SReachTools: A MATLAB stochastic reachability toolbox

Vinod, Abraham P.; Gleason, Joseph D.; Oishi, Meeko

doi:10.1145/3302504.3313352

Cited by 28 publications

(44 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3(a). We compared the RKHS solution for the Gaussian distribution with a dynamic programming solution implemented in [37]. The absolute error (26) ( Fig.…”

Section: A N-d Stochastic Chain Of Integratorsmentioning

confidence: 99%

Model-Free Stochastic Reachability Using Kernel Distribution Embeddings

Thorpe

Oishi

2020

IEEE Control Syst. Lett.

Self Cite

View full text Add to dashboard Cite

We present a data-driven solution to the terminalhitting stochastic reachability problem for a Markov control process. We employ a nonparametric representation of the stochastic kernel as a conditional distribution embedding within a reproducing kernel Hilbert space (RKHS). This representation avoids intractable integrals in the dynamic recursion of the stochastic reachability problem since the expectations can be calculated as an inner product within the RKHS. We demonstrate this approach on a high-dimensional chain of integrators and on Clohessy-Wiltshire-Hill dynamics.

show abstract

“…3(a). We compared the RKHS solution for the Gaussian distribution with a dynamic programming solution implemented in [37]. The absolute error (26) ( Fig.…”

Section: A N-d Stochastic Chain Of Integratorsmentioning

confidence: 99%

Model-Free Stochastic Reachability Using Kernel Distribution Embeddings

Thorpe

Oishi

2020

IEEE Control Syst. Lett.

Self Cite

View full text Add to dashboard Cite

show abstract

“…We use SReachTools [22] to set up the problems, CharFunTool for the proposed FT approach [21], and CVX [23] for the CVaR approach. We presume w as a two-dimensional exponential random vector with mean µ w = [2 4] ⊤ , and that a = [1 1] ⊤ , b = 5, and c = −[1 1] ⊤ .…”

Section: Numerical Examplementioning

confidence: 99%

Sampling-free enforcement of non-gaussian chance constraints via fourier transforms

Vinod

Sivaramakrishnan²,

Oishi³

2019

Proceedings of the Fifth International Workshop on Symbolic-Numeric Methods for Reasoning About CPS and IoT

Self Cite

View full text Add to dashboard Cite

Verification of stochastic dynamical systems can often be formulated as chance-constrained optimization problems -maximize probability of satisfaction of safety/reachability objectives subject to dynamics and control bounds. For linear systems perturbed by Gaussian noise, chance-constraint techniques have proven to be highly efficient. With the goal of extending this approach to non-Gaussian disturbances, this short paper focuses on tractable approaches to enforce chance constraints involving non-Gaussian random vectors. After reviewing existing techniques, we propose a novel approach to enforce chance constraints for arbitrary disturbances using Fourier transforms that is sampling-free and provides tight approximations. We demonstrate the efficiency of our approach in a simple example. CCS CONCEPTS• Theory of computation → Stochastic control and optimization; Convex optimization; • Computing methodologies → Control methods; Computational control theory;

show abstract

“…SReachTools SReachTools [74] is an open-source, repeatability-evaluated, MATLAB toolbox for tackling the problem of stochastic reachability of a target tube [78]. This problem subsumes terminal hitting-time stochastic reach-avoid and stochastic viability problems (guaranteeing safety in stochastic systems) [72,3].…”

Section: Participating Tools and Frameworkmentioning

confidence: 99%

ARCH-COMP20 Category Report: Stochastic Models

Abate¹,

Blom²,

Cauchi³

et al.

EPiC Series in Computing

View full text Add to dashboard Cite

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.

show abstract

SReachTools: A MATLAB stochastic reachability toolbox

Cited by 28 publications

References 3 publications

Model-Free Stochastic Reachability Using Kernel Distribution Embeddings

Model-Free Stochastic Reachability Using Kernel Distribution Embeddings

Sampling-free enforcement of non-gaussian chance constraints via fourier transforms

ARCH-COMP20 Category Report: Stochastic Models

Contact Info

Product

Resources

About