Resilient Optimal Tracking of Output Formation for Open Multiagent Systems With Time-Varying Malicious Agents
研究了开放多智能体系统中，智能体可随时加入或退出且可能切换正常与恶意身份时，如何通过分布式协议实现输出编队的最优跟踪，无需知道智能体身份。
This article focuses on resilient time-varying optimal tracking problems of output formation in open multiagent systems (MASs). Agents can join or exit at any time and may be subject to switching between normal and malicious identities. Normal agents in the open MAS aim to minimize the sum of their local time-varying composite objective functions, each consisting of an output-related term and a state-related nonsmooth term. Simultaneously, agents are required to maintain a given output formation configuration. Based on relative outputs from neighbors, a distributed tracking protocol is proposed, combining the subgradient method with proximal mapping and an adaptive aggregation technique. By analyzing the upper bounds of total dynamic regret and individual dynamic regrets, it is proved that resilient optimal tracking of output formation can be achieved without knowledge of agent identities. Simulations validate these results.