Control-Oriented Optimization of a BLDC Drive Powered by PEM Fuel Cell for Hydrogen Electric Vehicles Using PSO-Tuned ADRC

This paper addresses the speed regulation of a Brushless DC (BLDC) motor powered by a fuel cell, where the overall dynamics of the system are complex, nonlinear, and partially unknown due to the inverter-motor–fuel-cell interactions. Traditional control approaches like Proportional–Integral (PI) controllers often underperform under such uncertainties. To overcome this challenge, we propose an Active Disturbance Rejection Control (ADRC) framework whose core parameters are automatically tuned using Particle Swarm Optimization (PSO). The optimization process relies on experimental speed data acquired from a real-time dSPACE MicroLabBox platform interfaced with MATLAB/Simulink R2015a. Comparative simulations demonstrate that the proposed PSO-ADRC significantly outperforms the classical PI in terms of dynamic response, tracking accuracy, and robustness to disturbances. This approach offers a scalable, model-agnostic solution for high-performance control of uncertain electromechanical systems.