Distributed Event-Triggered Model Predictive Control for Multi-Agent Systems via the Communication-Censored ADMM

Abstract

This paper considers a distributed model predictive control problem for multi-agent systems. Without an event-triggered mechanism, the conventional alternating direction method of multipliers (ADMM), which requires frequent exchange of information between agents, can give an optimal solution to the problem after several tens of iterations. With an event-triggered mechanism, the communication-censored ADMM (COCA), which allows restricted information exchanges, can give an optimal solution. This paper applies the COCA to the problem above with the event-triggered mechanism, especially in a leader-follower setting. The numerical examples for single and double integrators illustrate the effectiveness of the COCA for distributed model predictive control.