Safety-Certified Hierarchical Control of DC Microgrids via Continuous Deep Reinforcement Learning and Covariance-Weighted Sliding Mode Synthesis

This paper presents a hierarchical safe control approach for DC microgrids, combining learning-based energy management with rigorous constraint enforcement. The main novelty is a covariance-aware coupling mechanism that simultaneously informs CBF constraint tightening, sliding-mode gain scheduling, and DRL state augmentation. A continuous Soft Actor-Critic agent generates smooth current references and a flexible load-shedding ratio. A predictive CBF quadratic program acts as a safety filter, using online confidence intervals from a recursive least-squares predictor. The filter ensures a sampled-data safety property with an inter-sample margin, applying to the actual uncertain microgrid via a robust-CBF term, provided the QP is feasible with zero slack and the true voltage stays within the confidence interval. The same covariance information serves as an online uncertainty metric for adaptive gain and weight scheduling. A composite Lyapunov–CBF theorem proves uniform ultimate boundedness and constraint satisfaction. Simulations under severe conditions show high-probability constraint satisfaction with negligible slack, while improving transient recovery and reducing battery stress. Comparisons with fuzzy logic, discrete DQL, and plain SAC isolate the CBF contribution, and ablation studies assess individual components. While robust MPC can also enforce constraints, the proposed method achieves comparable satisfaction with lower complexity. A clear distinction is maintained between theoretical guarantees and simulation observations.