Archives of Acoustics, 39, 2, pp. 243-248, 2014

Active Noise Control Using a Fuzzy Inference System Without Secondary Path Modelling

Sebastian KURCZYK
Institute of Automatic Control, Silesian University of Technology

Institute of Automatic Control, Silesian University of Technology

For many adaptive noise control systems the Filtered-Reference LMS, known as the FXLMS algorithm is used to update parameters of the control filter. Appropriate adjustment of the step size is then important to guarantee convergence of the algorithm, obtain small excess mean square error, and react with required rate to variation of plant properties or noise nonstationarity. There are several recipes presented in the literature, theoretically derived or of heuristic origin.

This paper focuses on a modification of the FXLMS algorithm, were convergence is guaranteed by changing sign of the algorithm steps size, instead of using a model of the secondary path. A Takagi-Sugeno-Kang fuzzy inference system is proposed to evaluate both the sign and the magnitude of the step size. Simulation experiments are presented to validate the algorithm and compare it to the classical FXLMS algorithm in terms of convergence and noise reduction.
Keywords: Active Noise Control; adaptive control; fuzzy inference system; FXLMS; sign-varying step size.
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Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN).


ELLIOTT S. J., NELSON, P. A. (1993), Active Noise Control, IEEE Signal Processing Magazine, 10, 12–35,

TAKENOUCHI Y., SUZUKI H., OMOTO A. (2006), Behavior of the practically implemented filtered reference LMS algorithm in an active noise control system, Acoustical Science and Technology, 27, 20–27,

PAWELCZYK, M. (2008), Active noise control – a review of control-related problems, Archives of Acoustics, 33, 413–424,

ZHOU D., DEBRUNNER V. (2007), A New Active Noise Control Algorithm That Requires No Secondary Path Identification Based on the SPR Property, IEEE Transactions on Signal Processing, 55, 1719–1729,

WU M., CHEN G., QIU X. (2008), An Improved Active Noise Control Algorithm Without Secondary Path Identification Based on the Frequency-Domain Subband Architecture, IEEE Transactions on Audio, Speech, And Language Processing, 16, 1409–1419,

CHANG C.-Y., CHEN D.-R. (2010), Active Noise Cancellation Without Secondary Path Identification by Using an Adaptive Genetic Algorithm, IEEE Transactions on Instrumentation And Measurement, 59, 2315–2327,

PAWELCZYK M. (2002), Feedforward algorithms with simplified plant model for active noise control, Journal of Sound and Vibration, 255, 77-95,

KUNCHAKOORI N., ROUTRAY A., DAS D. P. (2008), An Energy Function Based Fuzzy Variable Step Size FxLMS Algorithm for Active Noise Control, IEEE Region 10 Colloquium and the Third International Conference on Industrial and Information Systems, 1–7,

WANG A. K., REN W. (1999), Convergence Analysis of the Multi-Variable Filtered-X LMS Algorithm with Application to Active Noise Control, IEEE Transactions on Signal

Processing, 47, 1166–1169,

ELLIOTT S. (2001), Signal Processing for Active Control, Academic Press,

BISMOR D. (2012), LMS Algorithm Step Size Adjustment for Fast Convergence, Archives of Acoustics, 37, 31–40,

JANG J.-S. R., SUN C. T, MIZUTANI E. (1997), Neuro-Fuzzy and Soft Computing, Prentice Hall, 73–91,

CORDÓN O., HERRERA F., PEREGRIN A. (1997), Applicability of the fuzzy operators in the design of fuzzy logic controllers, Fuzzy Sets and Systems, 86, 15–41.

BISMOR D. (2014), Comments on ‘A New Feedforward Hybrid ANC System’, IEEE Signal Processing Letters, 21(5), 635–637,

ZHANG L., QIU X. (2014), Causality study on a feedforward active noise control headset with different noise coming directions in free field, Applied Acoustics, 80, 36–44,

MAZUR K., PAWELCZYK M. (2011), Active Noise-Vibration Control using the Filtered-Reference LMS Algorithm with Compensation of Vibrating Plate Temperature Variation, Archives of Acoustics, 36(1), 65–76.

DOI: 10.2478/aoa-2014-0028