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

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.

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.

Active Noise Control Based on TMS320C5509

2013 ◽  
Vol 631-632 ◽  
pp. 1172-1176
Author(s):  
Yong Wei Ma ◽  
Xin Ke Gou ◽  
Xian Jun Du ◽  
Chong Yu Ren
Keyword(s):  
Control System ◽  
Real Time ◽  
Noise Control ◽  
Active Noise Control ◽  
Noise Cancellation ◽  
Real Time Control ◽  
Time Control ◽  
Active Noise ◽  
Fxlms Algorithm ◽  
Mixed Language

The feed-forward adaptive active noise control (AANC) system is presented. Firstly, the hardware project of the system is brought forward, by selecting TMS320C5509 DSP as the controller. Then, using the mixed language, the active noise real-time control system is realized, based on the FXLMS algorithm. It’s proved that a good noise cancellation is achieved by the experiment.

Active Noise Control Using Phase-Compensated, Damped Resonant Filters

2003 ◽  
Author(s):  
Jesse B. Bisnette ◽  
Jeffrey S. Vipperman ◽  
Daniel D. Budny
Keyword(s):  
Noise Control ◽  
Control Method ◽  
Active Noise Control ◽  
Control Device ◽  
Pass Filter ◽  
Acoustic Transducers ◽  
Active Noise ◽  
Controller Performance ◽  
The Impact ◽  
Resonant Filters

An active noise control device called active noise absorber (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) can not be truly colocated, 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. A companion experimental study validated the simulation result and demonstrated nearly 10 dB of control in the first duct mode.

The Study of Active Noise Control Method for Moving Target in Noisy Space

2011 ◽  
Vol 52-54 ◽  
pp. 1592-1597
Author(s):  
Yan Jian Liao ◽  
Alan Slade ◽  
Hong Yan Luo ◽  
Li Chen
Keyword(s):  
Evaluation System ◽  
Noise Control ◽  
Control Method ◽  
Control Point ◽  
Active Noise Control ◽  
Interaction Model ◽  
Position Tracking ◽  
Moving Target ◽  
Active Noise ◽  
Head Space

For the noise problem of moving target in noisy space, This work is dedicated to developing a novel and promising solution based on the moving target tracking technology and the active noise control (ANC) technology. The development process began with the simplified sound field interaction model, the “auto position tracking near head space ANC” strategy has been proposed from the modelling study. Furthermore, the primary experimental system that consists of the acoustic positioning and tracking subsystem for tracking the control point and the ANC subsystem for generating the anti-noise signal is presented, with the emphasis on the development of the improved TDC-FXLMS ANC algorithm. Finally, the performance of the auto-position tracking near head space ANC device is evaluated by carrying out the primary experiments on an experimental evaluation system.

Sign in / Sign up

Export Citation Format

Share Document