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PID-like neural network nonlinear adaptive control for uncertain multivariable motion control systems / Cong, S. in IEEE transactions on industrial electronics, Vol. 56 N° 10 (Octobre 2009) 

Public ISBD [article]  in IEEE transactions on industrial electronics > Vol. 56 N° 10 (Octobre 2009) . - pp. 3872 - 3879 Titre : PID-like neural network nonlinear adaptive control for uncertain multivariable motion control systems Type de document : texte imprimé Auteurs : Cong, S., Auteur ; Liang, Y., Auteur Article en page(s) : pp. 3872 - 3879 Note générale : Génie électrique Langues : Anglais (eng) Mots-clés : Neural  network  Nonlinear  adaptive  control  Proportional-integral-derivative  (PID)  Single-input/multi-output  (SIMO)  Uncertain  multivariable  system Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : A mix locally recurrent neural network was used to create a proportional-integral-derivative (PID)-like neural network nonlinear adaptive controller for uncertain multivariable single-input/multi-output system. It is composed of a neural network with no more than three neural nodes in hidden layer, and there are included an activation feedback and an output feedback, respectively, in a hidden layer. Such a special structure makes the exterior feature of the neural network controller able to become a P, PI, PD, or PID controller as needed. The closed-loop error between directly measured output and expected value of the system is chosen to be the input of the controller. Only a group of initial weights values, which can run the controlled closed-loop system stably, are required to be determined. The proposed controller can update weights of the neural network online according to errors caused by uncertain factors of system such as modeling error and external disturbance, based on stable learning rate. The resilient back-propagation algorithm with sign instead of the gradient is used to update the network weights. The basic ideas, techniques, and system stability proof were presented in detail. Finally, actual experiments both of single and double inverted pendulums were implemented, and the comparison of effectiveness between the proposed controller and the linear optimal regulator were given. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4812095 [article] PID-like neural network nonlinear adaptive control for uncertain multivariable motion control systems [texte imprimé] / Cong, S., Auteur ; Liang, Y., Auteur . - pp. 3872 - 3879. Génie électrique Langues : Anglais (eng) in IEEE transactions on industrial electronics > Vol. 56 N° 10 (Octobre 2009) . - pp. 3872 - 3879 Mots-clés : Neural  network  Nonlinear  adaptive  control  Proportional-integral-derivative  (PID)  Single-input/multi-output  (SIMO)  Uncertain  multivariable  system Index. décimale : 621.38 Dispositifs électroniques. Tubes à électrons. Photocellules. Accélérateurs de particules. Tubes à rayons X Résumé : A mix locally recurrent neural network was used to create a proportional-integral-derivative (PID)-like neural network nonlinear adaptive controller for uncertain multivariable single-input/multi-output system. It is composed of a neural network with no more than three neural nodes in hidden layer, and there are included an activation feedback and an output feedback, respectively, in a hidden layer. Such a special structure makes the exterior feature of the neural network controller able to become a P, PI, PD, or PID controller as needed. The closed-loop error between directly measured output and expected value of the system is chosen to be the input of the controller. Only a group of initial weights values, which can run the controlled closed-loop system stably, are required to be determined. The proposed controller can update weights of the neural network online according to errors caused by uncertain factors of system such as modeling error and external disturbance, based on stable learning rate. The resilient back-propagation algorithm with sign instead of the gradient is used to update the network weights. The basic ideas, techniques, and system stability proof were presented in detail. Finally, actual experiments both of single and double inverted pendulums were implemented, and the comparison of effectiveness between the proposed controller and the linear optimal regulator were given. DEWEY : 621.38 ISSN : 0278-0046 En ligne : http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4812095