Experiment design for formal verification via stochastic optimal control

A measurement-based statistical verification approach is developed for systems with partly unknown dynamics. Grey-box systems, which are specified as a model class, are subject to identification experiments that enable accepting or rejecting system properties expressed as formulae in a linear-time logic with a given confidence. We employ a Bayesian framework for the computation of the confidence level and for the design of experiments to increase the confidence. The experiment design is formulated as a stochastic optimal control problem, which solvable via dynamic programming. Applied to linear control systems, this work enables efficient data-driven verification of partly-known dynamics with controllable non-determinism (inputs) and noisy output observations. A numerical case study concerning the safety of a dynamical system is used to elucidate this approach.