事件触发间歇通讯下多智能体系统的固定时间分布式优化

针对多智能体系统的分布式优化问题,提出一种新的事件触发非周期间歇通讯控制方法,并研究该控制方法下系统的固定时间收敛性.首先,考虑一类更一般的分布式优化问题,其优化目标是局部目标函数的凸组合.其次,为了减少控制过程中智能体之间的通讯花费,设计一种新的事件触发间歇控制协议.通过引入两个辅助动力系统,并运用固定时间稳定性理论、代数图论和不等式放缩技巧,证明智能体的状态在固定时间内达到一致并渐近收敛到优化问题的最优解.结合事件触发条件以及间歇控制机制,排除控制过程中的Zeno行为.最后,通过数值仿真验证所得结论的有效性.

Distributed fixed-time optimization for multi-agent systems via event-triggered intermittent communication

For the distributed optimization problem of multi-agent systems(MASs), this paper proposes a new event-triggered aperiodic intermittent communication control method and studies the fixed-time convergence of the MASs. Firstly, compared with existing distributed optimization problems, a more general one is considered in which the optimization objective is a convex combination of local objective functions. Secondly, to reduce the communication cost, a new event-triggered intermittent control protocol is designed. By introducing two auxiliary dynamical systems, and using fixed-time stability theory, algebraic graph theory and inequality techniques, it is proved that agents'' states can reach consensus in a fixed-time interval and asymptotically converge to the optimal solution of the optimization problem. In a combination with the event-triggering condition and intermittent control mechanism, Zeno behavior can be avoided. Finally, a numerical example is given to verify the effectiveness of the conclusion.