing system robustness in the face of extra disturbances, variation in system parameters, and parameter drift in the adaptation law. Furthermore, an H∞ control design method using the fuzzy Lyapunov function is presented for the design of the initial control gains that guarantees transient performance at the start of closed-loop control, which is generally overlooked in many adaptive control systems. This design of the initial control gains is a compound search strategy called conditional linear matrix inequality (CLMI) approach with IROA (Improved random optimal algorithm), it leads to less complex designs than a standard LMI method by fuzzy Lyapunov function. Numerical studies of the tracking control of an uncertain inverted pendulum system demonstrate the effectiveness of the control strategy. From results of this simulation, the generalized fuzzy model reduces the rule number of T-S fuzzy model indeed.
ER -