mlsanityData to 7 Oct 2026

Differentiable Safe Controller Design through Control Barrier Functions

arXiv 2209.10034 ↗First posted 20 Sep 2022eess.SY cs.LG cs.SYcited by 8

Not on the map. Why?

Abstract (arXiv)

Learning-based controllers, such as neural network (NN) controllers, can show high empirical performance but lack formal safety guarantees. To address this issue, control barrier functions (CBFs) have been applied as a safety filter to monitor and modify the outputs of learning-based controllers in order to guarantee the safety of the closed-loop system. However, such modification can be myopic with unpredictable long-term effects. In this work, we propose a safe-by-construction NN controller which employs differentiable CBF-based safety layers, and investigate the performance of safe-by-construction NN controllers in learning-based control. Specifically, two formulations of controllers are compared: one is projection-based and the other relies on our proposed set-theoretic parameterization. Both methods demonstrate improved closed-loop performance over using CBF as a separate safety filter in numerical experiments.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 26 of its 32 references that are in the map. First posted on arXiv 20 Sep 2022.

Builds on

26 of 32 references.

References · 26

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14 more
  1. 2020Safe Optimal Control Using Stochastic Barrier Functions and Deep Forward-Backward SDEscited by 22
  2. 2021Computationally Efficient Safe Reinforcement Learning for Power Systemscited by 17
  3. 2021Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approachcited by 12
  4. 2021BarrierNet: A Safety-Guaranteed Layer for Neural Networkscited by 11
  5. 2020Learning Constrained Adaptive Differentiable Predictive Control Policies With Guaranteescited by 10
  6. 2022Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environmentscited by 8
  7. 2021Recursive Feasibility Guided Optimal Parameter Adaptation of Differential Convex Optimization Policies for Safety-Critical Systemscited by 7
  8. 2022Safe and Efficient Model Predictive Control Using Neural Networks: An Interior Point Approachcited by 7
  9. 2020Approximate Optimal Control for Safety-Critical Systems with Control Barrier Functionscited by 6
  10. 2021Safety-Critical Control using Optimal-decay Control Barrier Function with Guaranteed Point-wise Feasibilitycited by 6
  11. 2022Neural Lyapunov Differentiable Predictive Controlcited by 6
  12. 2022Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approachcited by 6
  13. 2022Feasibility Guaranteed Traffic Merging Control Using Control Barrier Functionscited by 2
  14. 2022Explicit Solutions for Safety Problems Using Control Barrier Functionscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 1; 2025: 2; 2026: 1.

8 papers.

  1. 2023Empowering Autonomous Driving with Large Language Models: A Safety Perspectivecited by 34
  2. 2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systemscited by 28
  3. 2023Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Predictioncited by 15
  4. 2024Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agentscited by 11
  5. 2023Learning Adaptive Safety for Multi-Agent Systemscited by 3
  6. 2025Deep Fictitious Play-Based Potential Differential Games for Learning Human-Like Interaction at Unsignalized Intersectionscited by 0
  7. 2025Tail-Safe Hedging: Explainable Risk-Sensitive Reinforcement Learning with a White-Box CBF--QP Safety Layer in Arbitrage-Free Marketscited by 0
  8. 2026Topological Feasibility Guarantees for Differentiable Predictive Controlcited by 0