11016 16 archive 123 disk0/00/01/10/16 2005-03-30 2024-03-15 05:01:52 2010-03-26 17:10:01 article show 0 pub none feedforward control, active noise control, gradient decent adaptation algorithm, preconditioned lms algorithm, ship interior noise 2004 Acoustical Science and Technology 10.1250/ast.25.181 TRUE 1346-3969 bce01d1b-f795-4e32-82e2-fcb128995ba6 25 3 10.1250/ast.25.181 1346-3969 print Ishimitsu S. 960e48bb-9d5c-4056-bb8b-a9e0eb7bb8bc FALSE Elliott S.J. 721dc55c-8c3e-4895-b9c4-82f62abd3567 FALSE http://www.loc.gov/loc.terms/relators/AUT Ishimitsu S. 960e48bb-9d5c-4056-bb8b-a9e0eb7bb8bc FALSE http://www.loc.gov/loc.terms/relators/AUT Elliott S.J. 721dc55c-8c3e-4895-b9c4-82f62abd3567 FALSE QC VM Various structural measures against vibration and noise were taken in a training ship, the Oshima Maru. However, an unpleasant sound persisted in the mess hall, where crews take their breaks. To reduce the noise, active controllers were investigated to satisfy the causality constraint in their update. Some of the controllers were preconditioned using the inverse of the plant because their convergence rates are limited by the dynamics and coupling within the plant response. The overall response from the output of the control filter to the output of the error sensor is thus equal to the all-pass part of the plant response. Because this response has a flat magnitude, the convergence speed of the adaptive algorithm is not affected by resonances in the plant response, as it is for the normal filtered reference LMS algorithm. The algorithms were compared under the same conditions to investigate differences in their properties and also corrected to satisfy the causality of their update processes. A comparison of convergence properties shows that satisfying the causality constraint in their update results in an improvement, and consequently the effect was confirmed by the filtered reference - filtered error LMS algorithm. Simulations are presented for a control system that was introduced using plant responses measured from a loudspeaker to a microphone in the mess hall inside the Oshima Maru. After investigating the convergence speed in various gradient descent adaptation algorithms, the results were integrated with the actual plant response and applied to the active control of ship interior noise. It was also found that although the preconditioned LMS algorithm converges dramatically faster than the ordinary gradient descent adaptation algorithms with an accurate plant model, its convergence rate is still sensitive to the auto-correlation and cross-correlation properties of the reference signals. published 181-187 1) P. A. Nelson and S. J. Elliott, Active Control of Sound (Academic Press, London, 1992). 2) S. J. Elliott, Signal Processing for Active Control (Academic Press, London, 2001). 3) B. Widrow and E. Walach, Adaptive Inverse Control (Prentice-Hall, Upper Saddle River, 1996), p. 165. 4) L. Sujbert, “A new filtered LMS algorithm for active noise control,” ACTIVE 99, p. 1101 (1999). 5) S. J. Elliott, “Filtered reference and filtered error LMS algorithms for adaptive feedforward control,” Mech. Syst. Signal Process., 12, 769–781 (1998). 6) S. J. Elliott “Optimal controllers and adaptive controllers for multichannel feedforward control of stochastic disturbances,” IEEE Trans. Signal Process., 48, 1053–1060 (2000). 7) S. Ishimitsu, H. Kitagawa, N. Hagino and S. Horihata, “Active control of the room noise of ship by the correlation analysis of measured wavelet,” Proc. internoise 2000, Vol. 3, pp. 1768–1771 (2000). 8) M. Tohyama and T. Koike, Fundamentals of Acoustic Signal Processing (Academic Press, London, 1998). 9) J. G. Cook and S. J. Elliott, “Connection between multichannel prediction error filter and spectral factorisation,” Electron. Lett., 35, 1218–1220 (1999). 10) S. Haykin, Adaptive Filter Theory (Prentice-Hall, Englewood Chiffs, N.J., 1991). eng 7116b040-9e67-42a8-9701-638229c9494a info:eu-repo/semantics/article 2004 published 304 2005-03-30