Modelling and control of six‐phase induction motor servo‐driven continuously variable transmission system using blend modified recurrent Gegenbauer orthogonal polynomial neural network control system and amended artificial bee colony optimization

Abstract

AbstractBecause the nonlinear and time‐varying characteristics of the continuously variable transmission system operated using a six‐phase copper rotor induction motor are unknown, improving the control performance of the linear control design is time‐consuming. To capture the nonlinear and dynamic behaviour of the six‐phase copper rotor induction motor servo‐driven continuously variable transmission system, a blend modified recurrent Gegenbauer orthogonal polynomial neural network (NN) control system, which has the online learning capability to return to the nonlinear time‐varying system, was developed. The blend modified recurrent Gegenbauer orthogonal polynomial NN control system can perform overseer control, modified recurrent Gegenbauer orthogonal polynomial NN control, and recompensed control. Moreover, the adaptation law of online parameters in the modified recurrent Gegenbauer orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of amended artificial bee colony optimization yielded two optimal learning rates for the parameters, which helped improve convergence. Finally, comparison of the experimental results of the present study with those of previous studies demonstrated the high control performance of the proposed control scheme. Copyright © 2016 John Wiley & Sons, Ltd.