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arXiv 1702.06285eess.SYcs.SY
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多智能体系统中受性能约束的分布式事件触发一致性

Performance Constrained Distributed Event-triggered Consensus in Multi-agent Systems

Amir Amini, Arash Mohammadi, Amir Asif

AI总结:

本文提出了一种分布式事件触发一致性方法,用于线性多智能体系统,保证收敛速率、抗控制增益不确定性以及设计参数的帕累托最优性,即事件触发阈值(ET)和控制增益。通过将事件触发一致性问题转换为等效系统的稳定性问题,并利用Lyapunov稳定性定理将性能约束纳入其中,采用近似线性标量化方法,通过求解凸约束优化问题同时设计ET和控制增益,无需全局信息即可局部优化。通过实验多智能体系统的仿真验证了所提方法的有效性。

摘要:

本文提出了一种分布式事件触发一致性方法,用于线性多智能体系统,保证收敛速率、抗控制增益不确定性以及设计参数的帕累托最优性,即事件触发阈值(ET)和控制增益。事件触发一致性问题首先被转换为等效系统的稳定性问题。然后利用Lyapunov稳定性定理将性能约束与事件触发一致性相结合。使用近似线性标量化方法,通过求解一个凸约束优化问题同时设计ET和控制增益。随后进行一些初步步骤,优化可以局部进行,即不需要全局信息。通过模拟研究了所提出方法的有效性,用于实验多智能体系统。

英文摘要:

The paper proposes a distributed eventtriggered consensus approach for linear multi-agent systems with guarantees over rate of convergence, resilience to control gain uncertainties, and Pareto optimality of design parameters, namely, the event-triggering threshold (ET) and control gain. The event-triggered consensus problem is first converted to stability problem of an equivalent system. The Lyapunov stability theorem is then used to incorporate the performance constraints with the event-triggered consensus. Using an approximated linear scalarization method, the ET and the control gain are designed simultaneously by solving a convex constrained optimization problem. Followed by some preliminary steps, the optimization can be performed locally, i.e., no global information is required. The effectiveness of the proposed approach is studied through simulations for an experimental multi-agent system.

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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