Aperiodically Intermittent Event-Triggered Optimal Average Consensus for Nonlinear Multi-Agent Systems

This article is concerned with average consensus of multi-agent systems via intermittent event-triggered strategy. First, a novel intermittent event-triggered condition is designed and the corresponding piecewise differential inequality for the condition is established. Using the established inequality, several criteria on average consensus are obtained. Second, the optimality has been investigated based on average consensus. The optimal intermittent event-triggered strategy in the sense of Nash equilibrium and corresponding local Hamilton-Jacobi-Bellman equation are derived. Third, the adaptive dynamic programming algorithm for the optimal strategy and its neural network implementation with actor-critic architecture are also given. Finally, two numerical examples are presented to show the feasibility and effectiveness of our strategies.

Automated summary

This article focuses on achieving average consensus of multi-agent systems using intermittent event-triggered strategy. Firstly, a novel intermittent event-triggered condition is designed and the corresponding piecewise differential inequality is established. Several criteria for average consensus are obtained based on the established inequality. Secondly, the optimality of average consensus is investigated, and the optimal intermittent event-triggered strategy in terms of Nash equilibrium and the corresponding local Hamilton-Jacobi-Bellman equation are derived. Thirdly, an adaptive dynamic programming algorithm for the optimal strategy and its neural network implementation with actor-critic architecture are provided. Finally, two numerical examples are presented to demonstrate the feasibility and effectiveness of the proposed strategies.