A Novel Reduced-Ripple Torque Control Technique for Electric Vehicle Switched Reluctance Motors Using an Adaptive Fuzzy Neural Network

This paper presents a novel torque control technique for SRMs using an adaptive fuzzy neural network (AFNN); the proposed technique allows for a precise current profiling with a superior ability of torque ripple reduction, even compared to torque sharing function (TSF) strategies. The proposed AFNN involves a four-layer neural network based on fuzzy logic; the torque reference and torque error are the inputs of FNN; the output is the reference commanded current; the parameters are optimized by a multi-objective Aquila Optimizer (AO) algorithm with only 16 parameters trained at a single operating point, generalizing across the full speed range. Moreover, the switching angles of the tested 12/8 SRM prototype are optimized; thereby contributing to improved torque quality and overall operational performance. Furthermore, the experimental and simulation results are achieved compared to TSF strategies; they reveal the superiority of proposed torque control technique over the entire speed range and different loading levels; it shows significant torque ripple reductions compared to TSF strategies, the proposed AFNN technique shows 52%, 83%, 89% reduction ratios of torque ripple for low-speed and light loads, medium-speed and heavy loads, and high-speed and light loads, respectively. Besides, it has fast dynamics, a simple structure, and a good generalization ability for several industrial applications, including EVs.