DOCSLIB.ORG

Multi-Channel Active Noise Control for All Uncertain Primary and Secondary Paths

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Multi-Channel Active Noise Control for All Uncertain Primary and Secondary Paths Multi-channel Active Noise Control for All Uncertain Primary and Secondary Paths Yuhsuke Ohta and Akira Sano Department of System Design Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan Abstract filter matrices in an on-line manner, which enables the noise cancellation at the objective points. Unlike the pre- Fully adaptive feedforward control algorithm is proposed vious indirect approaches based on the explicit on-line for general multi-channel active noise control (ANC) identification, neither dither sounds nor the PE property when all the noise transmission channels are uncertain. of the source noises are required in the proposed scheme. To reduce the actual canceling error, two kinds of virtual errors are introduced and are forced into zero by adjusting 2. Feedforward Adaptive Active Noise Control three adaptive FIR filter matrices in an on-line manner, which can result in the canceling at the objective points. Primary Reference Secondary Error Noise Microphones Sources Microphones Unlike other conventional approaches, the proposed algo- Sources G (z) rithm does not need exact identification of the secondary 1 e1 (k) paths, and so requires neither any dither signals nor the v1(k) s1 (k) PE property of the source noises, which is a great advan- r1(k) G (z) tage of the proposed adaptive approach. 3 G2 (z) G4 (z) s(k) 1. Introduction vN (k) c e (k) Nc s (k) Ns r (k) Active noise control (ANC) is efficiently used to sup- Nr press unwanted low frequency noises generated from pri- mary sources by emitting artificial secondary sounds to u(k) r(k) Adaptice Active objective points [1][2]. Adaptive feedforward control Noise Controller Cˆ(z,k) schemes using the primary noises measured by reference e(k) microphones are effective to ANC, since the noise path channels cannot be precisely modeled and are uncertainly changeable. A variety of filtered-x algorithms have been Figure 1: Schematic diagram of multi-channel adaptive adopted to attain the feedforward adaptation [2][3][4], on feedforward active noise control system. the assumption that all the secondary channels are known a priori. To deal with a general case when the secondary The setup of multi-channel feedforward active noise path channels are also unknown, almost previous works control system is illustrated in Fig.1. The primary noises N were based on indirect adaptive approaches which employ s(k) ∈ R s are generated from the Ns sources, and de- on-line identification of the secondary channels. There- tected by the Nr reference microphones. The detected N fore, dither noise should be added to artificial control signals r(k) ∈ R r are the input to the Nc × Nr adap- sound to assure the PE condition. By taking an exten- tive feedforward controller matrix Cˆ (z,k), where Nc is sion of the filtered-x type of approaches, the identified the number of the secondary loudspeakers which pro- models of the secondary channels are used correspond- duce artificial sounds to cancel the primary noises at ingly in the filtered-x algorithms [3][6][7][8] or the feed- the Nc objective points. The canceling errors are de- N forward controller [4][5]. Recently different approaches tected as ec(k) ∈ R c by the Nc error microphones. N ×N N ×N without secondary path identification have also been pro- G1(z) ∈Z c s and G2(z) ∈Z r s represent the posed [9][10]. primary channel matrices from the primary noise s(k) The aim of this paper is to propose a fully direct adap- to the reference microphones and error microphones, re- N ×N N ×N tive control approach which does not need explicit identi- spectively. G3(z) ∈Z r c and G4(z) ∈Z c c are fication of the secondary channels. To reduce the actual the secondary channel matrices from the secondary con- canceling errors, two kinds of virtual errors are introduced trol sounds u(k) to the reference and error microphone and are forced into zero by adjusting three adaptive FIR sets, respectively, where G3(z) is referred to as the cou- 3ULPDU\ 3DWK ea k eb k → k →∞ &KDQQHO Thus, if ( ) and ( ) 0 for is satisfied and *] \N ˆ ˆ the FIR parameters of C(z,k) and K(z,k) converge to HN . any constants, then the