Active control of compressor noise in the machinery room of refrigerators

2019 ◽  
Vol 67 (5) ◽  
pp. 350-362
Author(s):  
J. M. Ku ◽  
W. B. Jeong ◽  
C. Hong
Keyword(s):  
Control System ◽  
Active Control ◽  
Weighting Function ◽  
Reference Signal ◽  
Active Noise Control ◽  
Dominant Frequency ◽  
Time Signal ◽  
Frequency Noise ◽  
Sound Power ◽  
Fxlms Algorithm

The low-frequency noise generated by the vibration of the compressor in the machinery room of refrigerators is considered as annoying sound. Active noise control is used to reduce this noise without any change in the design of the compressor in the machinery room. In configuring the control system, various signals are measured and analyzed to select the reference signal that best represents the compressor noise. As the space inside the machinery room is small, the size of a speaker is limited, and the magnitude of the controller transfer function is designed to be small at low frequencies, the controller uses FIR filter structure converged by the FxLMS algorithm using the pre-measured time signal. To manage the convergence speed for each frequency, the frequency-weighting function is applied to FxLMS algorithm. A series of measurements are performed to design the controller and to evaluate the control performance. After the control, the sound power transmitted by the refrigerator is reduced by 9 dB at the first dominant frequency (408 Hz in this case) and 3 dB at the second dominant frequency (459 Hz here), and the overall sound power decreases by 2.6 dB. Through this study, an active control system for the noise generated by refrigerator compressors is established.

Combined Feedforward–Feedback Active Control of Road Noise Inside a Vehicle Cabin

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Jie Duan ◽  
Mingfeng Li ◽  
Teik C. Lim ◽  
Ming-Ran Lee ◽  
Ming-Te Cheng ◽  
...  
Keyword(s):  
Active Control ◽  
Feedforward Control ◽  
Reference Signal ◽  
Active Noise Control ◽  
Internal Model Control ◽  
Feedback Controller ◽  
Road Noise ◽  
Vehicle Cabin ◽  
Model Control ◽  
Fxlms Algorithm

Conventional active control of road noise inside a vehicle cabin generally uses a pure feedforward control system with the conventional filtered-x least mean square (FXLMS) algorithm. While it can yield satisfactory noise reduction when the reference signal is well correlated with the targeted noise, in practice, it is not always possible to obtain a reference signal that is highly coherent with a broadband response typically seen in road noise. To address this problem, an active noise control (ANC) system with a combined feedforward–feedback controller is proposed to improve the performance of attenuating road noise. To take full advantage of the feedforward control, a subband (SFXLMS) algorithm, which can achieve more noise attenuation over a broad frequency range, is used to replace the conventional FXLMS algorithm. Meanwhile, a feedback controller, based on internal model control (IMC) architecture, is introduced to reduce the road noise components that have strong response but are poorly correlated with the reference signals. The proposed combined feedforward–feedback ANC system has been demonstrated by a simulation model with six reference accelerometers, two control loudspeakers and one error microphone, using actual data measured from a test vehicle. Results show that the performance of the proposed combined controller is significantly better than using either a feedforward controller only or a feedback controller only, and is able to achieve about 4 dBA of overall sound pressure level reduction.

Simulation of Active Noise Control System on the Truck Interior Cab

2013 ◽  
Vol 798-799 ◽  
pp. 443-447
Author(s):  
Qi Chen LU ◽  
Hui Bin LI ◽  
Hua Huang
Keyword(s):  
Control System ◽  
Control Systems ◽  
Active Control ◽  
Noise Control ◽  
Active Noise Control ◽  
Low Frequency ◽  
Frequency Noise ◽  
Simulink Model ◽  
Active Noise ◽  
Active Control System

Studying on adaptive active noise control (AANC) system of the truck interior cab to reduce the low-frequency noise,a normalization FLMS algorithm simulink model is established in Matlab/Simulink.Then taking it as the core,a feedforward adaptive active control system and a feedback adaptive active control system of the tuck interior cab are established in Matlab/Simulink .Considerating the actual error channels effects on systems ,the noise reduction effects of two adaptive active control systems are verified from the simulintion results.Through comparing the two wo adaptive active control systems,we found that the feedforward adaptive active control system is more stable.

