Abstract
<p indent="0mm">The tracking consensus of multiagent systems based on distributed model predictive control (DMPC) is a current research hotspot. However, as a research difficulty, effective solutions to deal with external disturbances and reduce computational resource consumption are lacking. This paper proposes a new constraint tightening scheme and establishes a new distributed optimization problem. Moreover, an adaptive event-triggering mechanism is designed using the idea of variable prediction horizon, and an adaptive event-triggered DMPC approach for the tracking consensus of multiagent systems is proposed, effectively solving the problems of the unsatisfaction of constraints and computational resource consumption in the tracking consensus of multiagent systems. The effectiveness of the proposed approach is validated by a multivehicle system simulation.
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