A Machine Learning Approach to Passivity-Preserving Safety-Critical Control

Passivity-Based Control enables to constructively synthetize controllers with guarantees on the stability of the overall system. Although safety-critical control based on Control Barrier Functions is able to synthetize controllers that effectively cope with state constraints, passivity and input bounds are not addressed in general. This paper proposes a novel model-based Machine Learning methodology aimed at synthesizing Control Barrier Functions such that passivity of the closed loop system is preserved under safety-critical control and input saturation. Numerical simulations on a cart-pole system and on a 2R robot validate the effectiveness of the proposed control strategy in terms of performances and passivity preservation.