Cooperative nonlinear distributed model predictive control with dissimilar control horizons

TL;DR

This paper proposes a nonlinear distributed model predictive control algorithm that allows agents to have different and time-varying control horizons, improving decentralization and efficiency in multi-agent systems.

Contribution

It introduces a novel DMPC scheme with dissimilar control horizons, ensuring recursive feasibility and cost stability, and demonstrates its effectiveness through numerical simulations.

Findings

Effectively approximates traditional DMPC performance

Reduces the number of optimization variables

Ensures recursive feasibility and cost stability

Abstract

In this paper, we introduce a nonlinear distributed model predictive control (DMPC) algorithm, which allows for dissimilar and time-varying control horizons among agents, thereby addressing a common limitation in current DMPC schemes. We consider cooperative agents with varying computational capabilities and operational objectives, each willing to manage varying numbers of optimization variables at each time step. Recursive feasibility and a non-increasing evolution of the optimal cost are proven for the proposed algorithm. Through numerical simulations on systems with three agents, we show that our approach effectively approximates the performance of traditional DMPC, while reducing the number of variables to be optimized. This advancement paves the way for a more decentralized yet coordinated control strategy in various applications, including power systems and traffic management.