Poster Abstract: Data-Driven Correct-by-Design Control of Parametric Stochastic Systems

In this ongoing work, we address data-driven computation of controllers that are correct by design for safety-critical systems and can provably satisfy complex functional requirements. We propose a two-stage approach that decomposes the problem into a data-driven stage and a robust formal controller synthesis stage. The first stage utilizes available Bayesian linear regression methods to compute robust confidence sets for the true parameters of the system. The second stage develops methods for systems subject to both stochastic and parametric uncertainties. We provide simulation relations for enabling control refinement that are founded on coupling uncertainties of stochastic systems via sub-probability measures. Such relations are essential for constructing abstract models that are related to not only one model but to a set of parametric models.