Self-Triggered Distributed Model Predictive Control with Synchronization Parameters Interaction

TL;DR

This paper presents a novel self-triggered distributed model predictive control method for multi-agent systems that uses parameter synchronization to improve efficiency and stability, with proven guarantees and simulation validation.

Contribution

Introduces a self-triggered distributed MPC approach utilizing synchronization parameters, reducing communication and computation in multi-agent systems.

Findings

Ensures recursive feasibility of the optimal control problem.

Guarantees stability of the closed-loop system.

Demonstrates effectiveness through simulation results.

Abstract

This paper investigates an aperiodic distributed model predictive control approach for multi-agent systems (MASs) in which parameterized synchronization constraints is considered and an innovative self-triggered criterion is constructed. Different from existing coordination methodology, the proposed strategy achieves the cooperation of agents through the synchronization of one-dimensional parameters related to the control inputs. At each asynchronous sampling instant, each agent exchanges the one-dimensional synchronization parameters, solves the optimal control problem (OCP) and then determines the open-loop phase. The incorporation of the selftriggered scheme and the synchronization parameter constraints relieves the computational and communication usage. Sufficient conditions guaranteeing the recursive feasibility of the OCP and the stability of the closed-loop system are proven. Simulation results illustrate the validity of the proposed control algorithm.