Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule

Ya-Li Zhou,Qi-Zhi Zhang,Woon-Seng Gan,Tao Zhang,Xiao-Dong Li

doi:10.1155/2009/587685

Abstract

In practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN)-based simultaneous perturbation stochastic approximation (SPSA) algorithm, which functions as a nonlinear mode-free (MF) controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS) algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.

Highlights

The exponential increase of noise pollution and ineffectiveness of passive techniques for noise attenuation have led to the development of active noise control (ANC) system [1,2,3,4,5,6]
Observations of various structures and algorithms reported in [3,4,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21] reveal that the structure based on functional link artificial neural network (FLANN) with the filtered-s least mean square (FSLMS) algorithm is a preferable nonlinear structure for active control of nonlinear noise processes because of its interesting properties. In this control method, in order to update the weights of the FLANN, we need a gradient of the error function, namely, we must know the model of the secondary path [3]
It is evident that the FLANN-based simultaneous perturbation stochastic approximation (SPSA) algorithm performs well in the case where the secondary path is nonlinear in an ANC system, the major disturbance frequency is attenuated by approximately 40 dB

Summary

Introduction

The exponential increase of noise pollution and ineffectiveness of passive techniques for noise attenuation have led to the development of active noise control (ANC) system [1,2,3,4,5,6]. Observations of various structures and algorithms reported in [3,4,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21] reveal that the structure based on FLANN with the FSLMS algorithm is a preferable nonlinear structure for active control of nonlinear noise processes because of its interesting properties In this control method, in order to update the weights of the FLANN, we need a gradient of the error function, namely, we must know the model of the secondary path [3].

Control algorithm

Simulation studies

Conclusions