NDLI: Optimal Non-Parametric System Identification From Arbitrary Corrupt Finite Time Series: A Control-Oriented Approach

Content Provider	IEEE Xplore Digital Library
Author	Chen, Jie Nett, Carl N. Fan, Michael K. H.
Copyright Year	1992
Description	Author affiliation: Assistant Professor and Director, Computer-Aided Control Systems Engineering Laboratory (CACSEL), School of Electrical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, (404) 853-9828, eefacmf@prism.gatech.edu (Fan, Michael K. H.) \|\| Postdoctoral Fellow, Schools of Aerospace and Electrical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, (404) 853-0173, chen@licchusl.gatech.edu (Chen, Jie) \|\| Associate Professor and Director, Laboratory for Identification and Control of Complex Highly Uncertain Systems (LICCHUS), School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150, (404) 898-8200, cn4@prism.gatech.edu (Nett, Carl N.)
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 apriori 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 aposteriori 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 maps the available apriori and aposteriori information into an "uncertain model" of the plant. The resulting uncertain plant model is comprised of a nominal plant model, a bounded additive output noise, and a bounded additive model uncertainty. The upper bound on the model uncertainty is explicit, worst-case/deterministic in nature, and expressed in terms of both the l1 and H¿ system norms. Under the assumption that the available apriori information is "correct" for the underlying physical plant, the resulting uncertain plant model has the property that it not only "explains" the available aposteriori information, but will also explain all aposteniori information observed in the future. Because this property hinges on the correctness of the available apriori information, a method is also presented for developing confidence that the available apriori information is in fact correct.
Starting Page	279
Ending Page	285
File Size	933439
Page Count	7
File Format	PDF
ISBN	0780302109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1992-06-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	System identification Optimal control Control systems Additive noise Robust control Uncertainty Noise level Aerospace engineering Upper bound Fasteners
Content Type	Text
Resource Type	Article

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