Asynchronous Computation of Tube-based Model Predictive Control

TL;DR

This paper introduces an asynchronous computation approach for tube-based MPC that separates the optimization of the nominal trajectory and tubes into primary and secondary processes, reducing computational load and maintaining feasibility.

Contribution

It proposes a novel asynchronous mechanism for system level tube-MPC, enabling high-frequency trajectory updates while managing complex tube computations separately.

Findings

Secondary process can continuously update tubes without losing feasibility

The method reduces computational complexity of online tube-based MPC

Maintains recursive feasibility despite asynchronous updates

Abstract

Tube-based model predictive control (MPC) methods bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction. While techniques that compute the tubes online reduce conservativeness and increase performance, they suffer from high and potentially prohibitive computational complexity. This paper presents an asynchronous computation mechanism for system level tube-MPC (SLTMPC), a recently proposed tube-based MPC method which optimizes over both the nominal trajectory and the tubes. Computations are split into a primary and a secondary process, computing the nominal trajectory and the tubes, respectively. This enables running the primary process at a high frequency and moving the computationally complex tube computations to the secondary process. We show that the secondary process can continuously update the tubes, while retaining recursive feasibility of the primary process.