📝 SummarXiv

Abstract

Reachability analysis is a key formal verification technique for ensuring the safety of modern cyber physical systems subject to uncertainties in measurements, system models (parameters), and inputs. Classical model-based approaches rely on accurate prior knowledge of system dynamics, which may not always be available or reliable. To address this, we present a data-driven reachability analysis framework that computes over-approximations of reachable sets directly from online state measurements. The method estimates time-varying unknown models using an Exponentially Forgetting Zonotopic Recursive Least Squares (EF ZRLS) method, which processes data corrupted by bounded noise. Specifically, a time-varying set of models that contains the true model of the system is estimated recursively, and then used to compute the forward reachable sets under process noise and uncertain inputs. Our approach applies to both discrete-time Linear Time Varying (LTV) and nonlinear Lipschitz systems. Compared to existing techniques, it produces less conservative reachable set over approximations, remains robust under slowly varying dynamics, and operates solely on real-time data without requiring any pre-recorded offline experiments. Numerical simulations and real-world experiments validate the effectiveness and practical applicability of the proposed algorithms.