A Computationally Efficient Multichannel Active Road Noise Control System

2014 ◽  
Vol 137 (1) ◽  
Author(s):  
Jie Duan ◽  
Mingfeng Li ◽  
Teik C. Lim ◽  
Ming-Ran Lee ◽  
Ming-Te Cheng ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Noise Control ◽  
Acoustic Noise ◽  
Reference Signal ◽  
Active Noise Control ◽  
Computational Cost ◽  
Road Noise ◽  
Time Frequency ◽  
Fxlms Algorithm

A multichannel active noise control (ANC) system has been developed for a vehicle application, which employs loudspeakers to reduce the low-frequency road noise. Six accelerometers were attached to the vehicle structure to provide the reference signal for the feedforward control strategy, and two loudspeakers and two microphones were applied to attenuate acoustic noise near the headrest of the driver's seat. To avoid large computational burden caused by the conventional time-domain filtered-x least mean square (FXLMS) algorithm, a time-frequency domain FXLMS (TF-FXLMS) algorithm is proposed. The proposed algorithm calculates the gradient estimate and filtered reference signal in the frequency domain to reduce the computational requirement, while also updates the control signals in the time domain to avoid delay. A comprehensive computational complexity analysis is conducted to demonstrate that the proposed algorithm requires significantly lower computational cost as compared to the conventional FXLMS algorithm.

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.

Parallel Architecture of Reconfigurable Hardware for Massive Output Active Noise Control

2019 ◽  
Vol 29 (03) ◽  
pp. 1950014
Author(s):  
Diego Mendez ◽  
David Arevalo ◽  
Diego Patino ◽  
Eduardo Gerlein ◽  
Ricardo Quintana
Keyword(s):  
Acoustic Waves ◽  
Noise Control ◽  
Reference Signal ◽  
Active Noise Control ◽  
Computational Cost ◽  
Main Idea ◽  
Parallel Architecture ◽  
Acceptable Error ◽  
Active Noise ◽  
Fxlms Algorithm

Filtered-x Least Mean Squares (FxLMS) is an algorithm commonly used for Active Noise Control (ANC) systems in order to cancel undesired acoustic waves from a sound source. There is a small number of hardware designs reported in the literature, that in turn only use one reference signal, one error signal and one output control signal. In this paper, it is proposed a 3-dimensional hardware-based version of the widely used FxLMS algorithm, using one reference microphone, 18 error microphones, one output and a FIR filter of 400[Formula: see text] order. The FxLMS algorithm was implemented in a Xilinx Artix 7 FPGA running at 25 MHz, which allowed to update the filter coefficients in 32.44[Formula: see text] s. The main idea behind this work is to propose a pipelined parallelized architecture to achieve processing times faster than real time for the filter coefficients update. The main contribution of this work is not the ANC technique itself, but rather the proposed hardware implementation that utilizes integer arithmetic, which provided an acceptable error when benchmarked with a software implementation. This parallel system allows a scalable implementation as an advantage of using FPGA without compromising the computational cost and, consequently, the latency.

scholarly journals Virtual Secondary Path Algorithm for Multichannel Active Control of Vehicle Powertrain Noise

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
Jie Duan ◽  
Mingfeng Li ◽  
Teik C. Lim ◽  
Ming-Ran Lee ◽  
Ming-Te Cheng ◽  
...  
Keyword(s):  
Control System ◽  
Noise Control ◽  
Multiple Input Multiple Output ◽  
Active Noise Control ◽  
Least Mean Square ◽  
Multiple Channel ◽  
Fxlms Algorithm ◽  
Input Multiple Output ◽  
Dependent Property ◽  
Vehicle Powertrain

An enhanced multiple-input multiple-output (MIMO) filtered-x least mean square (FXLMS) algorithm using improved virtual secondary path is proposed as the basis for an active noise control (ANC) system for treating vehicle powertrain noise. This new algorithm is developed to overcome the limitation caused by the frequency-dependent property of the standard FXLMS algorithm and to reduce the variation of convergence speed inherent in multiple-channel cases, in order to improve the overall performance of the control system. In this study, the convergence property of the proposed algorithm is analyzed in the frequency domain in order to yield a better understanding of the physical meaning of the virtual secondary path. In practice, because of the arrangement and sensitivities of the actuators (speakers), transducers (microphones), and physical environment, the magnitude response of the main secondary paths can be very different from each other. This difference will cause difficulty in the overall convergence of the algorithm, which will result in minimal attenuation at some of the channels. The proposed channel equalized (CE) virtual secondary path algorithm is designed to tackle this difficulty by equalizing the mean magnitude level of the main secondary paths and by adjusting other secondary paths correspondingly to keep the coupling effects among the control channels unchanged. The performance of the proposed algorithm is validated by analyzing a two-input two-output active powertrain noise control system.

Sign in / Sign up

Export Citation Format

Share Document