A Higher-Order Spectra Based Method of On-Line Secondary Path Model Identification for Active Noise Control Systems

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Abstract:

The aim of this paper is to present a method of nonparametric and parametric secondary path model identification for adaptive active noise control systems with low-power non-Gaussian excitations of the form of a higher-order discrete-time multisine random process and data processing based on cross-higher-order spectra. Properties of the discussed method are illustrated by simulation experiments devoted to secondary path identification for feedforward and feedback active noise control systems. Its robustness to nonlinear distortions implied by data acquisition system and adaptation procedure is proved.

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Solid State Phenomena (Volume 248)

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3-10

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March 2016

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© 2016 Trans Tech Publications Ltd. All Rights Reserved

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[1] C.H. Hansen, S.D. Snyder, Active Control of Noise and Vibration, Cambridge University Press, (1997).

Google Scholar

[2] Y.L. Zhou, Q.Z. Zhang, X. Dong Li, W.S. Gan, On the use of an SPSA-based model-free feedback controller in active noise control for periodic disturbances in a duct, J Sound Vib. 317 (2008) p.456–472.

DOI: 10.1016/j.jsv.2008.05.027

Google Scholar

[3] J. Figwer, Secondary path model identification in active noise control, Proc. 15th International Conference on Methods and Models in Automation and Robotics, Międzyzdroje, Poland (2010) pp.110-113.

DOI: 10.1109/mmar.2010.5587253

Google Scholar

[4] T. Główka, J. Figwer, Identyfikacja modeli obiektów pracujących w torze sprzężenia w przód, in: K. Malinowski, J. Józefczyk, J. Świątek (Eds. ), Aktualne Problemy Automatyki i Robotyki, Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2014, pp.676-684.

Google Scholar

[5] T. Główka, J. Figwer, Identification of linear subsystem for simple block-oriented nonlinear systems working in closed-loop – higher order spectra approach, Proc. 18th International Conference on System Theory, Control and Computing, Sinaia, Romania (2014).

DOI: 10.1109/icstcc.2014.6982534

Google Scholar

[6] S.M. Kuo, D.R. Morgan, Active Noise Control Systems, Algorithms and DSP Implementations, J. Wiley & Sons, New York, (1996).

Google Scholar

[7] P.A. Nelson, S.J. Elliott, Active Control of Sound, Academic Press, London, (1992).

Google Scholar

[8] J. Figwer, A new method of on-line model identification and update for multichannel active noise control systems, Archives of Control Sciences 13 (2003) pp.141-155.

Google Scholar

[9] C.L. Nikias, A.P. Petropulu, Higher-Order Spectra Analysis – A Nonlinear Signal Processing Framework, PTR Prentice Hall Inc., Englewood Cliffs, New Jersey, (1993).

Google Scholar

[10] T. Główka, Higher Order Spectra for Frequency Model Identification, Jacek Skalmierski Computer Studio, Gliwice, (2011).

Google Scholar

[11] J. Figwer, Multisine Random Processes. Theory and Applications (in Polish), Akademicka Oficyna Wydawnicza EXIT, Warszawa, (2012).

Google Scholar

[12] J.K. Tugnait, Y. Zhou, On closed-loop system identification using polyspectral analysis given noisy input-output time-domain data, Automatica 36 (2000) pp.1795-1808.

DOI: 10.5555/s0005-1098(00)00104-7

Google Scholar

[13] J.S. Bendat, A.G. Piersol, Engineering Applications of Correlation and Spectral Analysis, John Wiley & Sons, Inc., New York, (1993).

Google Scholar