second and third terms in the VN R T *] 6HFRQGDU\ 3DWK right hand side of (4) can be cancelled, then by the rela- &KDQQHO ea k eb k e k R UN XN tion ( )+ ( )= c( ), thus it can also be attained *] R &]N *] \N that ec(k) → 0 [11]. $GDSWLYH )LOWHU , , Dˆ z,k Kˆ z,k R HN It seems that ( ) and ( ) are the identified .]N R T models for G1(z) and G4(z) respectively and the adaptive $GDSWLYH )LOWHU ,, Cˆ z,k ']N controller ( ) is adjusted according to the identified $GDSWLYH models of the secondary path dynamics. However, even )LOWHU ,,, - R HN when the source noise does not satisfy the PE property, .]N &]N T $GDSWLYH )HHGIRUZDUG [N &RQWUROOHU IRU $1& $GDSWLYH the proposed algorithm does not require the convergence )LOWHU ,, $GDSWLYH )LOWHU , of the adjusted parameters to their true values, but only the convergence of their parameters to any constants such ea k eb k Figure 2: Direct fully adaptive algorithm for single chan- that the errors ( ) and ( ) can converge to zero (see nel ANC Fig.9). Therefore, any dither sounds are not needed un- like the conventional indirect adaptive algorithm. The pling channel matrix. All the channels contain model degradation and complexity caused by the dither signals uncertainty and parameter changeability. can also be overcome by the proposed direct adaptive al- It follows from Fig.1 that gorithm. e k G z s k − G z u k c( )= 1( ) ( ) 4( ) ( ) (1a) 3.2 New ANC algorithm in two-channel case r k G z s k G z u k ( )= 2( ) ( )+ 3( ) ( ) (1b) In a multi-channel case, the exchange of two matrices u(k)=Cˆ (z,k)r(k) (1c) gives a different result, so the algorithm should be mod- ified. To mitigate the problem, we employ a diagonal Cˆ z,k where ( ) is an FIR type of adaptive feedforward con- matrix as Kˆ (z,k) to derive a fully adaptive algorithm for troller matrix, and the coefficients in Cˆ (z,k) can be up- adjusting the controller parameters. Here for the sim- dated by various filtered-x adaptive algorithms using the plicity of notation, we show the adaptive algorithm in given secondary path models. two-channel case. Similar to the single-channel case, we define two kinds 3. New Adaptive Algorithm for ea k eb k Uncertain Secondary Channels of virtual error vectors ( ) and ( ) which can be de- scribed by 3.1 Key idea for new adaptation algorithm in a e1(k) ec1(k) Kˆ11(z)0 u1(k) single channel case a = + e2(k) ec2(k) 0 Kˆ22(z) u2(k) We give a new direct adaptive algorithm which does not Dˆ11(z) Dˆ12(z) r1(k) need explicit identification of the secondary path chan- − (5) Dˆ21(z) Dˆ22(z) r2(k) nels, unlike the filtered-x algorithms using the identified eb k Dˆ z Dˆ z r k model of G¯4(z). The basic structure of the proposed 1( ) 11( ) 12( ) 1( ) b = e2(k) Dˆ 21(z) Dˆ 22(z) r2(k) adaptive feedforward control algorithm is illustrated in e k ea k eb k Fig.2, where c( ), ( ), ( ) can be expressed as: x11(k) Cˆ z Cˆ z x k ec(k)=G1(z)r(k) − G4(z)u(k) (2a) 11( ) 12( )0 0 12( ) − (6) a 00Cˆ21(z) Cˆ22(z) x21(k) e (k)=ec(k)+Kˆ (z,k)u(k) − Dˆ(z,k)r(k) (2b) x22(k) eb(k)=Dˆ(z,k)r(k) − Cˆ(z,k)x(k) (2c) where where G1(z) and G4(z) are defined in the previous sec- Kˆ z x11(k) 11( )0 tion, and the control input u(k) and the auxiliary signal x12(k) 0 Kˆ11(z) r1(k) x(k) are also defined as = (7) x21(k) Kˆ22(z)0 r2(k) u k Cˆ z,k r k ,xk Kˆ z,k r k x k ( )= ( ) ( ) ( )= ( ) ( ) (3) 22( ) 0 Kˆ22(z) It follows from Fig.2 and the definitions that u1(k) Cˆ11(z) Cˆ12(z) r1(k) = (8) a b u k Cˆ z Cˆ z r k e (k)+e (k)=[ec(k)+Kˆ (z,k)u(k) − Dˆ(z,k)r(k)] 2( ) 21( ) 22( ) 2( ) Dˆ z,k r k − Cˆ z,k x k +[ ( ) ( ) ( ) ( )] In the proposed adaptive algorithm, the coefficient pa- = ec(k)+Kˆ (z,k)Cˆ(z,k)r(k) − Cˆ(z,k)Kˆ (z,k)r(k) (4) rameters of Kˆii(z) and Dˆ ij(z) are updated so that the a a first virtual errors e1(k) and e2(k) can be eliminated, where i = 1 and 2. while the parameters of Cˆij(z) are updated so that the Other adaptive algorithms can also be derived, for in- b b 2 second virtual errors e1(k) and e2(k) can be eliminated. stance, the norm e(k) of the augmented error e(k)= If these parameters converge to constant and the two vir- (eaT (k), ebT (k))T can also minimized, which gives an al- tual errors can be reduced almost to zero, then it follows thernative algorithm which requries more computation. from (5) ∼ (8) that Therefore, we adopted the proposed algorithm given by (10) to (14) in the experiment.
