Comparing data scaling based recursive least squares algorithms with Kalman Filter for nano parameters identification
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Comparing data scaling based recursive least squares algorithms with Kalman Filter for nano parameters identification. / Schimmack, Manuel; Mercorelli, Paolo; Georgiadis, Anthimos.
Proceedings of 2014 International Conference on Modelling, Identification and Control, ICMIC 2014: ICMIC 2014 Melbourne, Australia December 3rd - 5th , 2014; Proceedings . IEEE - Institute of Electrical and Electronics Engineers Inc., 2015. S. 316 - 321 7020772 (Proceedings of 2014 International Conference on Modelling, Identification and Control, ICMIC 2014).Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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, Melbourne, Australien, 03.12.14. https://doi.org/10.1109/ICMIC.2014.7020772
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TY - CHAP
T1 - Comparing data scaling based recursive least squares algorithms with Kalman Filter for nano parameters identification
AU - Schimmack, Manuel
AU - Mercorelli, Paolo
AU - Georgiadis, Anthimos
PY - 2015/1/23
Y1 - 2015/1/23
N2 - This paper considers a single-input and single-output (SISO) controlled autoregressive moving average system by using scalar factors of the input-output data. A general identification technique, through scaling data, is obtained. To obtain this data, Recursive Least Squares (RLS) methods are used to estimate the nano parameters of a linear model using input-output scaling factors. Different variations of the RLS method are tested and compared. The first RLS method uses a forgetting factor and the second method is integrated with a Kalman Filter covariance. In order to estimate the parameters in the nano range, the input signal requires a very high frequency and thus a very high sampling rate is required. Although using this proposed technique, a broader sampling rate and an input signal with low frequency can be used to identify the nano parameters characterizing the linear model. The simulation results indicate that the proposed algorithm is effective and robust. The main contribution of this work is to provide a scaled identification bandwidth and sampling rate of the detecting signal in the identification process.
AB - This paper considers a single-input and single-output (SISO) controlled autoregressive moving average system by using scalar factors of the input-output data. A general identification technique, through scaling data, is obtained. To obtain this data, Recursive Least Squares (RLS) methods are used to estimate the nano parameters of a linear model using input-output scaling factors. Different variations of the RLS method are tested and compared. The first RLS method uses a forgetting factor and the second method is integrated with a Kalman Filter covariance. In order to estimate the parameters in the nano range, the input signal requires a very high frequency and thus a very high sampling rate is required. Although using this proposed technique, a broader sampling rate and an input signal with low frequency can be used to identify the nano parameters characterizing the linear model. The simulation results indicate that the proposed algorithm is effective and robust. The main contribution of this work is to provide a scaled identification bandwidth and sampling rate of the detecting signal in the identification process.
KW - Engineering
KW - Control systems
KW - Control systems
KW - Kalman Filter
KW - Least Squares Methods
KW - Parameters Identification
UR - http://www.scopus.com/inward/record.url?scp=84923402142&partnerID=8YFLogxK
U2 - 10.1109/ICMIC.2014.7020772
DO - 10.1109/ICMIC.2014.7020772
M3 - Article in conference proceedings
SN - 978-0-9567157-4-6
T3 - Proceedings of 2014 International Conference on Modelling, Identification and Control, ICMIC 2014
SP - 316
EP - 321
BT - Proceedings of 2014 International Conference on Modelling, Identification and Control, ICMIC 2014
PB - IEEE - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th Institute of Electrical and Electronics Engineers International Conference on Modelling, Identification and Control - 2014
Y2 - 3 December 2014 through 5 December 2014
ER -