Parallelizing a State Exchange Strategy for Noncooperative Distributed NMPC

TL;DR

This paper introduces a parallelizable algorithm for distributed noncooperative NMPC systems with interconnected state constraints, ensuring feasibility and stability through a hierarchical control strategy.

Contribution

It presents a novel parallelization method for distributed NMPC with theoretical guarantees of feasibility and stability based on an extended trajectory stability analysis.

Findings

Algorithm generates a parallel hierarchy among systems.

Feasibility and stability are proven for the closed-loop system.

The approach extends trajectory stability to a distributed control setting.

Abstract

We consider a distributed non cooperative control setting in which systems are interconnected via state constraints. Each of these systems is governed by an agent which is responsible for exchanging information with its neighbours and computing a feedback law using a nonlinear model predictive controller to avoid violations of constraints. For this setting we present an algorithm which generates a parallelizable hierarchy among the systems. Moreover, we show both feasibility and stability of the closed loop using only abstract properties of this algorithm. To this end, we utilize a trajectory based stability result which we extend to the distributed setting.