Reconfigurable Plug-and-play Distributed Model Predictive Control for Reference Tracking

TL;DR

This paper introduces a reconfigurable plug-and-play distributed model predictive control scheme for networks with changing topology, ensuring stability and feasibility during subsystem join/leave operations, demonstrated on mass-spring-damper systems.

Contribution

It presents a novel two-phase PnP MPC scheme with passivity-based redesign and reconfigurable terminal ingredients for stability and feasibility in dynamic networks.

Findings

Faster PnP operations due to the redesign phase

Increased flexibility with more subsystem requests

Effective reference tracking in mass-spring-damper networks

Abstract

A plug-and-play model predictive control (PnP MPC) scheme is proposed for varying-topology networks to track piecewise constant references. The proposed scheme allows subsystems to occasionally join and leave the network while preserving asymptotic stability and recursive feasibility and comprises two main phases. In the redesign phase, passivity-based control is used to ensure that asymptotic stability of the network is preserved. In the transition phase, reconfigurable terminal ingredients are used to ensure that the distributed MPC problem is initially feasible after the PnP operation. The efficacy of the proposed scheme is evaluated by applying it to a network of mass-spring-damper systems and comparing it to a benchmark scheme. It is found that the novel redesign phase results in faster PnP operations, whereas the novel transition phase increases flexibility by accepting more requests.