Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
This work addresses safety-critical control for robotics, particularly in high-performance scenarios like autonomous racing, but it appears incremental as it combines existing MPC and CBF concepts.
The authors tackled the challenge of ensuring safety in model predictive control (MPC) for robotic systems operating near performance limits by developing a strategy that integrates discrete-time control barrier functions (CBFs) to guarantee safety and achieve optimal performance, with validation on a 2D double integrator for obstacle avoidance and a competitive car racing simulation.
The optimal performance of robotic systems is usually achieved near the limit of state and input bounds. Model predictive control (MPC) is a prevalent strategy to handle these operational constraints, however, safety still remains an open challenge for MPC as it needs to guarantee that the system stays within an invariant set. In order to obtain safe optimal performance in the context of set invariance, we present a safety-critical model predictive control strategy utilizing discrete-time control barrier functions (CBFs), which guarantees system safety and accomplishes optimal performance via model predictive control. We analyze the stability and the feasibility properties of our control design. We verify the properties of our method on a 2D double integrator model for obstacle avoidance. We also validate the algorithm numerically using a competitive car racing example, where the ego car is able to overtake other racing cars.