Self-triggered Control for Safety Critical Systems using Control Barrier Functions
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Abstract (arXiv)
We propose a real-time control strategy that combines self-triggered control with Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF). Similar to related works proposing CLF-CBF-based controllers, the computation of the controller is achieved by solving a Quadratic Program (QP). However, we propose a Zeroth-Order Hold (ZOH) implementation of the controller that overcomes the main limitations of traditional approaches based on periodic controllers, i.e., unnecessary controller updates and potential violations of the safety constraints. Central to our approach is the novel notion of safe period, which enforces a strong safety guarantee for implementing ZOH control. In addition, we prove that the system does not exhibit a Zeno behavior as it approaches the desired equilibrium.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Mar 2019.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 0; 2025: 1.
5 papers.
- 2021BarrierNet: A Safety-Guaranteed Layer for Neural Networkscited by 12
- 2022Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Drivingcited by 10
- 2022Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approachcited by 6
- 2023Learning Feasibility Constraints for Control Barrier Functionscited by 2
- 2025Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systemscited by 0
- 2025Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systems14 May 2025
- 2023Learning Feasibility Constraints for Control Barrier Functions10 Mar 2023
- 2022Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach3 Aug 2022
- 2022Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving4 Mar 2022
- 2021BarrierNet: A Safety-Guaranteed Layer for Neural Networks22 Nov 2021