Priority-Driven Constraints Softening in Safe MPC for Perturbed Systems

Abstract

This letter presents a safe model predictive control framework designed to guarantee the satisfaction of hard safety constraints, for perturbed dynamical systems. Safety is guaranteed by softening the constraints selected on a priority basis from a subset of constraints defined by the designer. Since such an online selection is the result of an auxiliary optimization problem, its computational overhead is alleviated by off-line learning its approximated solution, rather than solving it exactly online. Simulation results, obtained from an automated driving application, show that the proposed approach provides guarantees of collision-avoidance hard constraints despite the unpredicted behaviors of the surrounding environment.