Adaptive distributed convex optimization for multi-agent and its application in flocking behavior. Issue 2 (January 2019)

• Record Type: Journal Article
• Authors: Yang, Zhengquan; Zhang, Qing; Chen, Zengqiang
• Is Part Of: Journal of the Franklin Institute. Volume 356:Issue 2(2019)
• Page Start: 1038
• Page End: 1050
• Publication Date: 2019-01
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2018.05.004
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper studies adaptive optimization problem of continuous-time multi-agent systems. Multi-agents with second-order dynamics are considered. Each agent is equipped with a 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. First, a distributed adaptive algorithm is presented, in which each agent depends only on its own velocity and neighbors velocities. It is indicated that all agents can track the optimal velocity. Then we apply the distributed adaptive algorithm to flocking of multi-agents. It is proved that all agents can track the optimal trajectory. The agents will maintain connectivity and avoid the inter-agent collision. Finally, two simulations are included to illustrate the results.