The nonlinear active noise control (ANC) is studied. The nonlinear ANC system is approximated by an equivalent model composed of a simple linear sub-model plus a nonlinear sub-model. Feedforward neural networks are selected to approximate the nonlinear sub-model. An adaptive active nonlinear noise control approach using a neural network enhancement is derived, and a simplified neural network control approach is proposed. The feedforward compensation and output error feedback technology are utilized in the controller designing. The on-line learning algorithm based on the error gradient descent method is proposed, and local stability of closed loop system is proved based on the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise control method based on neural network compensation is very effective to the nonlinear noise control, and the convergence of the NNEH control is superior to that of the NN control.
Active Noise Hybrid Feedforward/Feedback Control Using Neural Network Compensation*
Contributed by the Technical Committee on Vibration and Sound for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received Dec. 2000; Revised July 2001. Associated Editor: R. L. Clark.
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Qizhi, Z., and Yongle, J. (July 1, 2001). "Active Noise Hybrid Feedforward/Feedback Control Using Neural Network Compensation." ASME. J. Vib. Acoust. January 2002; 124(1): 100–104. https://doi.org/10.1115/1.1424296
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