Load more

Recommended publications
  • A Deep Learning Approach to Active Noise Control
    A Deep Learning Approach to Active Noise Control Hao Zhang1, DeLiang Wang1;2 1Department of Computer Science and Engineering, The Ohio State University, USA 2Center for Cognitive and Brain Sciences, The Ohio State University, USA fzhang.6720, [email protected] Abstract Reference Error Microphone �(�) Microphone �(�) We formulate active noise control (ANC) as a supervised �(�) �(�) �(�) learning problem and propose a deep learning approach, called �(�) deep ANC, to address the nonlinear ANC problem. A convo- lutional recurrent network (CRN) is trained to estimate the real Cancelling Loudspeaker �(�) and imaginary spectrograms of the canceling signal from the reference signal so that the corresponding anti-noise can elimi- ANC nate or attenuate the primary noise in the ANC system. Large- scale multi-condition training is employed to achieve good gen- Figure 1: Diagram of a single-channel feedforward ANC sys- eralization and robustness against a variety of noises. The deep tem, where P (z) and S(z) denote the frequency responses of ANC method can be trained to achieve active noise cancella- primary path and secondary path, respectively. tion no matter whether the reference signal is noise or noisy speech. In addition, a delay-compensated strategy is intro- tional link artificial neural network to handle the nonlinear ef- duced to address the potential latency problem of ANC sys- fect in ANC. Other nonlinear adaptive models such as radial tems. Experimental results show that the proposed method is basis function networks [12], fuzzy neural networks [13], and effective for wide-band noise reduction and generalizes well to recurrent neural networks [14] have been developed to further untrained noises.
    [Show full text]
  • Active Noise Control in a Three-Dimensional Space Jihe Yang Iowa State University
    Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1994 Active noise control in a three-dimensional space Jihe Yang Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Acoustics, Dynamics, and Controls Commons, and the Physics Commons Recommended Citation Yang, Jihe, "Active noise control in a three-dimensional space " (1994). Retrospective Theses and Dissertations. 10660. https://lib.dr.iastate.edu/rtd/10660 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. U'M'I MICROFILMED 1994 INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these vrill be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps.
    [Show full text]
  • Active Noise Control Over Space: a Subspace Method for Performance Analysis
    applied sciences Article Active Noise Control over Space: A Subspace Method for Performance Analysis Jihui Zhang 1,* , Thushara D. Abhayapala 1 , Wen Zhang 1,2 and Prasanga N. Samarasinghe 1 1 Audio & Acoustic Signal Processing Group, College of Engineering and Computer Science, Australian National University, Canberra 2601, Australia; [email protected] (T.D.A.); [email protected] (W.Z.); [email protected] (P.N.S.) 2 Center of Intelligent Acoustics and Immersive Communications, School of Marine Science and Technology, Northwestern Polytechnical University, Xi0an 710072, China * Correspondence: [email protected] Received: 28 February 2019; Accepted: 20 March 2019; Published: 25 March 2019 Abstract: In this paper, we investigate the maximum active noise control performance over a three-dimensional (3-D) spatial space, for a given set of secondary sources in a particular environment. We first formulate the spatial active noise control (ANC) problem in a 3-D room. Then we discuss a wave-domain least squares method by matching the secondary noise field to the primary noise field in the wave domain. Furthermore, we extract the subspace from wave-domain coefficients of the secondary paths and propose a subspace method by matching the secondary noise field to the projection of primary noise field in the subspace. Simulation results demonstrate the effectiveness of the proposed algorithms by comparison between the wave-domain least squares method and the subspace method, more specifically the energy of the loudspeaker driving signals, noise reduction inside the region, and residual noise field outside the region. We also investigate the ANC performance under different loudspeaker configurations and noise source positions.
