Sample-based Dynamic Event-triggered Algorithm for Optimization Problem of Multi-agent Systems

The convex optimization problem of multi-agent systems is investigated. In order to deduce the communication burden of the system and simplify the implementation of the algorithm on the digital controllers, a sample-based event-triggered optimization algorithm is proposed and a novel dynamic event-trigged condition is designed. The global optimal solution can be obtained exponentially. The event-triggered time intervals are significantly enlarged by dynamic event-triggered condition. Moreover, Zeno behavior is excluded due to sampling control mechanism in our scheme. Two numerical cases are presented to validate the relevant results.