•  
  •  
 

Corresponding Author

Ameer L. Saleh

Subject Area

Electrical Engineering

Article Type

Original Study

Abstract

Switched Reluctance Motors (SRMs) have been considered a high-performance and environmentally friendly solution for Electric Vehicle (EV) applications owing to their simpler construction, robust design, and high efficiency. However, it suffers from high torque ripple and acoustic noise due to its highly nonlinear magnetic characteristics and double-salient structure.  This paper introduces a nonlinear torque control based on the Direct Instantaneous Torque Control (DITC) scheme and a Wavelet Neural Network (WNN)  to achieve better dynamic response and mitigate torque ripple.  The proposed WNN is employed as a nonlinear torque controller, inserted between the torque error and the hysteresis torque controller within the DITC loop, acting as a torque error compensator that dynamically regulates the torque error to generate an optimal input for the hysteresis comparator. This intelligent mechanism noticeably suppresses the torque ripple, enhances steady-state torque smoothness, and maintains a fast dynamic response. Furthermore, the Aquila Optimizer (AO) algorithm has been employed to train the WNN network's parameters, which leads to suppressing torque ripple, boosting efficiency, and decreasing torque error. Simulation results show that the proposed WNN-enhanced DITC exhibits outstanding performance compared with the conventional DITC approach in terms of torque ripple minimization, response time, and torque profile smoothness, making it convenient for high-performance SRM drive applications.

Keywords

Aquila Optimizer (AO), Direct Instantaneous Torque Control, Switched Reluctance Motors, Wavelet Neural Network (WNN).

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Download

Share

COinS