Guo Ge, Kang Jian, Li Ranran, Yang Guanghong
IEEE Trans Cybern. 2022 Jun;52(6):5464-5473. doi: 10.1109/TCYB.2020.3032429. Epub 2022 Jun 16.
This article pays close attention to a distributed optimization problem for multiagent systems subject to exogenous disturbances. A novel distributed model reference adaptive control (D-MRAC) scheme is proposed that no explicit disturbance observer or internal model unit is involved, which not only enhances robustness but also improves transient performance. In contrast to the continuous communication that is often assumed in the existing distributed optimization works, the new method allows for more realistic scenarios in which the agents communicate with each other at discrete-time instants. It is shown by Lyapunov analysis that the concerned distributed optimization problem can be solved by the proposed D-MRAC scheme as long as the communication interval is smaller than a given threshold, which can be calculated by following the steps given in this article. Numerical simulations have shown the effectiveness of the presented method.
本文关注受外部干扰的多智能体系统的分布式优化问题。提出了一种新颖的分布式模型参考自适应控制（D-MRAC）方案，该方案不涉及明确的干扰观测器或内部模型单元，不仅增强了鲁棒性，还改善了瞬态性能。与现有分布式优化工作中通常假设的连续通信不同，新方法允许更实际的场景，即智能体在离散时刻相互通信。通过李雅普诺夫分析表明，只要通信间隔小于给定阈值，所提出的D-MRAC方案就能解决相关的分布式优化问题，该阈值可按照本文给出的步骤计算。数值模拟表明了所提方法的有效性。
