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A neural-network-identifier and fuzzy-controller-based algorithm for dynamic decoupling control of permanent-magnet spherical motor / Changliang, Xia in IEEE transactions on industrial electronics, Vol. 57 N° 8 (Août 2010) 

Public ISBD [article]  in IEEE transactions on industrial electronics > Vol. 57 N° 8 (Août 2010) . - pp. 2868 - 2878 Titre : A neural-network-identifier and fuzzy-controller-based algorithm for dynamic decoupling control of permanent-magnet spherical motor Type de document : texte imprimé Auteurs : Changliang, Xia, Auteur ; Chen, Guo, Auteur ; Tingna, Shi, Auteur Année de publication : 2011 Article en page(s) : pp. 2868 - 2878 Note générale : Génie électrique Langues : Anglais (eng) Mots-clés : Dynamic  decoupling  control  algorithm  Fuzzy  controller  (FC)  Neural  network  identifier  (NNI)  Online  identification  Permanent-magnet  (PM)  spherical  motor  (PMSM) Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : This paper proposes a dynamic model of permanent-magnet spherical motor (PMSM) and puts forward a dynamic decoupling control algorithm of the motor, using fuzzy controllers (FCs) and a neural network identifier (NNI). PMSM is a multivariable nonlinear system with strong interaxis couplings. A computed torque method structure is applied to PMSM. There are such uncertainties as estimated errors of the model and external perturbations, which may influence the precision of the control system. A back-propagation algorithm with additional momentum term and self-adaptive learning rate applied to feed-forward neural network can approach nonlinear functions with a learning rate adjusted online, which helps to improve training speed. In this paper, an NNI is applied to identify the uncertainties online. An adaptive-neuro-fuzzy-inference-system-based FC is applied, which has self-adaptive ability and strong robustness. Simulation results preliminarily validate that the algorithm proposed in this paper can eliminate the influences of interaxis nonlinear couplings effectively to actualize dynamic decoupling control. Furthermore, the static and dynamic performances of the control system have been improved greatly with strong robustness to uncertainties. A hypothetical microprocessor system is proposed, and simple experiments of spinning operation are carried out as a foundation for further study. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5332328 [article] A neural-network-identifier and fuzzy-controller-based algorithm for dynamic decoupling control of permanent-magnet spherical motor [texte imprimé] / Changliang, Xia, Auteur ; Chen, Guo, Auteur ; Tingna, Shi, Auteur . - 2011 . - pp. 2868 - 2878. Génie électrique Langues : Anglais (eng) in IEEE transactions on industrial electronics > Vol. 57 N° 8 (Août 2010) . - pp. 2868 - 2878 Mots-clés : Dynamic  decoupling  control  algorithm  Fuzzy  controller  (FC)  Neural  network  identifier  (NNI)  Online  identification  Permanent-magnet  (PM)  spherical  motor  (PMSM) Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : This paper proposes a dynamic model of permanent-magnet spherical motor (PMSM) and puts forward a dynamic decoupling control algorithm of the motor, using fuzzy controllers (FCs) and a neural network identifier (NNI). PMSM is a multivariable nonlinear system with strong interaxis couplings. A computed torque method structure is applied to PMSM. There are such uncertainties as estimated errors of the model and external perturbations, which may influence the precision of the control system. A back-propagation algorithm with additional momentum term and self-adaptive learning rate applied to feed-forward neural network can approach nonlinear functions with a learning rate adjusted online, which helps to improve training speed. In this paper, an NNI is applied to identify the uncertainties online. An adaptive-neuro-fuzzy-inference-system-based FC is applied, which has self-adaptive ability and strong robustness. Simulation results preliminarily validate that the algorithm proposed in this paper can eliminate the influences of interaxis nonlinear couplings effectively to actualize dynamic decoupling control. Furthermore, the static and dynamic performances of the control system have been improved greatly with strong robustness to uncertainties. A hypothetical microprocessor system is proposed, and simple experiments of spinning operation are carried out as a foundation for further study. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5332328