Event-triggered Multi-agent Optimal Regulation Using Adaptive Dynamic Programming

Document Type

Conference Proceeding

Date of Original Version



This paper develops an event-triggered multi-agent control method based on adaptive dynamic programming (ADP) techniques. Different from the traditional ADP-based multi-agent control with fixed sampling period, our method designs an adaptive controller only based on the efficiently reduced samples. The sampling instants are decided by an adaptive triggering condition to guarantee the stability of the event-triggered learning process. The theoretical analysis of the proposed method is also provided in this paper. It is proved that the designed event-triggered ADP controller can make all the agents synchronize to the leader's dynamics with reduced sampled data, and also reach Nash equilibrium at the same time. Therefore, the proposed method can save the computational resources in the learning process. Finally, the simulation results verify the theoretical analysis and also demonstrate the performance of the developed method.

Publication Title, e.g., Journal

Proceedings of the International Joint Conference on Neural Networks