Intelligent Speed Regulation of Electric Vehicles on Unpaved Road via LSTM-Driven RNN Models

This paper presents an adaptive speed control system for BLDC motors using PID and LSTM-based optimization. The system integrates real-time Hall sensor feedback to regulate motor speed by computing the speed error and adjusting the inverter switching pattern. The LSTM model optimizes PID gains dynamically, ensuring stable operation despite torque variations and reference speed changes. Simulation results demonstrate improved speed tracking, reduced overshoot, and enhanced stability, making this approach efficient for intelligent motor control applications.