Distributed Model Predictive Control with Reconfigurable Terminal Ingredients for Reference Tracking

TL;DR

This paper introduces a novel distributed MPC scheme with reconfigurable terminal ingredients for reference tracking in networked systems, improving flexibility and reducing conservativeness through online adaptation.

Contribution

It proposes a reconfigurable terminal set approach for distributed MPC, addressing limitations of traditional offline computed sets and enhancing stability and feasibility.

Findings

The scheme is successfully tested in simulations.

Reconfigurable terminal ingredients improve tracking performance.

Distributed optimization efficiently solves the non-convex problem.

Abstract

Various efforts have been devoted to developing stabilizing distributed Model Predictive Control (MPC) schemes for tracking piecewise constant references. In these schemes, terminal sets are usually computed offline and used in the MPC online phase to guarantee recursive feasibility and asymptotic stability. Maximal invariant terminal sets do not necessarily respect the distributed structure of the network, hindering the distributed implementation of the controller. On the other hand, ellipsoidal terminal sets respect the distributed structure, but may lead to conservative schemes. In this paper, a novel distributed MPC scheme is proposed for reference tracking of networked dynamical systems where the terminal ingredients are reconfigured online depending on the closed-loop states to alleviate the aforementioned issues. The resulting non-convex infinite-dimensional problem is approximated using a quadratic program. The proposed scheme is tested in simulation where the proposed MPC problem is solved using distributed optimization.