# A Divide and Conquer Approach to Cooperative Distributed Model Predictive Control

## TL;DR

This paper introduces a novel divide and conquer cooperative distributed MPC framework that enables parallel local computations with guaranteed stability, reducing communication and synchronization requirements in linear systems.

## Contribution

It proposes a state transformation-based approach allowing parallel local optimization without iterative cooperation, while maintaining stability guarantees.

## Key findings

- Parallel local optimization without iterations is feasible.
- Stability can be guaranteed with proper cost function design.
- Numerical examples demonstrate the method's effectiveness.

## Abstract

This paper is concerned with the design of cooperative distributed Model Predictive Control (MPC) for linear systems. Motivated by the special structure of the distributed models in some existing literature, we propose to apply a state transformation to the original system and global cost function. This has major implications on the closed-loop stability analysis and the mechanism of the resultant cooperative framework. It turns out that the proposed framework can be implemented without cooperative iterations being performed in the local optimizations, thus allowing one to compute the local inputs in parallel and independently from each other while requiring only partial plant-wide state information. The proposed framework can also be realized with cooperative iterations, thereby keeping the advantages of the technique in the former reference. Under certain conditions, closed-loop stability for both implementation procedures can be guaranteed a priori by appropriate selections of the original local cost functions. The strengths and benefits of the proposed method are highlighted by means of two numerical examples.