WSEAS Transactions on Systems


Print ISSN: 1109-2777
E-ISSN: 2224-2678

Volume 18, 2019

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Volume 18, 2019


Least Square Identification using Noise Reduction Disturbance Observer

AUTHORS: Jesus U. Liceaga-Castro, Irma I. Siller-Alcala, Roberto A. Alcantara

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ABSTRACT: - In this article, the application of the identification algorithm of Recursive Least Squares with Forgetting Factor in conjunction with the Noise Reduction Disturbance Observer shows that the effects of noise, which affects input and output signals of the process, can be reduced so that the identification process can be more effective and precise. In order to evaluate the effectiveness of this strategy the results of a case of study in which estimation of a first order process using the Noise Reduction Disturbance Observer is compared to an estimation without the Disturbance Observer.

KEYWORDS: Identification, Least Square, Noise Reduction Disturbance Observer.

REFERENCES:

[1] Ioan D. Landau and Gianluca Zito, (2006). “Digital Control Systems. Design, Identification and Implementation”, Springer, ISBN 978-1- 84628-056-6

[2] G. Imbens and J. Angrist, (1994). “Identification and estimation of local average treatment effects”. Econometrica 62 (2): 467- 476. JSTOR 2951620I.

[3] Wei Xie, (2010). “High frequency measurement noise rejection based on disturbance observer”. Journal of the Franklin Institute 347 pp. 1825– 1836

[4] Nam H. Jo and Hyungbo Shim, (2013), “Robust Stabilization via Disturbance Observer with Noise Reduction”. European Control Conference (ECC) Zürich, Switzerland.

[5] Emre Sariyildiz and Kouhei Ohnishi, (2015). “Stability and Robustness of DisturbanceObserver-Based Motion Control Systems”. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 62, NO. 1, JANUARY 2015

[6] Hyungbo SHIM, Gyunghoon PARK, Youngjun JOO, Juhoon BACK, Nam Hoon JO, (2016). “Yet Another Tutorial of Disturbance Observer: Robust Stabilization and Recovery of Nominal Performance Control”. Theory_Tech, Vol.14, No.3, pp.237–249, August 2016 Control Theory and Technology, http://link.springer.com/journal/11768

[7] Noriaki Hirose, Ryosuke Tajima, Nagisa Koyama, Kazutoshi Sukigara, Minoru Tanaka. (2016). “Following control approach based on model predictive control for wheeled inverted pendulum robot”. Advanced Robotics 30:6, pages 374-385.

[8] Graham C. Goodwin and Kwai Sang Sin, (2009). “Adaptive Filtering Prediction and Control” Dover Publications, Inc. New York, NY, USA ©2009 ISBN:0486469328 9780486469324

WSEAS Transactions on Systems, ISSN / E-ISSN: 1109-2777 / 2224-2678, Volume 18, 2019, Art. #39, pp. 313-318


Copyright Β© 2018 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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