Predictive control barrier functions: Enhanced safety mechanisms for learning-based control

Abstract

While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications. Recent developments address this issue through so-called predictive safety filters, which assess if a proposed learning-based control input can lead to constraint violations and modifies it if necessary to ensure safety for all future time steps. The theoretical guarantees of such predictive safety filters rely on the model assumptions and minor deviations can lead to failure of the filter putting the system at risk. This paper introduces an auxiliary softconstrained predictive control problem that is always feasible at each time step and asymptotically stabilizes the feasible set of the original safety filter, thereby providing a recovery mechanism in safety-critical situations. This is achieved by a simple constraint tightening in combination with a terminal control barrier function. By extending discrete-time control barrier function theory, we establish that the proposed auxiliary problem provides a 'predictive' control barrier function. The resulting algorithm is demonstrated using numerical examples.

Safety-Aware Learning-Based Control of Systems with Uncertainty Dependent Constraints (extended version)

“…Most recently CBF has been combined with learning techniques to design control for safety critical systems [13], [14], [15]. Authors in [16] applied Gaussian Process (GP) to model the uncertainty using which they imposed an uncertainty-aware CBF constraint to ensure safety of an RL algorithm.…”