Optimized Sensor-based Speed Control of BLDC Motor using Tunicate Swarm Algorithm Tuned PID Controller for Electric Vehicle Applications
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Abstract
Brushless Direct Current (BLDC) motors are highly favoured in electric vehicle (EV) applications owing to their superior efficiency and high torque density compared to other motor types. The Proportional-Integral-Derivative (PID) controller is the most prevalent control algorithm used in these systems. This study proposes an optimized PID design to ensure reliable operation of BLDC motor drives. The optimization of the PID is carried out using the Tunicate Swarm Algorithm (TSA). The optimized PID values are then implemented in the closed-loop speed control of BLDC drives. A prototype system was developed to validate the proposed TSA-based method, demonstrating effective speed control of the BLDC motor using an FPGA with the tuned PID values. Hall effect sensors are employed to measure the motor's speed. The results confirm that the TSA-based optimization of PID values enhances the performance of BLDC motors, making them well-suited for EV applications.
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