Predictive safety filter using system level synthesis

TL;DR

This paper introduces an improved model predictive safety filter that leverages system level synthesis to enhance safety guarantees, reduce control modifications, and improve scalability for linear systems with disturbances.

Contribution

It presents a novel SL-MPSF formulation incorporating system level synthesis, offering better safety, fewer control modifications, and an explicit, scalable variant with reduced computational effort.

Findings

SL-MPSF ensures safety with less control intervention.

Explicit variant maintains scalability and reduces online computation.

Numerical example demonstrates advantages over existing methods.

Abstract

Safety filters provide modular techniques to augment potentially unsafe control inputs (e.g. from learning-based controllers or humans) with safety guarantees in the form of constraint satisfaction. In this paper, we present an improved model predictive safety filter (MPSF) formulation, which incorporates system level synthesis techniques in the design. The resulting SL-MPSF scheme ensures safety for linear systems subject to bounded disturbances in an enlarged safe set. It requires less severe and frequent modifications of potentially unsafe control inputs compared to existing MPSF formulations to certify safety. In addition, we propose an explicit variant of the SL-MPSF formulation, which maintains scalability, and reduces the required online computational effort - the main drawback of the MPSF. The benefits of the proposed system level safety filter formulations compared to state-of-the-art MPSF formulations are demonstrated using a numerical example.