Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function

Abstract

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.

References in corpus (3)

• Optimization-Based Autonomous Racing of 1:43 Scale RC Cars
• Adaptive Safety with Control Barrier Functions
• Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates

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• Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehicles
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• Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations
• Safety-Critical Control using Optimal-decay Control Barrier Function with Guaranteed Point-wise Feasibility
• Nonlinear Model Predictive Control with Single-Shooting Method for Autonomous Personal Mobility Vehicle
• Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change