BLDC Motor Parameter Identification using Metaheuristic Optimization: Salp Swarm Algorithm
Résumé
The precise identification of Brushless DC (BLDC) motor parameters is important for accurate modeling, control, and sensorless control in various applications. This paper presents an identification approach based on optimization algorithms to estimate key motor parameters, including stator resistance, inductance, torque constant, moment of inertia, friction coefficient. The methodology involves performing tests while acquiring real-time voltage and current measurements and the rotor speed. A metaheuristic optimization algorithm, namely the Salp Swarm Algorithm (SSA), is employed to minimize an objective function that quantifies the error between the simulated and measured motor responses. The Simulink model of the BLDC motor is used to evaluate different parameter sets iteratively. The results demonstrate the effectiveness of the proposed approach in accurately estimating motor parameters.