Improvement of the convergence property of adaptive feedforward controllers and their application to the active control of ship interior noise

Ishimitsu, S. and Elliott, S.J. (2004) Improvement of the convergence property of adaptive feedforward controllers and their application to the active control of ship interior noise. Acoustical Science and Technology, 25 (3), 181-187. (doi:10.1250/ast.25.181).

Abstract

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.

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