scholarly journals Active Noise Feedback Control Using a Neural Network

2001 ◽  
Vol 8 (1) ◽  
pp. 15-19 ◽  
Author(s):  
Zhang Qizhi ◽  
Jia Yongle
Keyword(s):  
Neural Network ◽  
Control System ◽  
Feedback Control ◽  
Noise Control ◽  
Finite Impulse Response ◽  
Gradient Descent Method ◽  
Noise Power ◽  
Nonlinear Simulation ◽  
Active Noise ◽  
Nonlinear Noise

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.

Active Noise Hybrid Feedforward/Feedback Control Using Neural Network Compensation*

2001 ◽  
Vol 124 (1) ◽  
pp. 100-104 ◽  
Author(s):  
Zhang Qizhi ◽  
Jia Yongle
Keyword(s):  
Neural Network ◽  
Noise Control ◽  
Control Method ◽  
Active Noise Control ◽  
Equivalent Model ◽  
Gradient Descent Method ◽  
Nonlinear Simulation ◽  
Control Approach ◽  
Active Noise ◽  
Nonlinear Noise

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.

Design and fabrication of active noise control system for a grass cutter

2021 ◽  
pp. 095745652199983
Author(s):  
Rahmatullah Khan ◽  
Mohammad Muzammil ◽  
Omar Farooq
Keyword(s):  
Control System ◽  
Noise Control ◽  
Active Noise Control ◽  
Control Circuit ◽  
Operational Amplifiers ◽  
Control Technique ◽  
Noise Power ◽  
Loud Noise ◽  
Cutting Machine ◽  
Active Noise

Active noise control technique was used to reduce noise generated by a grass cutting machine. Grass cutting machine running on a diesel engine generates loud noise of about 105 dBA. Based on the spectral analysis of engine noise, it was observed that frequencies in the range of 440–5000 Hz were having more noise power. An active noise control circuit was designed and fabricated using operational amplifiers. The active noise control circuit was tested with the help of a duct made of thermocol. Results show that reduction in noise up to 10 dBA was obtained when the active noise control circuit was used with a duct made of thermocol, while a reduction up to 5 dBA was obtained when used on a grass cutting machine. The active noise control system developed may be used to reduce noise generated by a grass cutting machine.

scholarly journals Online secondary path modeling method with auxiliary noise power scheduling strategy for multi-channel adaptive active noise control system

2019 ◽  
Vol 38 (2) ◽  
pp. 740-752 ◽  
Author(s):  
Pu Yuxue ◽  
Shu Pengfei
Keyword(s):  
Control System ◽  
Noise Control ◽  
Active Noise Control ◽  
Variable Step Size ◽  
Noise Power ◽  
Path Modeling ◽  
Step Size ◽  
Scheduling Strategy ◽  
Active Noise ◽  
Secondary Path Modeling

Accurate model of secondary paths is very crucial for the multi-channel filtered-X least mean square algorithm applied in adaptive active noise control system. The auxiliary random noise technique is popular for online secondary path modeling during adaptive active noise control operation. This paper proposes a simplified variable step-size strategy and an effective auxiliary noise power scheduling strategy for the multi-channel filtered-X least mean square algorithm. Through a defined indirect error signal, the proposed method can guarantee every online secondary path modeling filter has its own exclusive variable step-size strategy to update their coefficients, and every injected noise has its own exclusive scheduling strategy considering all of the corresponding online secondary path modeling filters. The proposed method can improve the adaptive performance and simplifies the complexity of multi-channel adaptive active noise control system. Computer simulations show that the proposed method gives much better noise reduction and secondary path modeling accuracy at a somewhat faster convergence rate comparing with the competing methods.

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