Predictive Hybrid Energy Management for DC Microgrids: Adaptive Fuzzy Sliding Mode Control with Augmented Deep Q-Learning

This paper addresses the voltage regulation problem for DC microgrids modeled as nonlinear dynamical systems subject to parametric uncertainties and external disturbances. A data-driven predictive hybrid control scheme is developed, combining a nonlinear sliding mode law that guarantees finite-time current convergence, an adaptive fuzzy universal approximator that compensates for unknown residual dynamics and mitigates chattering, and a recursive predictor built online via forgetting-factor recursive least squares. Real-time gain optimization is achieved through the minimization of a quadratic predictive performance index. A composite Lyapunov analysis rigorously establishes uniform ultimate boundedness of the low level Adaptive Fuzzy Sliding Mode Control (AFSMC) inner loop, assuming bounded reference currents provided by the DQL agent and characterizes the convergence residual set of the tracking error. Comparative simulations against conventional fuzzy logic and a standard (non augmented) Deep Q-Learning baseline with fixed gain SMC corroborate the theoretical guarantees, demonstrating superior voltage regulation, reduced battery deep discharges, and improved load management.