Reach-Avoid Analysis for Polynomial Stochastic Differential Equations
Bai Xue, Naijun Zhan, Martin Fr\"anzle

TL;DR
This paper introduces a semi-definite programming method for probabilistic reach-avoid analysis of polynomial stochastic differential equations, enabling efficient safety verification over open time horizons.
Contribution
It presents a novel convex optimization approach using sum-of-squares decomposition to solve reach-avoid problems for stochastic systems with polynomial dynamics.
Findings
Efficient polynomial-time solution for probabilistic reach-avoid problems.
Extension to classical safety verification and barrier certificates.
Demonstrated effectiveness through multiple examples.
Abstract
In this paper we propose a novel semi-definite programming approach that solves reach-avoid problems over open (i.e., not bounded a priori) time horizons for dynamical systems modeled by polynomial stochastic differential equations. The reach-avoid problem in this paper is a probabilistic guarantee: we approximate from the inner a -reach-avoid set, i.e., the set of initial states guaranteeing with probability larger than that the system eventually enters a given target set while remaining inside a specified safe set till the target hit. Our approach begins with the construction of a bounded value function, whose strict super-level set is equal to the -reach-avoid set. This value function is then reduced to a twice continuously differentiable solution to a system of equations. The system of equations facilitates the construction of a semi-definite program using…
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Taxonomy
TopicsFormal Methods in Verification · Software Reliability and Analysis Research · Probabilistic and Robust Engineering Design
