Cloud-mediated self-triggered synchronization of a general linear multi-agent system over a directed graph

TL;DR

This paper introduces a cloud-mediated self-triggered control approach for high-order linear multi-agent systems over directed graphs, enabling asynchronous updates and bounded synchronization without Zeno behavior.

Contribution

It develops a novel self-triggered synchronization method for high-order linear agents using cloud data, handling exponential dynamics and ensuring bounded convergence.

Findings

Achieves bounded state synchronization among agents.

Prevents Zeno behaviors in the control scheme.

Validated effectiveness through numerical simulations.

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.