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arXiv 2103.15604eess.SYcs.SY
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STL任务下主从多智能体系统的高阶收敛控制障碍函数

Higher Order Convergent Control Barrier Functions for Leader-Follower Multi-Agent Systems under STL Tasks

Maryam Sharifi, Dimos V. Dimarogonas

摘要:

针对一类主从式多智能体系统,提出了基于时变收敛高阶控制障碍函数的信号时序逻辑(STL)任务控制策略.每个代理被分配一个本地STL任务,该任务可能取决于其他任务中涉及的代理的行为。领导者具有相关任务的知识,并控制子组所涉及的代理的性能。鲁棒的解决方案的任务满意度,基于领导者的可访问性的追随者代理的状态建议。我们的方法找到解决方案,以保证STL任务的满意度独立的代理的初始条件。

英文摘要:

This paper presents control strategies based on time-varying convergent higher order control barrier functions for a class of leader-follower multi-agent systems under signal temporal logic (STL) tasks. Each agent is assigned a local STL task which may be dependent on the behavior of agents involved in other tasks. The leader has knowledge on the associated tasks and controls the performance of the subgroup involved agents. Robust solutions for the task satisfaction, based on the leader's accessibility to the follower agents' states are suggested. Our approach finds solutions to guarantee the satisfaction of STL tasks independent of the agents' initial conditions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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