Control Barrier Functions: Theory and Applications
By Aaron D. Ames, Samuel D. Coogan, Magnus Egerstedt, Gennaro Notomista, Koushil Sreenath, Paulo Tabuada. European Control Conference, 2019
2,429 citations recorded by the source corpus.
This paper provides an introduction and overview of recent work on control barrier functions and their use to verify and enforce safety properties in the context of (optimization based) safety-critical controllers. We survey the main technical results and discuss applications to several domains including robotic systems.
Subfields: Computational Theory and Mathematics, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/2a9fd99d95d857b4d9283ea80cb6f31f72569bc6
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In Computational Theory and Mathematics
In Control and Systems Engineering
Citations in the corpus
- Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function (2020, positive): “Futhermore, a safety-critical control design for continuous-time systems was proposed by unifying a control Lyapunov function (CLF) and a control barrier function through a quadratic program (CLF-CBF-QP) [2], [15].”
- Safety Alignment Should Be Made More Than Just a Few Tokens Deep (2024, positive): “Our data augmentation approach in Section 3 relates to exploration requirements for optimal learning via policy gradient methods (Agarwal et al., 2021), learning recovery policies (Thananjeyan et al., 2021), and safe control theory (Burridge et al., 1999; Ames et al., 2019).”
- A Review of Safe Reinforcement Learning: Methods, Theory and Applications (2022, methodology): “Different from Wang’s method [288] using CBF, Cao et al. [49] improve the safety of autonomous driving in low-level decision making settings by integrating a rule-based policy, e.g. a Gipps car-following model [95], into RL framework, and a monte carlo tree searching method [44] is used to generate ”
- A Review of Safe Reinforcement Learning: Methods, Theories, and Applications (2024, positive): “Based on the work [58], Wang [63] presents a low-level decision-making method via a safety layer of CBFs [211], and a legal safe control method by following traf-fic rules to ensure motion planning safety for autonomous driving in highway scenarios.”