BLDC Motor Speed Control System for Drone Applications Using Tuned PID Control by Particle Swarm Optimization (PSO) - Jekli Boy Nainggolan; Beni Satria; Ahmad Dani (Sean Institute, 2026)
This study addresses the challenge of optimal speed control for Brushless DC (BLDC) motors in drone applications, where precise and responsive control is critical for stability and maneuverability. While the Proportional-Integral-Derivative (PID) controller is standard, manual tuning for dynamic drone operations is often suboptimal. This research proposes and investigates the implementation of a PID controller whose parameters are automatically tuned using the Particle Swarm Optimization (PSO) algorithm. The primary objective is to design a superior control system that enhances transient response and robustness against disturbances common in drone flight. The methodology employs a simulation-based experimental design using MATLAB/Simulink. The PSO algorithm optimizes the PID parameters (Kp, Ki, Kd) by minimizing the Integral Time Absolute Error (ITAE) as the fitness function. The performance of the PSO-tuned PID is then compared against a conventionally tuned (Ziegler-Nichols) PID controller under scenarios including step response and load disturbance. The findings demonstrate that the PSO-PID controller significantly outperforms the conventional PID, achieving a 73% reduction in overshoot (from 14.2% to 3.8%), a 25% faster settling time, and a 28.9% quicker recovery from load disturbances. Statistical t-test analysis confirms the significant superiority of the PSO-optimized system (p < 0.001). The implications of this research are substantial, offering an efficient offline tuning method that can be deployed on flight controllers to enhance drone stability, performance, and operational reliability without requiring additional real-time computational power, thereby advancing control system design for unmanned aerial vehicles.