Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
Open accessAbstract
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 feasibility and the stability 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.
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Recent work has integrated CBF constraints directly into model predictive control (MPC) formulations, allowing safety to be considered within a predictive optimization framework.
“To address these issues, recent work has integrated CBF constraints directly into model predictive control (MPC) formulations [6], allowing safety to be considered within a predictive optimization framework.”
From Where to Put Safety? Control Barrier Function Placement in Networked Control Systems (2026) · cites this paper as [6]
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Cited by
Sorted by citations
Where to Put Safety? Control Barrier Function Placement in Networked Control Systems
Beger, S., Chen, Y., & Hirche, S. (2026) · IEEE Control Systems Letters · doi:10.1109/lcsys.2026.3710212
- Authors
- Zeng, J., Zhang, B., & Sreenath, K.
- Affiliations
- University of California, Berkeley (US)
- Venue
- 2021 American Control Conference (ACC)
- Publisher
- IEEE
- Published
- 2021-05-25
- Pages
- 3882-3889
- ISSN
- 0743-1619 (ISSN-L)
- OpenAlex
- W3186317065
- Cited by
- 390
- References
- 0
- Citations by year
- 2026: 54 · 2025: 123 · 2024: 95 · 2023: 69 · 2022: 30 · 2021: 19
- FWCI
- 79.44
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the ... American Control Conference/Proceedings of the American Control Conference (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (CMMI-1931853)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Formal Methods in Verification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Keywords
- control barrier functions, set invariance, constraint satisfaction, stability analysis, feasibility study, obstacle avoidance, double integrator, robots
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