Hybrid Metaheuristic Optimization for Real-Time Tuning of Extended PID Controllers for DC–DC Buck Converter

This paper proposes a hybrid metaheuristic optimization approach for real-time tuning of an Extended Incremental Discrete Proportional–Integral–Derivative (EID–PID) controller applied to a DC–DC buck converter. The proposed method integrates Adaptive Particle Swarm Optimization with Local Refinement (APSO–L) and the Whale Optimization Algorithm (WOA) to achieve an effective balance between global exploration and local exploitation. The EID–PID structure enhances conventional PID control through dynamic filtering and anti-windup mechanisms, providing superior robustness and adaptability for nonlinear, time-varying power converter systems. The optimization process is executed in MATLAB, which communicates optimal parameters in real time to an STM32F429I microcontroller implementing the controller law. Simulation studies using MATLAB/Simulink models and experimental validation confirm the algorithm’s fast convergence, high precision, and high robustness through input voltage and load variations. The results demonstrate that the proposed APSO–L–WOA-based EID–PID control scheme offers a powerful and scalable solution for real-time embedded control in power electronics applications.