Data-driven reachability analysis of Lipschitz nonlinear systems via support vector data description
(2023) 2023 62nd IEEE Conference on Decision and Control (CDC) — Location: Singapore (13.December.2023)
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- Authors
Wang, Zheming 
- Abstract
- This paper is concerned with data-driven reachability analysis of discrete-time nonlinear systems without any dynamical model. We use only a number of observations of trajectories of the system to estimate the actual reachable set. With the data set, using the Support Vector Data Description (SVDD) technique, we propose a sample-based approximation method to solve the reachability analysis problem, which can be considered as a one-class classification problem. Under the framework of scenario optimization, we then derive over approximations of the reachable set in a probabilistic sense with Lipschitz continuity and other regularity conditions. Finally, we demonstrate the proposed method on a numerical example.
- Affiliations
Citations
Wang, Z., Chen, B., Jungers, R., & Yu, l. (2023). Data-driven reachability analysis of Lipschitz nonlinear systems via support vector data description. 2023 62nd IEEE Conference on Decision and Control (CDC), Singapore. https://hdl.handle.net/2078.5/252078