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

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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.
Original languageEnglish
Title of host publicationProceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014 : Proceedings
EditorsImed Kacem, Pierre Laroche, Zsuzsanna Roka
Number of pages6
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Publication date23.12.2014
Pages510-515
Article number6996946
ISBN (print)978-1-4799-6773-5
ISBN (electronic)9781479967735
DOIs
Publication statusPublished - 23.12.2014
Event2nd International Conference on Control, Decision and Information Technologies - CoDIT 2014 - University of Lorraine , Metz, France
Duration: 03.11.201405.11.2014
Conference number: 2
http://codit2014.event.univ-lorraine.fr/
http://codit2014.event.univ-lorraine.fr/

Bibliographical note

Export Date: 23 February 2015

    Research areas

  • Engineering - Control systems, Algorithms, Identification (control systems), Least squares approximations, Linear systems, Position control, Signal detection, Signal sampling, Autoregressive moving average, External disturbances, Identification techniques, Parameters identification, Recursive least square (RLS), Recursive least squares algorithms, Single input and single outputs, Sinusoidal disturbances