Kernel Filtered-x LMS Algorithm for Active Noise Control System with Nonlinear Primary Path

2018 ◽  
Vol 37 (12) ◽  
pp. 5576-5594 ◽  
Author(s):  
Yuqi Liu ◽  
Chao Sun ◽  
Shouda Jiang
Keyword(s):  
Control System ◽  
Noise Control ◽  
Active Noise Control ◽  
Lms Algorithm ◽  
Active Noise ◽  
Primary Path

scholarly journals Convex combination-based active impulse noise control system

2019 ◽  
Vol 39 (1) ◽  
pp. 190-202 ◽  
Author(s):  
Ning Yu ◽  
Zhaoxia Li ◽  
Yinfeng Wu ◽  
Renjian Feng ◽  
Bin Chen
Keyword(s):  
Control System ◽  
Noise Reduction ◽  
Impulse Noise ◽  
Noise Control ◽  
Convex Combination ◽  
Active Noise Control ◽  
Lms Algorithm ◽  
Frequency Noise ◽  
Active Noise ◽  
Risk Sensitive

Active noise control shows a good performance on the suppression of the low-frequency noise and hence it is widely applied. However, the traditional active noise control systems are unsatisfactory in controlling impulse noise in practical situations. A method based on the convex combination of filtered-x least mean square and filtered-x minimum kernel risk-sensitive loss adaptive algorithms (CFxLM) is presented to efficiently suppress impulse noise. Due to the simplicity of the LMS algorithm, the related filter is selected as the fast filter. Because the minimum kernel risk-sensitive loss algorithm is robust to impulse noise and can offer good convergence performance, we first apply it to the active noise control system and select the corresponding filter as the slow one. The proposed CFxLM algorithm can achieve both fast convergence and good noise reduction and any prior knowledge of reference noise is unnecessary. Extensive simulations demonstrate the superior noise reduction capability of the developed CFxLM-based active noise control system in controlling impulse noise.

Research of Quantum Algorithm for Adaptive Active Noise Control System

2012 ◽  
Vol 457-458 ◽  
pp. 196-201
Author(s):  
Wei Jiang
Keyword(s):  
Control System ◽  
Adaptive Algorithm ◽  
Noise Control ◽  
Active Noise Control ◽  
Quantum Algorithm ◽  
Adaptive Controller ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Active Noise

Adaptive active noise control based on least mean square (LMS) algorithm is a linear adaptive filter so that it cannot obtain desired noise reduction. Quantum algorithm is combined with noise control to form quantum adaptive controller. Quantum adaptive algorithm is discussed completely and noise control system is simulated in order to analyze the effects of noise control.

Hammerstein Nonlinear Active Noise Control with the Filtered-Error LMS Algorithm

2013 ◽  
Vol 38 (2) ◽  
pp. 197-203 ◽  
Author(s):  
Krzysztof Mazur ◽  
Marek Pawełczyk
Keyword(s):  
Control System ◽  
Noise Control ◽  
Feedforward Control ◽  
Active Noise Control ◽  
Lms Algorithm ◽  
Negative Effects ◽  
Active Noise ◽  
High Reduction ◽  
Filtered Error ◽  
Vibrating Plate

Abstract Active Noise Control (ANC) of noise transmitted through a vibrating plate causes many problems not observed in classical ANC using loudspeakers. They are mainly due to vibrations of a not ideally clamped plate and use of nonlinear actuators, like MFC patches. In case of noise transmission though a plate, nonlinerities exist in both primary and secondary paths. Existence of nonlinerities in the system may degrade performance of a linear feedforward control system usually used for ANC. The performance degradation is especially visible for simple deterministic noise, such as tonal noise, where very high reduction is expected. Linear feedforward systems in such cases are unable to cope with higher harmonics generated by the nonlinearities. Moreover, nonlinearities, if not properly tackled with, may cause divergence of an adaptive control system. In this paper a feedforward ANC system reducing sound transmitted through a vibrating plate is presented. The ANC system uses nonlinear control filters to suppress negative effects of nonlinearies in the system. Filtered-error LMS algorithm, found more suitable than usually used Filtered-reference LMS algorithm, is employed for updating parameters of the nonlinear filters. The control system is experimentally verified and obtained results are discussed.

Behavior of the practically implemented filtered reference LMS algorithm in an active noise control system

2006 ◽  
Vol 27 (1) ◽  
pp. 20-27 ◽  
Author(s):  
Yoko Takenouchi ◽  
Hisaharu Suzuki ◽  
Akira Omoto
Keyword(s):  
Control System ◽  
Noise Control ◽  
Active Noise Control ◽  
Lms Algorithm ◽  
Active Noise

scholarly journals Wavelet Adaptive Algorithm and Its Application to MRE Noise Control System

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Zhang Yulin ◽  
Zhao Xiuyang
Keyword(s):  
Control System ◽  
Adaptive Algorithm ◽  
Noise Control ◽  
Complex Modulus ◽  
Active Noise Control ◽  
Adaptive Algorithms ◽  
Lms Algorithm ◽  
Magnetorheological Elastomers ◽  
Active Noise ◽  
Simulation Results

To address the limitation of conventional adaptive algorithm used for active noise control (ANC) system, this paper proposed and studied two adaptive algorithms based on Wavelet. The twos are applied to a noise control system including magnetorheological elastomers (MRE), which is a smart viscoelastic material characterized by a complex modulus dependent on vibration frequency and controllable by external magnetic fields. Simulation results reveal that the Decomposition LMS algorithm (D-LMS) and Decomposition and Reconstruction LMS algorithm (DR-LMS) based on Wavelet can significantly improve the noise reduction performance of MRE control system compared with traditional LMS algorithm.

scholarly journals Multichannel active noise control system using variable step size algorithms

2015 ◽  
Vol 18 (3) ◽  
pp. 61-71
Author(s):  
Ngan Vuong Thuy Nguyen ◽  
Long Duc Tran ◽  
Tho Thi Minh Tran ◽  
Tuan Van Huynh
Keyword(s):  
Control System ◽  
Real Time ◽  
Noise Control ◽  
Active Noise Control ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Feed Forward ◽  
Active Noise ◽  
Variable Step

This paper presents the feed-forward multichannel active noise control which was simulated on LabVIEW and experimented on two channel feed-forward active noise control in real-time. The simulation of feed-forward multichannel active noise control system was built on LabVIEW by using FxLMS algorithm and other algorithms such as Variable Step-size (VSS) and saturation compensation. Moreover, LMS and VSS LMS algorithm were used to train the secondary path in order to improve the convergence speed and stabilization of ANC system in the real-time. The experimental results show that the ANC system in real-time using variable step-size and without using variable step-size algorithm work effectively with noise sources which were synthesized from one to three frequencies. The noise level in these cases decreased around 12 dB - 35 dB and the noise reduction radius measured is about 3 cm around the microphone.

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