Title: Cloud-mediated self-triggered synchronization of a general linear multi-agent system over a directed graph Show Chinese title

Title: 通过有向图实现的一般线性多智能体系统的云中自触发同步

Abstract: This paper proposes a self-triggered synchronization control method of a general high-order linear time-invariant multi-agent system through a cloud repository. In the cloud-mediated self-triggered control, each agent asynchronously accesses the cloud repository to get past information on its neighboring agents. Then, the agent predicts future behaviors of its neighbors as well as of its own, and locally determines its next access time to the cloud repository. In the case of a general high-order linear agent dynamics, each agent has to estimate exponential evolution of its trajectory characterized by eigenvalues of a system matrix, which is different from single/double integrator or first-order linear agents. Our proposed method deals with exponential behaviors of the agents by tightly evaluating the bounds on matrix exponentials. Based on these bound, we design the self-triggered controller through a cloud which achieves bounded state synchronization of the closed-loop system without exhibiting any Zeno behaviors. The effectiveness of the proposed method is demonstrated through the numerical simulation. Show Chinese abstract

Abstract: 本文提出了一种通过云存储库实现的一般高阶线性时不变多智能体系统的自触发同步控制方法。 在云中介的自触发控制中，每个智能体异步访问云存储库以获取其相邻智能体的过去信息。 然后，智能体预测其相邻智能体以及自身的未来行为，并本地确定其下一次访问云存储库的时间。 对于一般的高阶线性智能体动力学，每个智能体必须估计由系统矩阵特征值所表征的轨迹指数演化，这与单/双积分器或一阶线性智能体不同。 我们提出的方法通过对矩阵指数的界限进行紧密评估来处理智能体的指数行为。 基于这些界限，我们通过云设计自触发控制器，实现了闭环系统的有界状态同步，而不会表现出Zeno行为。 所提出方法的有效性通过数值仿真得到验证。