Optimum Speed Control of Permanent Magnet Synchronous Motor using Artificial Neural Network-Based Field-Oriented Controller

This paper presents a comprehensive study focused on improving the performance of electric vehicles (EVs) through the integration of a hybrid energy source system and the employment of an Artificial Neural Network (ANN)-based control technique known as Field-Oriented Control (FOC) for precise operation. A hybrid system integrating batteries and ultra-capacitors is suggested for enhancing the battery life. To control the two sources, sliding-based control is used in this paper, the focus is on employing Field Oriented Control (FOC), a widely used control technique in EVs, to regulate the Permanent Magnet Synchronous Motor (PMSM) which gets input from the Voltage Source inverter. The Voltage Source Inverter (VSI) is provided with control signals from Space Vector Pulse Width Modulation (SVPWM). Notably, the traditional Proportional-Integral-Derivative (PID) control typically used in FOC is replaced with an ANN-based approach for generating control gains. The utilization of ANN offers advantages in adaptability and robustness over conventional PID control. The study conducts a comparative analysis between the traditional PID-controlled FOC and the ANN-trained FOC to evaluate the efficacy of the proposed approach. MATLAB/Simulink is the simulation platform for system development and performance evaluation. The simulation results validate the efficiency of the ANN-based FOC which exhibits superior performance in terms of dynamic response, efficiency, and robustness compared to PID control, showcasing its potential for enhancing EV performance.