Using AoI Forecasts in Communicating and Robust Distributed Model-Predictive Control

TL;DR

This paper introduces a robust distributed model-predictive control framework that uses Age of Information forecasts to optimize communication scheduling in cyber-physical systems, improving stability and performance.

Contribution

It proposes a novel integrated control and communication design leveraging AoI predictions for robust stability in distributed systems.

Findings

Achieves robust stabilization under variable communication schedules.

Demonstrates effectiveness in vehicle platooning scenario.

Provides conditions for stability with AoI-based scheduling.

Abstract

In order to enhance the performance of cyber-physical systems, this paper proposes the integrated de-sign of distributed controllers for distributed plants andthe control of the communication network. Conventionaldesign methods use static interfaces between both enti-ties and therefore rely on worst-case estimations of com-munication delay, often leading to conservative behaviorof the overall system. By contrast, the present approachestablishes a robust distributed model-predictive controlscheme, in which the local subsystem controllers oper-ate under the assumption of a variable communicationschedule that is predicted by a network controller. Us-ing appropriate models for the communication network,the network controller applies a predictive network policyfor scheduling the communication among the subsystemcontrollers across the network. Given the resulting time-varying predictions of the age of information, the papershows under which conditions the subsystem controllerscan robustly stabilize the distributed system. To illustratethe approach, the paper also reports on the application to avehicle platooning scenario.