Remote Tube-based MPC for Tracking Over Lossy Networks

TL;DR

This paper introduces a novel remote tube-based model predictive control framework that effectively manages constrained systems over lossy networks by splitting control tasks between local and remote components, ensuring robustness against disturbances and packet losses.

Contribution

It proposes a new split control architecture combining local disturbance rejection with remote optimal trajectory planning, with theoretical guarantees for feasibility and tracking in lossy network conditions.

Findings

Outperforms existing solutions in lossy network scenarios.

Demonstrates robustness with both linear and nonlinear dynamics.

Effective in various packet loss probabilities and disturbance conditions.

Abstract

This paper addresses the problem of controlling constrained systems subject to disturbances in the case where controller and system are connected over a lossy network. To do so, we propose a novel framework that splits the concept of tube-based model predictive control into two parts. One runs locally on the system and is responsible for disturbance rejection, while the other runs remotely and provides optimal input trajectories that satisfy the system's state and input constraints. Key to our approach is the presence of a nominal model and an ancillary controller on the local system. Theoretical guarantees regarding the recursive feasibility and the tracking capabilities in the presence of disturbances and packet losses in both directions are provided. To test the efficacy of the proposed approach, we compare it to a state-of-the-art solution in the case of controlling a cartpole system. Extensive simulations are carried out with both linearized and nonlinear system dynamics, as well as different packet loss probabilities and disturbances. The code for this work is available at https://github.com/EricssonResearch/Robust-Tracking-MPC-over-Lossy-Networks