Data-Driven Reachability Analysis Using Matrix Zonotopes
This addresses safety verification in control systems where models are unavailable, offering a practical solution for engineers, though it appears incremental as it builds on existing zonotope methods.
The paper tackles the problem of reachability analysis for unknown system dynamics by proposing a data-driven approach using matrix zonotopes, providing theoretical guarantees for linear time-invariant and Lipschitz nonlinear systems, with numerical examples demonstrating applicability.
In this paper, we propose a data-driven reachability analysis approach for unknown system dynamics. Reachability analysis is an essential tool for guaranteeing safety properties. However, most current reachability analysis heavily relies on the existence of a suitable system model, which is often not directly available in practice. We instead propose a data-driven reachability analysis approach from noisy data. More specifically, we first provide an algorithm for over-approximating the reachable set of a linear time-invariant system using matrix zonotopes. Then we introduce an extension for Lipschitz nonlinear systems. We provide theoretical guarantees in both cases. Numerical examples show the potential and applicability of the introduced methods.