Adaptive dynamic programming-based decentralized event-triggered control of large-scale nonlinear systems

This paper proposes an adaptive dynamic programming (ADP)-based decentralized event-triggered control strategy for large-scale nonlinear systems with event-triggered scheme to efficiently reduce communication cost and computational burden. Under the event-triggered mechanism, an local neural networks (NNs)-based observer is introduced to identify the mismatched interconnections and the estimation error is guaranteed to be uniformly ultimately bounded (UUB). Then, a decentralized triggering condition related to the approximation error of interconnections is designed to reduce the local controller updates with guaranteed overall stabilization of large-scale systems. By virtue of critic-only structure, the local optimal control policy can be approximated via aperiodic tuning rule using ADP. In addition, the closed-loop large-scale system ensures to be asymptotically stable with adaptive triggering threshold according to Lyapunov method. Finally, the simulation results justify the theoretical analysis and illustrate the effectiveness of the proposed event-triggered control (ETC) strategy.