Distributed event-based model predictive control for multi-agent systems under disturbances

In this paper, we propose an aperiodic formulation of Distributed Model Predictive Control for the cooperation of multi-agent systems under additive bounded disturbances. In the proposed method, each agent solves an Optimal Control Problem only when certain control performances cannot be guaranteed according to certain triggering rules. This could lead to the reduction of energy consumption and the alleviation of over-usage of communication loads under critical resource constraints in networked control systems, such as limited communication power and the life-time of the battery. The triggering rule is derived for event-based case, where control inputs are executed based on the current state measurement. Our proposed method is also verified through a simple simulation example.