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Adaptive control of two-axis motion control system using interval type-2 fuzzy neural network / Lin, Faa-Jeng in IEEE transactions on industrial electronics, Vol. 56 N°1 (Janvier 2009) 

Public ISBD [article]  in IEEE transactions on industrial electronics > Vol. 56 N°1 (Janvier 2009) . - pp. 178 - 193 Titre : Adaptive control of two-axis motion control system using interval type-2 fuzzy neural network Type de document : texte imprimé Auteurs : Lin, Faa-Jeng, Auteur ; Po-Huan Chou, Auteur Année de publication : 2009 Article en page(s) : pp. 178 - 193 Note générale : electronics Langues : Anglais (eng) Mots-clés : Lyapunov  methods;  adaptive  control;  angular  velocity  control;  digital  control;  digital  signal  processing  chips;  fuzzy  control;  machine  control;  neurocontrollers;  permanent  magnet  motors;  robust  control;  synchronous  motors Résumé : An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4675304&sortType%3Das [...] [article] Adaptive control of two-axis motion control system using interval type-2 fuzzy neural network [texte imprimé] / Lin, Faa-Jeng, Auteur ; Po-Huan Chou, Auteur . - 2009 . - pp. 178 - 193. electronics Langues : Anglais (eng) in IEEE transactions on industrial electronics > Vol. 56 N°1 (Janvier 2009) . - pp. 178 - 193 Mots-clés : Lyapunov  methods;  adaptive  control;  angular  velocity  control;  digital  control;  digital  signal  processing  chips;  fuzzy  control;  machine  control;  neurocontrollers;  permanent  magnet  motors;  robust  control;  synchronous  motors Résumé : An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4675304&sortType%3Das [...]