A Fixed-Time Distributed Optimization Algorithm For First-Order Multi-Agent Systems Based on Dynamic Event-Triggered Mechanism

In order to solve the convex optimization problem of the local cost function of the multi-agent systems with consistency constraints in fixed time and the continuous-time communication problem, this paper proposes a distributed optimization algorithm for a first-order multi-agent systems based on a dynamic event-triggered communication mechanism. The proposed algorithm is divided into three parts, the first part enables each agent to converge to consensus in fixed time under any initial condition; the second part minimizes the sum of all local objective functions in fixed time while satisfied the consistency constraint; the third part designs a dynamic event-triggered communication mechanism, aiming to reduce the system energy consumption and save communication resources, which can avoid Zeno behavior after reaching consensus. Simulation results show that the proposed algorithm can make the multi-agent system converge to consensus in any initial state within a fixed time. What’s more, compared with the traditional communication mechanism, the system communication frequency is found to be significantly reduced, which verifies the superiority and effectiveness of the proposed dynamic event-triggered communication mechanism.