Mixed Modified Recurring Rogers-Szego Polynomials Neural Network Control with Mended Grey Wolf Optimization Applied in SIM Expelling System

Mathematics

Der-Fa Chen¹  Shih-Cheng Li¹  Yi-Cheng Shih¹  Jung-Chu Ting¹  Chin-Tung Chen² 

[1]Department of Industrial Education and Technology, National Changhua University of Education, Changhua 500, Taiwan

[2]Graduate School of Vocational and Technological Education, National Yunlin University of Science and Technology, Yunlin 640, Taiwan

关键词: six-phase induction motor;    Rogers-Szego polynomials neural network;    grey wolf optimization;    Lyapunov stability theorem;   

DOI  :  10.3390/math8050754

来源: DOAJ

【 摘 要 】

Due to a good ability of learning for nonlinear uncertainties, a mixed modified recurring Rogers-Szego polynomials neural network (MMRRSPNN) control with mended grey wolf optimization (MGWO) by using two linear adjusted factors is proposed to the six-phase induction motor (SIM) expelling continuously variable transmission (CVT) organized system for acquiring better control performance. The control system can execute MRRSPNN control with a fitted learning rule, and repay control with an evaluated rule. In the light of the Lyapunov stability theorem, the fitted learning rule in the MRRSPNN control can be derived, and the evaluated rule of the repay control can be originated. Besides, the MGWO by using two linear adjusted factors yields two changeable learning rates for two parameters to find two ideal values and to speed-up convergence of weights. Experimental results in comparisons with some control systems are demonstrated to confirm that the proposed control system can achieve better control performance.

【 授权许可】

Unknown