COMPARISON OF ADAPTIVE CONTROLLERS FOR MULTICHANNEL FEEDFORWARD CONTROL AND THEIR APPLICATION TO THE ACTIVE CONTROL OF SHIP INTERIOR NOISE

Abstract

Various structural measures against vibration and noise were taken in a training ship, Oshima Maru. However, an unpleasant sound persisted in the mess hall, where crews take their breaks. In order to reduce the noise, active controllers were investigated. Some of them were preconditioned using the inverse of the plant because their convergence rates are limited by the dynamics and coupling within the plant response. 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. Simulations for a control system were introduced using plant responses measured from a loudspeaker to a microphone in the mess hall inside 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 shown that the although 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 autocorrelation and cross-correlation properties of the reference signals.