Blend recurrent Gegenbauer orthogonal polynomials neural network control of a SynRM servo-drive CVT system using amended artificial bee colony optimization

A blend recurrent Gegenbauer orthogonal polynomials neural network (OPNN) control system is proposed to reduce effect of nonlinear time-varying uncertainty for a continuously variable transmission (CVT) system driven by synchronous reluctance motor (SynRM). The blend recurrent Gegenbauer OPNN control system consists of a censor control, a recurrent Gegenbauer OPNN control with adaptive law based on Lyapunov stability and a recompensed control. Furthermore, the using amended artificial bee colony optimization (ABCO) is proposed for regulating two varied learning rates of two parameters in the recurrent Gegenbauer OPNN to obtain better dynamic response and faster convergence. Finally, comparative studies demonstrated by experimental results are illustrated to verify the effectiveness of the proposed control scheme.