  In this paper, we propose a novel predictive safety filter that is robust to
bounded perturbations and is implemented in an even-triggered fashion to reduce
online computation. The proposed safety filter extends upon existing work to
reject disturbances for discrete-time, time-varying nonlinear systems with
time-varying constraints. The safety filter is based on novel concepts of
robust, discrete-time barrier functions and can be used to filter any control
law. Here, we use the safety filter in conjunction with Differentiable
Predictive Control (DPC) as a promising offline learning-based policy
optimization method. The approach is demonstrated on a two-tank system,
building, and single-integrator example.
