Convex Model Predictive Control for Safe Output Consensus of Nonlinear Multi-Agent Systems

TL;DR

This paper introduces a convex model predictive control method for nonlinear multi-agent systems that ensures safe output consensus with significantly reduced computational complexity, enabling real-time applications.

Contribution

The paper proposes a novel convex MPC approach using SQP and tangent-line projection to linearize nonlinear constraints, improving computational efficiency and guaranteeing stability.

Findings

Achieved 35-52 times faster computation than baseline methods.

Guaranteed convergence, recursive feasibility, and stability of the control scheme.

Validated effectiveness through simulations on multi-agent systems with unicycle dynamics.

Abstract

Nonlinear dynamics and safety constraints typically result in a nonlinear programming problem when applying model predictive control to achieve safe output consensus. To avoid the heavy computational burden of solving a nonlinear programming problem directly, this paper proposes a novel Convex Model Predictive Control (CMPC) approach based on a Sequential Quadratic Programming (SQP) scheme. The core of our method lies in transforming the nonlinear constraints into linear forms: we linearize the system dynamics and convexify the discrete-time high-order control barrier functions using a proposed tangent-line projection method. Consequently, the original problem is reduced to a quadratic program that can be iteratively solved within the SQP scheme at each time step of CMPC. Furthermore, we provide the formal guarantee of the convergence of the SQP scheme, and subsequently guarantee the recursive feasibility and stability of CMPC. Simulations on multi-agent systems with unicycle dynamics demonstrate a 35-52 times reduction in computation time compared with baseline methods, confirming the suitability of the proposed approach for real-time safe output consensus control.