Distributed Adaptive Optimal Secondary Control of DC Microgrids: A Fully Actuated System Approach Using an Identifier-Actor-Critic Architecture

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This paper proposes a distributed adaptive optimal secondary control strategy for DC microgrids with nonlinear and unknown dynamics by incorporating the fully actuated system (FAS) approach. First, a large-signal model of the microgrid is reformulated into an equivalent FAS representation. To circumvent a direct analytical solution of the Hamilton-Jacobi-Bellman equation, an identifier-actor-critic architecture is developed based on the FAS representation. Within this framework, a neural network-based identifier is utilized to estimate and compensate for lumped unknown system dynamics. Meanwhile, critic and actor networks are employed to evaluate control performance and generate control actions, respectively, thereby achieving voltage regulation and proportional current sharing. Subsequently, the stability of the closed-loop system is rigorously proven. Finally, simulation results are presented to validate the effectiveness of the proposed control strategy.

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