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

Public ISBD [article]  inIEEE 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 : Faa-Jeng Lin, 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 digital signal processing chips  fuzzy machine neurocontrollers  permanent magnet motors  robust 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] / Faa-Jeng Lin, 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 digital signal processing chips  fuzzy machine neurocontrollers  permanent magnet motors  robust 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 [...]

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