Research on Active Noise Control Method Compensating for Acoustic Metamaterial Noise Barrier in Transformer Noise Reduction

2018 ◽  
Author(s):  
Tian-zheng Wang ◽  
Tao Jin ◽  
Zhu-mao Lu ◽  
Chao Zhang ◽  
Guo-qiang Liu ◽  
...  
Keyword(s):  
Noise Reduction ◽  
Noise Control ◽  
Control Method ◽  
Active Noise Control ◽  
Acoustic Metamaterial ◽  
Active Noise ◽  
Noise Barrier

Active Noise Control Using Phase-Compensated, Damped Resonant Filters

2005 ◽  
Vol 128 (2) ◽  
pp. 148-155 ◽  
Author(s):  
Jesse B. Bisnette ◽  
Adam K. Smith ◽  
Jeffrey S. Vipperman ◽  
Daniel D. Budny
Keyword(s):  
Noise Control ◽  
Control Method ◽  
Active Noise Control ◽  
Control Device ◽  
Modal Control ◽  
Pass Filter ◽  
Acoustic Transducers ◽  
Active Noise ◽  
The Impact ◽  
Resonant Filters

An active noise control device called active noise absorber or ANA, which is based upon damped, resonant filters is developed and demonstrated. It is similar to structural positive position feedback (PPF) control, with two exceptions: (1) Acoustic transducers (microphone and speaker) cannot be truly collocated, and (2) the acoustic actuator (loudspeaker) has significant dynamics. The speaker dynamics can affect performance and stability and must be compensated. While acoustic modal control approaches are typically not sought, there are a number of applications where controlling a few room modes is adequate. A model of a duct with speakers at each end is developed and used to demonstrate the control method, including the impact of the speaker dynamics. An all-pass filter is used to provide phase compensation and improve controller performance and permits the control of nonminimum phase plants. A companion experimental study validated the simulation results and demonstrated nearly 8 dB of control in the first duct mode. A multi-modal control example was also demonstrated producing an average of 3 dB of control in the first four duct modes.

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.

scholarly journals Noise cancellation system based on active noise control. 2nd Report, Adaptive control method.

1991 ◽  
Vol 57 (534) ◽  
pp. 431-436
Author(s):  
Seiichirou SUZUKI ◽  
Takurou HAYASHI ◽  
Katsuyoshi NAGAYASU ◽  
Susumu SARUTA ◽  
Hiroshi TAMURA
Keyword(s):  
Adaptive Control ◽  
Noise Control ◽  
Control Method ◽  
Active Noise Control ◽  
Noise Cancellation ◽  
Active Noise

Reinforcement Learning for Active Noise Control in a Hydraulic System

2021 ◽  
Vol 143 (6) ◽  
Author(s):  
Eric R. Anderson ◽  
Brian L. Steward
Keyword(s):  
Reinforcement Learning ◽  
Hydraulic System ◽  
Noise Control ◽  
Control Method ◽  
Active Noise Control ◽  
Operating Point ◽  
Hydraulic Pressure ◽  
Hydraulic Systems ◽  
Active Noise ◽  
Pressure Ripple

Abstract Hydraulic pressure ripple in a pump, as a result of converting rotational power to fluid power, continues to be a problem faced when developing hydraulic systems due to the resulting noise generated. In this paper, we present simulation results from leveraging an actor-critic reinforcement learning method as the control method for active noise control in a hydraulic system. The results demonstrate greater than 96%, 81%, and 61% pressure ripple reduction for the first, second, and third harmonics, respectively, in a single operating point test, along with the advantage of feed forward like control for high bandwidth response during dynamic changes in the operating point. It also demonstrates the disadvantage of long convergence times while the controller is effectively learning the optimal control policy. Additionally, this work demonstrates the ancillary benefit of the elimination of the injection of white noise for the purpose of system identification in the current state of the art.

Coherence-based performance analysis on noise reduction in multichannel active noise control systems

2020 ◽  
Vol 148 (3) ◽  
pp. 1519-1528
Author(s):  
Jihui Aimee Zhang ◽  
Naoki Murata ◽  
Yu Maeno ◽  
Prasanga N. Samarasinghe ◽  
Thushara D. Abhayapala ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Noise Reduction ◽  
Control Systems ◽  
Noise Control ◽  
Active Noise Control ◽  
Active Noise

A Review of Active Noise Control Applications on Noise Barrier in Three-Dimensional/Open Space: Myths and Challenges

2019 ◽  
Vol 18 (04) ◽  
pp. 1930002 ◽  
Author(s):  
Hsiao Mun Lee ◽  
Zhaomeng Wang ◽  
Kian Meng Lim ◽  
Heow Pueh Lee
Keyword(s):  
Open Space ◽  
Noise Control ◽  
Active Noise Control ◽  
Three Dimensional ◽  
Construction Site ◽  
Noise Sources ◽  
Current State ◽  
Active Noise ◽  
Noise Barrier ◽  
Passive Noise

Active noise control (ANC), with counteracting sound in exact equal magnitude and opposite phase to the noise to be controlled, is often considered as a potential solution for solving complex noise problems. However, there are both myths and challenges in its implementations. In a crowded city like Singapore, many noise sources from construction site and subway track are located very close to the residential and commercial buildings. It was suggested by few researchers that by placing suitable control speakers at the construction site (working principle of ANC), the noise from the construction site could be prevented from propagating to the surrounding buildings. Similarly, for viaduct or subway track, by placing control speakers along the viaduct or track, the noise generated by the passing trains or vehicles could be reduced based on the principle of ANC technique. However, implementation of ANC technique on these noise issues is not easy as all of these noise control problems involve multiple noise sources with complex or transient frequency spectrum in large three-dimensional/open space. Therefore, the main intention of the present paper is to discuss the current state of the art of this topic as well as to examine the potential application and limitation of the ANC technique in mitigating unwanted noise, particularly in large three-dimensional/open space and with cooperation of passive noise barrier.

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