Distributed Model Predictive Control for Asynchronous Multi-agent Systems with Self-Triggered Coordinator

TL;DR

This paper presents a distributed model predictive control approach for asynchronous multi-agent systems, utilizing self-triggered coordination to balance control performance and energy efficiency, with proven stability and feasibility.

Contribution

It introduces a novel self-triggered control scheme with a shrinking constraint and adaptive prediction horizon for asynchronous multi-agent systems.

Findings

Achieves coordination under asynchronous communication.

Ensures recursive feasibility and stability.

Demonstrates improved energy-performance trade-off.

Abstract

This paper investigates the distributed model predictive control for an asynchronous nonlinear multi-agent system with external interference via a self-triggered generator and a prediction horizon regulator. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enable the robustness of the system. Then, the trigger interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade-off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the multi-agent system is achieved under asynchronous communication. Finally, the recursive feasibility and stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.