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

Because 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.

A Six-Phase CRIM Driving CVT using Blend Modified Recurrent Gegenbauer OPNN Control

“…Because of their high computational complexity, these NNs can effectively identify and control complex dynamic systems. Therefore, to reduce the computational complexity and to enhance the identification ability of high-order nonlinear systems, the modified recurrent Gegenbauer OPNN [2] , which offers more advantages than the Gegenbauer OPNN [24]- [26] including better performance, higher accuracy and dynamic robustness, was proposed for controlling a six-phase CRIM driving CVT system with nonlinear and time-varying characteristics in this paper.…”

“…In this study, because CVT systems [2], [9]- [16], [42]- [45] possess nonlinear dynamics and uncertainties, the blend modified recurrent Gegenbauer OPNN control system using an amended ABC optimization [2] is proposed for controlling a six-phase CRIM driving CVT system. The blend modified recurrent Gegenbauer OPNN control system using an amended ABC optimization [2] has a good generalization ability and a fast learning capability.…”

“…Therefore, to further improve the performance of ABC optimization, some modified ABC optimizations inspired by using the Grenade explosion method [38], [39] and the Bisection explosion method [40], [41] were proposed. However, since the ABC optimization method possess a slow convergence, the amended ABC optimization [2] with an inertia weight is proposed to search for two optimal learning rates of the weights in the modified recurrent Gegenbauer OPNN. This is done in order to obtain faster convergence and a better learning rate in this study.…”

“…This characteristic makes them convenient in high power and high current applications, such as ship propulsion, aerospace applications, and electric/hybrid vehicles (EV) [1]. A six-phase copper rotor induction motor (CRIM) [2] is chosen as an alternating current (AC) motor driving a continuously variable transmission (CVT) system because it provides lower torque ripple, higher efficiency, and better reliability for its size when compared to single-phase and three-phase aluminum rotor IMs [3]- [8]. However, a simplified model of a CVT system, which is driven by a six-phase CRIM, has not been presented.…”

“…A CVT system [2], [9]- [16] may be manipulated at a specific speed while changing the pulleys' radii for achieving torque multiplication, acceleration, and speed. This working profile provides the research motivation for the CVT dynamics and nonlinear control algorithms.…”