Configuration-Constrained Tube MPC for Tracking

TL;DR

This paper introduces a new tube-based MPC method that ensures robust, feasible tracking of changing references in uncertain linear systems, with applications demonstrated in autonomous vehicle lane changes.

Contribution

It develops a configuration-constrained polytope framework for tube parameterization, enhancing flexibility and robustness in tracking control.

Findings

Ensures recursive feasibility for varying references

Achieves robust asymptotic stability for piecewise constant references

Demonstrates effectiveness through numerical examples including autonomous vehicle lane change

Abstract

This paper proposes a novel tube-based Model Predictive Control (MPC) framework for tracking varying setpoint references with linear systems subject to additive and multiplicative uncertainties. The MPC controllers designed using this framework exhibit recursively feasible for changing references, and robust asymptotic stability for piecewise constant references. The framework leverages configuration-constrained polytopes to parameterize the tubes, offering flexibility to optimize their shape. The efficacy of the approach is demonstrated through two numerical examples. The first example illustrates the theoretical results, and the second uses the framework to design a lane-change controller for an autonomous vehicle.