Distributed Data-Driven Dynamic Event-Triggered Control of Power Buffers in DC Microgrids
Abstract
Direct current (DC) microgrids have attracted increasing attention due to their high efficiency and compatibility with renewable sources and energy storage systems. However, existing cooperative and communication-efficient control schemes for power buffers still rely heavily on accurate system models, which may limit performance under parameter variations and topology changes. This brief develops a distributed data-driven dynamic event-triggered control framework for power buffers in DC microgrids. Historical state-input trajectories are used to characterize the interconnected system and synthesize the controller directly, thereby avoiding explicit model identification. A dynamic event-triggered mechanism (DETM) is introduced to reduce unnecessary transmissions, and a model-free co-design of structured feedback gains and triggering weights is established via Lyapunov theory, the S-procedure, and linear matrix inequalities (LMIs). The design is centralized offline but distributed online: stacked trajectories are used only for LMI synthesis, while the implemented law follows the prescribed communication sparsity. Simulation results show that the proposed method achieves fast energy and voltage regulation under load disturbances while reducing communication burden compared with model-based and periodic communication strategies. Additional robustness discussions are included for representative parameter-perturbation and topology-change cases.