具有群集行为的时变函数分布式优化（英文）
Distributed velocity optimization of time-varying functions with flocking behavior

本文对具有群集行为的连续时间多智能体系统的优化问题进行了研究. 考虑具有二阶动力学的多智能体系统, 每个智能体都具有一个局部的时变代价函数. 本文的目标是仅仅依靠局部信息交流使得多智能体在运动的过程中保持 连通性、避免碰撞、总体代价函数最小. 为此本文设计了一种具有群集行为的控制协议, 该协议仅仅依赖于自己和邻居 的速度. 可以证明在该控制协议作用下, 所有智能体在保持连通、避免碰撞的同时, 速度能够跟踪上最优速度. 最后, 通 过一个仿真来说明本文的结果.
This paper studies optimization problem for continuous-time multi-agent systems with flocking behavior. Multi-agents with second-order dynamics are considered. Each agent is equipped with a local time-varying cost function which is known only to an individual agent. The objective is to make multi-agents’ velocities minimize the sum of local functions by local interaction, while avoiding collision and preserving connectivity. A distributed protocol with flocking behavior is presented, in which each agent depends only on its own velocity and neighbor’s velocities. It can be proved that under the control protocol, all agents remain connected and avoid collisions while the velocity of the agents tracks the optimal velocity.