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PRoTECT: Parallelized Construction of Safety Barrier Certificates for Nonlinear Polynomial Systems

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arxiv 2404.14804 v2 pith:4X3ER5VC submitted 2024-04-23 eess.SY cs.SY

Ben Wooding , Viacheslav Horbanov , Abolfazl Lavaei This is my paper · ORCID
classification eess.SYcs.SY
keywords systemsprotectbarriersafetycertificatesconstructioncontinuous-timedeterministic
verification ladder T0 review T1 audit T2 compute T3 formal
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We develop an open-source software tool, called PRoTECT, for the parallelized construction of safety barrier certificates (BCs) for nonlinear polynomial systems. This tool employs sum-of-squares (SOS) optimization programs to systematically search for polynomial-type BCs, while aiming to verify safety properties over four classes of dynamical systems: (i) discrete-time stochastic systems, (ii) discrete-time deterministic systems, (iii) continuous-time stochastic systems, and (iv) continuous-time deterministic systems. PRoTECT is implemented in Python as an application programming interface (API), offering users the flexibility to interact either through its user-friendly graphic user interface (GUI) or via function calls from other Python programs. PRoTECT leverages parallelism across different barrier degrees to efficiently search for a feasible BC.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Formal Methods to Data-Driven Safety Certificates of Unknown Large-Scale Networks

    eess.SY 2025-08 unverdicted novelty 6.0 of 10

    A decentralized scheme builds a control barrier certificate for an unknown large-scale network from a single noisy input-state trajectory per subsystem, with linear-in-subsystems complexity.

  2. Data-Efficient Control of Polynomial Systems via Physics-Guided Quadratic Constraints

    eess.SY 2025-08 conditional novelty 6.0 of 10

    A physics-guided quadratic constraint added to a sum-of-squares optimization lets a single short noisy trajectory certify robust safety for polynomial control systems, reducing data needs substantially.

  3. StochasticBarrier.jl: A Toolbox for Stochastic Barrier Function Synthesis

    eess.SY 2026-02 conditional novelty 5.0 of 10

    StochasticBarrier.jl synthesizes stochastic barrier functions for linear, polynomial, and piecewise-affine system models, and its benchmarks show large speedups over existing MATLAB/Python tools.

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