  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.
