Adaptive fuzzy sliding mode control for smart hybrid microgrid energy management with PI-lowpass filter under uncertainties parameters

Abstract

This study presents an Adaptive Fuzzy Sliding Mode Control combined with a PI regulator (AFSMC-PI) for robust power management in hybrid microgrids with high renewable penetration. The proposed method integrates the robustness of sliding mode, the learning capability of fuzzy approximators, and the steady-state accuracy of a PI controller. The control scheme is modeled in MATLAB/Simulink (R2023a) for a hybrid microgrid comprising distributed generators, loads, a PWM IGBT inverter, and filtering components, which was tested on a 2 MW demand profile under uncertain conditions. The AFSMC-PI achieves precise load tracking with a mean error of 0.5, a maximum overshoot limited to 0.2%, and rapid transient settling within 10 seconds. Diesel generation remains smooth and ramp-limited, averaging 55 kW, while battery compensation is constrained within ±30 kW, maintaining SOC between 0.1 and 0.95. Fuzzy parameter estimates converge effectively, reducing reliance on switching and minimizing chattering. Furthermore, the controller ensures stable frequency and voltage regulation across varying operating conditions.