Active noise feedback control using a neural network

Article type: Research Article

Authors: Qizhi, Zhang | Yongle, Jia

Affiliations: Department of Automation, Beijing Institute of Machinery, Beijing, 100085, P. R. China

Abstract: The active noise control (ANC) is discussed. Many digital ANC systems often based on the filter-x algorithm for finite impulse response (FIR) filter use adaptive filtering techniques. But if the primary noise path is nonlinear, the control system based on adaptive filter technology will be invalid. In this paper, an adaptive active nonlinear noise feedback control approach using a neural network is derived. The feedback control system drives a secondary signal to destructively interfere with the original noise to cut down the noise power. An on-line learning algorithm based on the error gradient descent method was proposed, and the local stability of closed loop system is proved using the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise feedback control method based on a neural network is very effective to the nonlinear noise control.

Journal: Shock and Vibration, vol. 8, no. 1, pp. 15-19, 2001