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

Manuel Schimmack; P. Mercorelli

doi:10.1109/CoDIT.2014.6996946

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

Standard

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

Harvard

Schimmack, M & Mercorelli, P 2014, Contemporary sinusoidal disturbance detection and nano parameters identification using data scaling based on Recursive Least Squares algorithms. in I Kacem, P Laroche & Z Roka (eds), Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014: Proceedings., 6996946, Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 510-515, 2nd International Conference on Control, Decision and Information Technologies - CoDIT 2014 , Metz, France, 03.11.14. https://doi.org/10.1109/CoDIT.2014.6996946

APA

Schimmack, M., & Mercorelli, P. (2014). Contemporary sinusoidal disturbance detection and nano parameters identification using data scaling based on Recursive Least Squares algorithms. In I. Kacem, P. Laroche, & Z. Roka (Eds.), Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014: Proceedings (pp. 510-515). Article 6996946 (Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CoDIT.2014.6996946

Vancouver

Schimmack M , Mercorelli P. Contemporary sinusoidal disturbance detection and nano parameters identification using data scaling based on Recursive Least Squares algorithms. In Kacem I, Laroche P, Roka Z, editors, Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014: Proceedings. 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). doi: 10.1109/CoDIT.2014.6996946

Bibtex

@inbook{5a58aa0dec1742c291db567b0ad5b6e0,

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

abstract = "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.",

keywords = "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, Control systems",

author = "Manuel Schimmack and P. Mercorelli",

note = "Export Date: 23 February 2015; 2nd International Conference on Control, Decision and Information Technologies - CoDIT 2014 , CoDIT 2014 ; Conference date: 03-11-2014 Through 05-11-2014",

year = "2014",

month = dec,

day = "23",

doi = "10.1109/CoDIT.2014.6996946",

language = "English",

isbn = "978-1-4799-6773-5",

series = "Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

pages = "510--515",

editor = "Imed Kacem and Pierre Laroche and Zsuzsanna Roka",

booktitle = "Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014",

address = "United States",

url = "http://codit2014.event.univ-lorraine.fr/, http://codit2014.event.univ-lorraine.fr/",

}

RIS

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 -

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