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

2020/07/22 by Jun Zeng, Zeng, Jun, Bike Zhang +3 · 32 citations
Engineering · #Advanced Control Systems Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #Real-time simulation and control systems #Robotics (cs.RO) #Systems and Control (eess.SY) #Vehicle Dynamics and Control Systems #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2007.11718

openalex publication_date 2020/07/22 · openalex created_date 2020/07/29 · openalex updated_date 2026/07/28

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.

Citations

• On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
• Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates

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