Integral Control Barrier Functions for Dynamically Defined Control Laws
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Abstract (arXiv)
This paper introduces integral control barrier functions (I-CBFs) as a means to enable the safety-critical integral control of nonlinear systems. Importantly, I-CBFs allow for the holistic encoding of both state constraints and input bounds in a single framework. We demonstrate this by applying them to a dynamically defined tracking controller, thereby enforcing safety in state and input through a minimally invasive I-CBF controller framed as a quadratic program.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jun 2020.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 2.
6 papers.
- 2022On the Forward Invariance of Neural ODEscited by 10
- 2022Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approachcited by 6
- 2024Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuationcited by 4
- 2023Safe Neural Control for Non-Affine Control Systems with Differentiable Control Barrier Functionscited by 3
- 2023Learning Exactly Linearizable Deep Dynamics Modelscited by 3
- 2024Domain Adaptive Safety Filters via Deep Operator Learningcited by 1
- 2024Domain Adaptive Safety Filters via Deep Operator Learning18 Oct 2024
- 2024Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuation11 Jan 2024
- 2023Learning Exactly Linearizable Deep Dynamics Models30 Nov 2023
- 2023Safe Neural Control for Non-Affine Control Systems with Differentiable Control Barrier Functions6 Sep 2023
- 2022On the Forward Invariance of Neural ODEs10 Oct 2022
- 2022Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach3 Aug 2022