Continuous-time model identification from filtered sampled data: error analysis

Title: Continuous-time model identification from filtered sampled data: error analysis
Creator: Hu, Xiao-Li; Welsh, James S.
Relation: IEEE Transactions on Automatic Control Vol. 65, Issue 10, p. 4005-4015
Publisher Link: http://dx.doi.org/10.1109/TAC.2020.3006354
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Resource Type: journal article
Date: 2020
Description: In this article, an upper bound is established for the estimation error of a standard least squares (LS) algorithm used to identify a continuous-time model from filtered, sampled input-output data. It is found that the error has three constituent components due to the initial conditions, observation noise, and sample period. In particular, the initial condition bias is bounded by O(1/[NΔt]), which requires sufficiently large [NΔt] for accurate LS estimation. The theoretical results obtained are confirmed by simulation.
Subject: continuous-time model; system identification; sampled data; least squares (LS)
Identifier: http://hdl.handle.net/1959.13/1430984
Identifier: uon:38907
Identifier: ISSN:0018-9286
Language: eng
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