Contemporary sinusoidal disturbance detection and nano parameters identification using data scaling based on Recursive Least Squares algorithms

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

Single-input and single-output (SISO) controlled autoregressive moving average system by using a scalar factor input-output data is considered. Through data scaling, a simple identification technique is obtained. Using input-output scaling factors a data Recursive Least Squares (RLS) method for estimating the parameters of a linear model and contemporary sinusoidal disturbance detection is deduced. For estimating parameters of a model in nano range a very high frequency input signal with a very small sampling rate is needed. The main contribution of this work consists of the use of a scaled Recursive Least Square with a forgetting factor. Using this proposed technique, a low input signal frequency and a wider sampling rate can be used to identify the parameters. In the meantime, the scaling technique reduces the effect of the external disturbance so that RLS can be applied to identify the disturbance without considering a model of it. The proposed technique is quite general and can be applied to any kind of linear systems. The simulation results indicate that the proposed algorithm is effective.
OriginalspracheEnglisch
TitelProceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014 : Proceedings
HerausgeberImed Kacem, Pierre Laroche, Zsuzsanna Roka
Anzahl der Seiten6
VerlagIEEE - Institute of Electrical and Electronics Engineers Inc.
Erscheinungsdatum23.12.2014
Seiten510-515
Aufsatznummer6996946
ISBN (Print)978-1-4799-6773-5
ISBN (elektronisch)9781479967735
DOIs
PublikationsstatusErschienen - 23.12.2014
Veranstaltung2nd International Conference on Control, Decision and Information Technologies - CoDIT 2014 - University of Lorraine , Metz, Frankreich
Dauer: 03.11.201405.11.2014
Konferenznummer: 2
http://codit2014.event.univ-lorraine.fr/
http://codit2014.event.univ-lorraine.fr/

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