Abstract
In this paper we formulate and solve a control-oriented system identification problem for single-input, single-output, linear, shift-invariant, distributed parameter plants. In this problem the available a priori information is minimal, consisting only of worst-case/deterministic, time dependent, upper and lower bounds on the plant impulse response and the additive output noise. The available a posteriori information consists of a corrupt finite output time series obtained in response to a known, non-zero but otherwise arbitrary, applied input. A novel system identification method is presented for this problem. This method yields an “uncertain model” of the plant, which is comprised of a nominal plant model, a bounded additive output noise, and a bounded additive model uncertainty measured in both the l 1 and H ∞ system norms. Both the identification method and the resulting uncertain model are computationally simple and possess certain desirable properties.
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© 1994 Springer-Verlag
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Chen, J., Nett, C.N., Fan, M.K.H. (1994). A worst case identification method using time series data. In: Smith, R.S., Dahleh, M. (eds) The Modeling of Uncertainty in Control Systems. Lecture Notes in Control and Information Sciences, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0036253
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DOI: https://doi.org/10.1007/BFb0036253
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