Decentralized Dynamic Event-Triggered Online Adaptive Control For Unknown Large-Scale Energy Interconnected Systems

This article introduces an innovative online decentralized dynamic event-triggered control framework developed for a class of nonlinear large-scale energy interconnected systems whose system dynamics are unknown and influenced by interconnections. Firstly, we utilize a neural network-based identifier to reconstruct these unknown internal dynamics. Secondly, we develop an adaptive critic network to learn the approximate optimal control policies under a dynamic event-triggered framework. In this mechanism, the control policies for each subsystem are designed independently, and the update of the control policies is completed asynchronously. In other words, each decentralized event trigger condition and the controller solely depends on the local state data of their corresponding subsystems. And there is no need to frequently obtain information from adjacent subsystems, thus circumventing the need to transfer state information between subsystems through communication networks. Finally, the effectiveness and feasibility of the developed dynamic event-triggered control method are confirmed through a numerical example.