Adaptive Fuzzy‐Based Predictive Control of Buck–Boost Converters in DC Microgrids Interfaced With Constant Power Loads

ABSTRACT

The negative impedance behavior of constant power loads (CPLs) often destabilizes DC microgrids by reducing the damping effect. Additionally, non‐conventional disturbances can negatively affect DC–DC buck–boost converters in DC microgrids, causing severe voltage oscillations. To overcome these challenges, this paper proposes a generalized predictive controller (GPC) with an adaptive fuzzy‐based prediction horizon for a DC–DC buck–boost converter connected to a constant power load under disturbances. The adaptive prediction horizon improves control performance when facing non‐conventional disturbances. Furthermore, since predictive control depends on the nominal model of the system and real systems are subject to disturbances, a fixed‐time disturbance observer (FTDO) is integrated into the controller to identify and counteract these disturbances. To demonstrate the effectiveness of the proposed controller, a comprehensive comparison is conducted among the PI controller, the fixed‐horizon predictive controller, the self‐tuning adaptive predictive controller, and the fuzzy‐based adaptive predictive controller. The superiority of the fuzzy‐based approach is evaluated in terms of dynamic performance. Simulation results in MATLAB/Simulink confirm the performance improvement and effectiveness of the proposed control strategy. The practical effectiveness of the proposed control algorithm is further validated through processor‐in‐the‐loop (PIL) testing using an STM32 Nucleo‐G431RB board.