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.

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.

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.

scholarly journals Internal Model Control for a Light-Weight Active Noise-Reducing Casing

2016 ◽  
Vol 41 (2) ◽  
pp. 315-322 ◽  
Author(s):  
Krzysztof Mazur ◽  
Marek Pawełczyk
Keyword(s):  
Internal Model ◽  
Noise Control ◽  
Reference Signal ◽  
Active Noise Control ◽  
Internal Model Control ◽  
Classical Solutions ◽  
Light Weight ◽  
Active Noise ◽  
Model Control ◽  
Global Reduction

Abstract The active noise-reducing casing developed and promoted by the authors in recent publications have multiple advantages over other active noise control methods. When compared to classical solutions, it allows for obtaining global reduction of noise generated by a device enclosed in the casing. Moreover, the system does not require loudspeakers, and much smaller actuators attached to the casing walls are used instead. In turn, when compared to passive casings, the walls can be made thinner, lighter and with much better thermal transfer than sound-absorbing materials. For active noise control a feedforward structure is usually used. However, it requires an in-advance reference signal, which can be difficult to be acquired for some applications. Fortunately, usually the dominant noise components are due to rotational operations of the enclosed device parts, and thus they are tonal and multitonal. Therefore, it can be adequately predicted and the Internal Model Control structure can be used to benefit from algorithms well developed for feedforward systems. The authors have already tested that approach for a rigid casing, where interaction of the walls was significantly reduced. In this paper the idea is further explored and applied for a light-weight casing, more frequently met in practice, where each vibrating wall of the casing influences all the other walls. The system is verified in laboratory experiments.

scholarly journals Gain-Preserving Data-Driven Approximation of the Koopman Operator and Its Application in Robust Controller Design

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 949
Author(s):  
Keita Hara ◽  
Masaki Inoue
Keyword(s):  
Controller Design ◽  
Internal Model Control ◽  
Feedback Controller ◽  
Data Driven ◽  
Koopman Operator ◽  
Plant System ◽  
Dimensional Approximation ◽  
Model Control ◽  
Data Driven Modeling ◽  
L2 Gain

In this paper, we address the data-driven modeling of a nonlinear dynamical system while incorporating a priori information. The nonlinear system is described using the Koopman operator, which is a linear operator defined on a lifted infinite-dimensional state-space. Assuming that the L2 gain of the system is known, the data-driven finite-dimensional approximation of the operator while preserving information about the gain, namely L2 gain-preserving data-driven modeling, is formulated. Then, its computationally efficient solution method is presented. An application of the modeling method to feedback controller design is also presented. Aiming for robust stabilization using data-driven control under a poor training dataset, we address the following two modeling problems: (1) Forward modeling: the data-driven modeling is applied to the operating data of a plant system to derive the plant model; (2) Backward modeling: L2 gain-preserving data-driven modeling is applied to the same data to derive an inverse model of the plant system. Then, a feedback controller composed of the plant and inverse models is created based on internal model control, and it robustly stabilizes the plant system. A design demonstration of the data-driven controller is provided using a numerical experiment.

IMC-Based Calibration of the Boost Pressure Controller in an Electrically Assisted Turbocharged Gasoline Engine

2019 ◽  
Author(s):  
Baitao Xiao ◽  
Tyler Kelly ◽  
Timothy Stolzenfeld ◽  
Chenliu Lu ◽  
Dave Bell ◽  
...  
Keyword(s):  
Gasoline Engine ◽  
Pressure Control ◽  
Internal Model Control ◽  
Feedback Controller ◽  
Boost Pressure ◽  
Relay Feedback ◽  
Loop Control ◽  
Electrically Assisted ◽  
Model Control ◽  
Different Types

Abstract In this work, a systematic approach is developed to calibrate a feedback controller for boost pressure control of an electrically assisted turbocharged gasoline engine. The information from the experiments indicates the system can be approximated by a Gain-Integrator-Delay (GID) model which can be robustly identified. Two controllers are designed for two different types of inner loop control (torque/speed) of the electrically assisted turbocharger. The underlying calibration methodology is based on Internal Model Control (IMC). The application of IMC leads to controllers that can be naturally mapped to a classic feedback controller. The plant model is obtained by characterizing the boost system with relay feedback experiments. The calibration methodology as well as the controller designs are demonstrated with a validated simulation platform and good performance is observed.

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 A Modified Filtered-X LMS Algorithm for Active Control of Periodic Noise with On-Line Cancellation Path Modelling

2000 ◽  
Vol 19 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Xiaojun Qiu ◽  
Colin H. Hansen
Keyword(s):  
Transfer Function ◽  
Active Control ◽  
Random Noise ◽  
Reference Signal ◽  
Fir Filter ◽  
Lms Algorithm ◽  
Adaptive Estimator ◽  
Periodic Noise ◽  
Fxlms Algorithm ◽  
On Line

The filtered-x LMS algorithm (FXLMS) has been successfully applied to the active control of periodic and random noise and vibration. This paper presents a modified algorithm for active control of periodic noise based on the FXLMS algorithm which uses random noise for on-line cancellation path transfer function (CPTF) estimation. In the proposed algorithm, another two short adaptive filters are introduced. One is an adaptive noise cancellation filter, which is used to improve the convergence speed of the CPTF modelling filter in the presence of very large amplitude primary noise by cancelling the component of the error signal that is correlated with the primary noise. The other is an adaptive estimator, which is used to re-estimate the obtained CPTF (long FIR filter estimated by random noise) with a short FIR filter by using the periodic reference signal as the input. The traditional FXLMS algorithm is then used with the shortened FIR filter to filter the reference signal, thus providing significant processing flexibility in practical situations where the primary path transfer function changes much faster than the CPTF. Simulation results demonstrate the effectiveness of the proposed algorithm.

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