Scalable Underapproximative Verification of Stochastic LTI Systems using Convexity and Compactness

Self Cite

This report presents the results of a friendly competition for formal verification and policy synthesis of stochastic models. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2018. In this first edition, we present five benchmarks with different levels of complexities and stochastic flavours. We make use of six different tools and frameworks (in alphabetical order): Barrier Certificates, FAUST2, FIRM-GDTL, Modest, SDCPN modelling & MC simulation and SReachTools; and attempt to solve instances of the five different benchmark problems. Through these benchmarks, we capture a snapshot on the current state-of the art tools and frameworks within the stochastic modelling domain. We also present the challenges encountered within this domain and highlight future plans which will push forward the development of more tools and methodologies for performing formal verification and optimal policy synthesis of stochastic processes.

Section: Sdcpn Modelling and MC Simulationmentioning

confidence: 99%

ARCH-COMP18 Category Report: Stochastic Modelling

Abate

Blom

Cauchi

et al.

Self Cite

“…The dynamics and the safety constraints can be timevarying, and the disturbance may be Gaussian or non-Gaussian. It relies on approaches drawn from convex optimization, Fourier transforms, scenario-based optimization, and computational geometry for a grid-free and scalable computation of the stochastic reach sets as well as controller (open-loop, affine feedback, and set-based) synthesis [25,53,54,56,41]. The toolbox is available at https://unm-hscl.github.io/SReachTools/.…”

Section: Sreachtools [52]mentioning

confidence: 99%

“…In the chance-constrained approach, the convexity of the stochastic viability set is utilized to construct a polytopic innerapproximation via ray shooting [56]. For the easy of computation of the rays, we fixed x 3 [0] as it has slow dynamics.…”

Section: Stochastic Viability With Input Uncertainty But No Anaesthementioning

confidence: 99%

ARCH-COMP19 Category Report: Stochastic Modelling

Abate

Blom

Cauchi

et al.

This report presents the results of a friendly competition for formal verification and policy synthesis of stochastic models. It also introduces new benchmarks within this category, and recommends next steps for this category towards next year's edition of the competition. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Spring 2019.

“…The dynamics and the safety constraints can be time-varying, and the disturbance may be Gaussian or non-Gaussian. It relies on approaches drawn from convex optimization, Fourier transforms, scenario-based optimization, and computational geometry for a grid-free and scalable computation of the stochastic reach sets as well as controller (open-loop, affine feedback, and set-based) synthesis [28,75,76,79,56]. SReachTools is available at https://sreachtools.github.io.…”

Section: Participating Tools and Frameworkmentioning

confidence: 99%

ARCH-COMP20 Category Report: Stochastic Models

Abate¹,

Blom²,

Cauchi³

et al.

This report presents the results of a friendly competition for formal verification and policy synthesis of stochastic models. It also introduces new benchmarks within this category, and recommends next steps for this category towards next year’s edition of the competition. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Spring/Summer 2020.