mlsanityData to 7 Oct 2026

Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions

arXiv 2109.06697 ↗First posted 14 Sep 2021eess.SY cs.RO cs.SYcited by 91

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Abstract (arXiv)

Safety and stability are common requirements for robotic control systems; however, designing safe, stable controllers remains difficult for nonlinear and uncertain models. We develop a model-based learning approach to synthesize robust feedback controllers with safety and stability guarantees. We take inspiration from robust convex optimization and Lyapunov theory to define robust control Lyapunov barrier functions that generalize despite model uncertainty. We demonstrate our approach in simulation on problems including car trajectory tracking, nonlinear control with obstacle avoidance, satellite rendezvous with safety constraints, and flight control with a learned ground effect model. Simulation results show that our approach yields controllers that match or exceed the capabilities of robust MPC while reducing computational costs by an order of magnitude.

Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Sep 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 14; 2023: 23; 2024: 26; 2025: 19; 2026: 7.

91 papers.

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  1. 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systemscited by 77
  2. 2022Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environmentscited by 54
  3. 2022Reachability Constrained Reinforcement Learningcited by 52
  4. 2022Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guaranteescited by 39
  5. 2023Empowering Autonomous Driving with Large Language Models: A Safety Perspectivecited by 34
  6. 2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systemscited by 28
  7. 2022Safe Control Under Input Limits with Neural Control Barrier Functionscited by 27
  8. 2024Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulationcited by 23
  9. 2022Safety Certification for Stochastic Systems via Neural Barrier Functionscited by 22
  10. 2023A Survey on Physics Informed Reinforcement Learning: Review and Open Problemscited by 22
  11. 2025Steering Dialogue Dynamics for Robustness against Multi-turn Jailbreaking Attackscited by 20
  12. 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrationscited by 19
38 more
  1. 2021Lyapunov-Net: A Deep Neural Network Architecture for Lyapunov Function Approximationcited by 16
  2. 2022Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificatecited by 16
  3. 2023AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learningcited by 16
  4. 2023In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution Statescited by 15
  5. 2024Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificatescited by 15
  6. 2023Exact Verification of ReLU Neural Control Barrier Functionscited by 14
  7. 2024Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagationcited by 12
  8. 2025A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systemscited by 12
  9. 2022Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specificationscited by 11
  10. 2022Joint Differentiable Optimization and Verification for Certified Reinforcement Learningcited by 11
  11. 2023Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic Approachcited by 11
  12. 2022Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrationscited by 10
  13. 2023Online Control Barrier Functions for Decentralized Multi-Agent Navigationcited by 10
  14. 2023Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functionscited by 9
  15. 2023State-Wise Safe Reinforcement Learning With Pixel Observationscited by 9
  16. 2022Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environmentscited by 8
  17. 2022Differentiable Safe Controller Design through Control Barrier Functionscited by 8
  18. 2022Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigationcited by 8
  19. 2023Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidancecited by 8
  20. 2023Task-Oriented Koopman-Based Control with Contrastive Encodercited by 8
  21. 2024Provably Safe Neural Network Controllers via Differential Dynamic Logiccited by 7
  22. 2024Human-AI Safety: A Descendant of Generative AI and Control Systems Safetycited by 7
  23. 2024Verified Safe Reinforcement Learning for Neural Network Dynamic Modelscited by 7
  24. 2025Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Controlcited by 7
  25. 2026Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPUcited by 7
  26. 2024Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddingscited by 6
  27. 2024Neural Model Checkingcited by 6
  28. 2025Two-Stage Learning of Stabilizing Neural Controllers via Zubov Sampling and Iterative Domain Expansioncited by 6
  29. 2023Learning Control-Oriented Dynamical Structure from Datacited by 5
  30. 2024MAGICS: Adversarial RL with Minimax Actors Guided by Implicit Critic Stackelberg for Convergent Neural Synthesis of Robot Safetycited by 5
  31. 2024RAIL: Reachability-Aided Imitation Learning for Safe Policy Executioncited by 5
  32. 2025V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functionscited by 5
  33. 2024Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuationcited by 4
  34. 2024Safe and Reliable Training of Learning-Based Aerospace Controllerscited by 4
  35. 2024Neural Continuous-Time Supermartingale Certificatescited by 4
  36. 2025CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functionscited by 4
  37. 2025Learning Conservative Neural Control Barrier Functions from Offline Datacited by 4
  38. 2022Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Modelscited by 3

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