Hybrid Recurrent Laguerre-Orthogonal-Polynomial NN Control System Applied in V-Belt Continuously Variable Transmission System Using Particle Swarm OptimizationReportar como inadecuado




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Mathematical Problems in Engineering - Volume 2015 2015, Article ID 106707, 17 pages -

Research ArticleDepartment of Electrical Engineering, National United University, No. 1, Lienda, Kung-Jing Li, Miaoli City, Miaoli County 36003, Taiwan

Received 18 May 2014; Accepted 1 September 2014

Academic Editor: Kang Li

Copyright © 2015 Chih-Hong Lin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Because the V-belt continuously variable transmission CVT system driven by permanent magnet synchronous motor PMSM has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming procedure. In order to overcome difficulties for design of the linear controllers, the hybrid recurrent Laguerre-orthogonal-polynomial neural network NN control system which has online learning ability to respond to the system’s nonlinear and time-varying behaviors is proposed to control PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Laguerre-orthogonal-polynomial NN control system consists of an inspector control, a recurrent Laguerre-orthogonal-polynomial NN control with adaptive law, and a recouped control with estimated law. Moreover, the adaptive law of online parameters in the recurrent Laguerre-orthogonal-polynomial NN is derived using the Lyapunov stability theorem. Furthermore, the optimal learning rate of the parameters by means of modified particle swarm optimization PSO is proposed to achieve fast convergence. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.





Autor: Chih-Hong Lin

Fuente: https://www.hindawi.com/



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