Torque Ripple Minimization for Switched Reluctance Motor Drives Based on Harris Hawks–Radial Basis Function Approximation

Switched reluctance motor drives are becoming attractive for electric vehicle propulsion systems due to their simple and cheap construction. However, their operation is degraded by torque ripples due to the salient nature of the stator and rotor poles. There are several methods of mitigating torque ripples in switched reluctance motors (SRMs). Apart from changing the geometrical design of the motor, the less costly technique involves the development of an adaptive switching strategy. By selecting suitable turn-on and turn-off angles, torque ripples in SRMs can be significantly reduced. This work combines the benefits of Harris Hawks Optimization (HHO) and Radial Basis Functions (RBFs) to search and estimate optimal switching angles. An objective function is developed under constraints and the HHO is utilized to perform search stages for optimal switching angles that guarantee minimal torque ripples at every speed and current operating point. In this work, instead of storing the θ o n , θ o f f values in a look-up table, the values are passed on to an RBF model to learn the nonlinear relationship between the columns of data from the HHO and hence transform them into high-dimensional outputs. The values are used to train an enhanced neural network (NN) in an adaptive switching strategy to address the nonlinear magnetic characteristics of the SRM. The proposed method is implemented on a current chopping control-based SRM 8/6, 600 V model. Percentage torque ripples are used as the key performance index of the proposed method. A fuzzy logic switching angle compensation strategy is implemented in numerical simulations to validate the performance of the HHO-RBF method.