Contemporary sinusoidal disturbance detection and nano parameters identification using data scaling based on Recursive Least Squares algorithms
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Contemporary sinusoidal disturbance detection and nano parameters identification using data scaling based on Recursive Least Squares algorithms. / Schimmack, Manuel; Mercorelli, P.
Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014: Proceedings. ed. / Imed Kacem; Pierre Laroche; Zsuzsanna Roka. IEEE - Institute of Electrical and Electronics Engineers Inc., 2014. p. 510-515 6996946 (Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Contemporary sinusoidal disturbance detection and nano parameters identification using data scaling based on Recursive Least Squares algorithms
AU - Schimmack, Manuel
AU - Mercorelli, P.
N1 - Conference code: 2
PY - 2014/12/23
Y1 - 2014/12/23
N2 - 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.
AB - 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.
KW - Engineering
KW - Control systems
KW - Algorithms
KW - Identification (control systems)
KW - Least squares approximations
KW - Linear systems
KW - Position control
KW - Signal detection
KW - Signal sampling
KW - Autoregressive moving average
KW - External disturbances
KW - Identification techniques
KW - Parameters identification
KW - Recursive least square (RLS)
KW - Recursive least squares algorithms
KW - Single input and single outputs
KW - Sinusoidal disturbances
KW - Control systems
UR - http://www.scopus.com/inward/record.url?scp=84921341906&partnerID=8YFLogxK
U2 - 10.1109/CoDIT.2014.6996946
DO - 10.1109/CoDIT.2014.6996946
M3 - Article in conference proceedings
SN - 978-1-4799-6773-5
T3 - Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014
SP - 510
EP - 515
BT - Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014
A2 - Kacem, Imed
A2 - Laroche, Pierre
A2 - Roka, Zsuzsanna
PB - IEEE - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Control, Decision and Information Technologies - CoDIT 2014
Y2 - 3 November 2014 through 5 November 2014
ER -