Adaptive event-triggered distributed model predictive control for multi-agent systems

Abstract

Event-triggered control is an effective control approach with control update instants determined by pre-defined event-triggering conditions, and the triggering conditions are usually required to be tested continuously. In this paper, we present an adaptive event-triggered distributed model predictive control algorithm for nonlinear continuous-time multi-agent systems. All the agents are able to adaptively determine when to check the triggering conditions, which means the triggering conditions are tested intermittently with intervals determined adaptively. We prove the feasibility of the proposed algorithm, and also prove that the multi-agent system under the algorithm will converge to an invariant set in finite time by utilizing variable prediction horizon approach instead of the Lyapunov function method, such that the event-triggering conditions are less conservative. Finally, we provide numerical examples to verify the effectiveness and superiority of the proposed algorithm, revealing that the proposed algorithm can efficiently reduce the communication and computation cost as well as the sensing load.