Distributed Safety-Critical MPC for Multi-Agent Formation Control and Obstacle Avoidance

TL;DR

This paper introduces a distributed safety-critical MPC framework for multi-agent systems that ensures formation control and obstacle avoidance by integrating high-order control barrier functions and Lyapunov functions, with proven stability and feasibility.

Contribution

It presents a novel distributed safety-critical MPC algorithm using high-order barrier functions and neighbor state estimation for nonlinear multi-agent systems.

Findings

Demonstrates improved performance over existing methods.

Ensures safety and stability with theoretical guarantees.

Reduces computation time in simulations.

Abstract

For nonlinear multi-agent systems with high relative degrees, achieving formation control and obstacle avoidance in a distributed manner remains a significant challenge. To address this issue, we propose a novel distributed safety-critical model predictive control (DSMPC) algorithm that incorporates discrete-time high-order control barrier functions (DHCBFs) to enforce safety constraints, alongside discrete-time control Lyapunov functions (DCLFs) to establish terminal constraints. To facilitate distributed implementation, we develop estimated neighbor states for formulating DHCBFs and DCLFs, while also devising a bound constraint to limit estimation errors and ensure convergence. Additionally, we provide theoretical guarantees regarding the feasibility and stability of the proposed DSMPC algorithm based on a mild assumption. The effectiveness of the proposed method is evidenced by the simulation results, demonstrating improved performance and reduced computation time compared to existing approaches.