mlsanityData to 7 Oct 2026

Formal Synthesis of Stochastic Systems via Control Barrier Certificates

arXiv 1905.04585 ↗First posted 11 May 2019eess.SY cs.SY math.OCcited by 21

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Abstract (arXiv)

This paper focuses on synthesizing control policies for discrete-time stochastic control systems together with a lower bound on the probability that the systems satisfy the complex temporal properties. The desired properties of the system are expressed as linear temporal logic (LTL) specifications over finite traces. In particular, our approach decomposes the given specification into simpler reachability tasks based on its automata representation. We then propose the use of so-called control barrier certificate to solve those simpler reachability tasks along with computing the corresponding controllers and probability bounds. Finally, we combine those controllers to obtain a hybrid control policy solving the considered problem. Under some assumptions, we also provide two systematic approaches for uncountable and finite input sets to search for control barrier certificates. We demonstrate the effectiveness of the proposed approach on a room temperature control and lane-keeping of a vehicle modeled as a four-dimensional single-track kinematic model. We compare our results with the discretization-based methods in the literature.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 8; 2025: 6; 2026: 3.

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  1. 2022Safety Certification for Stochastic Systems via Neural Barrier Functionscited by 22
  2. 2024Data-Driven Distributionally Robust Safety Verification Using Barrier Certificates and Conditional Mean Embeddingscited by 9
  3. 2024Error Bounds For Gaussian Process Regression Under Bounded Support Noise With Applications To Safety Certificationcited by 6
  4. 2025Two-Stage Learning of Stabilizing Neural Controllers via Zubov Sampling and Iterative Domain Expansioncited by 6
  5. 2021Correct-by-construction reach-avoid control of partially observable linear stochastic systemscited by 5
  6. 2021Formal Verification of Unknown Dynamical Systems via Gaussian Process Regressioncited by 5
  7. 2025CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functionscited by 4
  8. 2024Data-Driven Permissible Safe Control with Barrier Certificatescited by 3
  9. 2024Policy Verification in Stochastic Dynamical Systems Using Logarithmic Neural Certificatescited by 3
  10. 2024Data-Driven Abstractions via Binary-Tree Gaussian Processes for Formal Verificationcited by 3
  11. 2024Learning-Based Shielding for Safe Autonomy under Unknown Dynamicscited by 3
  12. 2025Safety Certification in the Latent space using Control Barrier Functions and World Modelscited by 2
9 more
  1. 2021Safety-Critical Learning of Robot Control with Temporal Logic Specificationscited by 1
  2. 2024Transfer of Safety Controllers Through Learning Deep Inverse Dynamics Modelcited by 1
  3. 2025Data-Driven Safety Verification using Barrier Certificates and Matrix Zonotopescited by 1
  4. 2026Learning Contextual Runtime Monitors for Safe AI-Based Autonomycited by 1
  5. 2026Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorialcited by 1
  6. 2024Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attackscited by 0
  7. 2025Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectivescited by 0
  8. 2025BarrierBench: Evaluating Large Language Models for Safety Verification in Dynamical Systemscited by 0
  9. 2026Kernel-Based Learning of Safety Barrierscited by 0