Decentralized Event-Triggered Adaptive Dynamic Programming Approach for Electric–Gas Coupling Energy Systems With Partially Unknown Dynamics

In this article, a novel online adaptive control scheme is developed for the optimal control issues of integrated electric–gas systems with partially unknown dynamics, by combining the decentralized event-triggered mechanism and adaptive dynamic programming techniques. Initially, the complex electric–gas coupling network is modeled in the state-space form. By virtue of neural networks (NNs), the NN-based identifier and the critic NN are designed to approximate the unknown drift dynamic and the optimal value function in an online fashion, respectively. Subsequently, the decentralized event-triggered control strategies are devised under the identifier–critic framework. Moreover, a novel decentralized event-triggered scheme with the dead-zone operation is proposed, which updates the controller and actuator signals only when the triggering condition is violated. As such, the computation complexity and the waste of communication resources can be significantly reduced. On the foundation of the Lyapunov theory, the uniform ultimate boundedness stability of the closed-loop control system and the exclusion of the Zeno behavior are proven. Finally, the effectiveness of the developed algorithm is verified through two numerical examples.