A System Level Approach to Tube-based Model Predictive Control

TL;DR

This paper introduces a novel system level tube-MPC method that optimizes the tube controller online using system level parameterization, reducing conservativeness in robust control.

Contribution

It develops a new SLTMPC approach based on SLP, enabling online optimization of the tube controller, which improves over traditional robust MPC methods.

Findings

Reduces conservativeness in tube-based MPC

Derives SLTMPC from an equivalence with robust MPC

Demonstrates advantages through a numerical example

Abstract

Robust tube-based model predictive control (MPC) methods address constraint satisfaction by leveraging an a priori determined tube controller in the prediction to tighten the constraints. This paper presents a system level tube-MPC (SLTMPC) method derived from the system level parameterization (SLP), which allows optimization over the tube controller online when solving the MPC problem, which can significantly reduce conservativeness. We derive the SLTMPC method by establishing an equivalence relation between a class of robust MPC methods and the SLP. Finally, we show that the SLTMPC formulation naturally arises from an extended SLP formulation and show its merits in a numerical example.