Non-Asymptotic Identification of Linear Dynamical Systems Using Multiple Trajectories
نویسندگان
چکیده
This letter considers the problem of linear time-invariant (LTI) system identification using input/output data. Recent work has provided non-asymptotic results on partially observed LTI a single trajectory but is only suitable for stable systems. We provide finite-time analysis learning Markov parameters based ordinary least-squares (OLS) estimator multiple trajectories, which covers both and unstable For systems, our suggest that are harder to estimate in presence process noise. Without noise, upper bound estimation error independent spectral radius dynamics with high probability. These two features different from fully systems recent shown bigger easier estimate. Extensive numerical experiments demonstrate performance OLS estimator.
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ژورنال
عنوان ژورنال: IEEE Control Systems Letters
سال: 2021
ISSN: ['2475-1456']
DOI: https://doi.org/10.1109/lcsys.2020.3042924