Synchronization-Based Cooperative Distributed Model Predictive Control

TL;DR

This paper introduces a synchronization-based iterative algorithm for cooperative distributed model predictive control, improving safety and consistency in multi-agent systems like vehicle control.

Contribution

It proposes a novel synchronization-based algorithm that ensures consistent predictions among agents, addressing safety issues in distributed MPC.

Findings

Effective in controlling multiple small-scale vehicles

Reduces inconsistencies in distributed control solutions

Demonstrates improved safety constraints adherence

Abstract

Distributed control algorithms are known to reduce overall computation time compared to centralized control algorithms. However, they can result in inconsistent solutions leading to the violation of safety-critical constraints. Inconsistent solutions can arise when two or more agents compute concurrently while making predictions on each others control actions. To address this issue, we propose an iterative algorithm called Synchronization-Based Cooperative Distributed Model Predictive Control, which we presented in [1]. The algorithm consists of two steps: 1. computing the optimal control inputs for each agent and 2. synchronizing the predicted states across all agents. We demonstrate the efficacy of our algorithm in the control of multiple small-scale vehicles in our Cyber-Physical Mobility Lab.