    [Show full text]
  • Frequency Domain Active Noise Control with Ultrasonic Tracking Thomas Chapin Waite Iowa State University
    Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2010 Frequency Domain Active Noise Control with Ultrasonic Tracking Thomas Chapin Waite Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Mechanical Engineering Commons Recommended Citation Waite, Thomas Chapin, "Frequency Domain Active Noise Control with Ultrasonic Tracking" (2010). Graduate Theses and Dissertations. 11782. https://lib.dr.iastate.edu/etd/11782 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Frequency domain active noise control with ultrasonic tracking by Tom Waite A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Mechanical Engineering Program of Study Committee: Atul G. Kelkar, Major Professor Julie A. Dickerson J. Adin Mann III Jerald M. Vogel Qingze Zou Iowa State University Ames, Iowa 2010 Copyright c Tom Waite, 2010. All rights reserved. ii TABLE OF CONTENTS LIST OF FIGURES . v ACKNOWLEDGEMENTS . xi ABSTRACT . xii CHAPTER 1. INTRODUCTION . 1 1.1 Background . 1 1.1.1 Frequency Domain ANC . 5 1.1.2 Ultrasonic Tracking . 7 1.2 Outline . 8 CHAPTER 2. ULTRASONIC TRACKING . 11 2.1 Introduction . 11 2.2 Localization . 11 2.2.1 Bandwidth Considerations . 16 2.3 Error . 18 2.3.1 Sources of Incorrect Solutions .
    [Show full text]
  • Noise and Vibration Control in the Built Environment
    applied sciences Noise and Vibration Control in the Built Environment Edited by Jian Kang Printed Edition of the Special Issue Published in Applied Sciences www.mdpi.com/journal/applsci Noise and Vibration Control in the Built Environment Special Issue Editor Jian Kang Special Issue Editor Jian Kang University of Sheffield UK Editorial Office MDPI AG St. Alban-Anlage 66 Basel, Switzerland This edition is a reprint of the Special Issue published online in the open access journal Applied Sciences (ISSN 2076-3417) from 2016–2017 (available at: http://www.mdpi.com/journal/applsci/special_issues/vibration_control). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: Author 1; Author 2; Author 3 etc. Article title. Journal Name. Year. Article number/page range. ISBN 978-3-03842-420-8 (Pbk) ISBN 978-3-03842-421-5 (PDF) Articles in this volume are Open Access and distributed under the Creative Commons Attribution license (CC BY), which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book taken as a whole is © 2017 MDPI, Basel, Switzerland, distributed under the terms and conditions of the Creative Commons license CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/). Table of Contents About the Guest Editor .............................................................................................................................. v Preface to “Noise and Vibration Control in the Built Environment” ................................................... vii Chapter 1: Urban Sound Environment and Soundscape Francesco Aletta, Federica Lepore, Eirini Kostara-Konstantinou, Jian Kang and Arianna Astolfi An Experimental Study on the Influence of Soundscapes on People’s Behaviour in an Open Public Space Reprinted from: Appl.
    [Show full text]
  • Noise Reduction Through Active Noise Control Using Stereophonic Sound for Increasing Quite Zone
    Noise reduction through active noise control using stereophonic sound for increasing quite zone Dongki Min1; Junjong Kim2; Sangwon Nam3; Junhong Park4 1,2,3,4 Hanyang University, Korea ABSTRACT The low frequency impact noise generated during machine operation is difficult to control by conventional active noise control. The Active Noise Control (ANC) is a destructive interference technic of noise source and control sound by generating anti-phase control sound. Its efficiency is limited to small space near the error microphones. At different locations, the noise level may increase due to control sounds. In this study, the ANC method using stereophonic sound was investigated to reduce interior low frequency noise and increase the quite zone. The Distance Based Amplitude Panning (DBAP) algorithm based on the distance between the virtual sound source and the speaker was used to create a virtual sound source by adjusting the volume proportions of multi speakers respectively. The 3-Dimensional sound ANC system was able to change the position of virtual control source using DBAP algorithm. The quiet zone was formed using fixed multi speaker system for various locations of noise sources. Keywords: Active noise control, stereophonic sound I-INCE Classification of Subjects Number(s): 38.2 1. INTRODUCTION The noise reduction is a main issue according to improvement of living condition. There are passive and active control methods for noise reduction. The passive noise control uses sound absorption material which has efficiency about high frequency. The Active Noise Control (ANC) which is the method for generating anti phase control sound is able to reduce low frequency.
    [Show full text]
  • Newport Beach, California
    47th International Congress and Exposition on Noise Control Engineering (INTERNOISE 2018) Impact of Noise Control Engineering Chicago, Illinois, USA 26 - 29 August 2018 Volume 1 of 10 ISBN: 978-1-5108-7303-2 Printed from e-media with permission by: Curran Associates, Inc. 57 Morehouse Lane Red Hook, NY 12571 Some format issues inherent in the e-media version may also appear in this print version. Copyright© (2018) by Institute of Noise Control Engineering - USA (INCE-USA) All rights reserved. Printed by Curran Associates, Inc. (2018) For permission requests, please contact Institute of Noise Control Engineering - USA (INCE-USA) at the address below. Institute of Noise Control Engineering - USA (INCE-USA) INCE-USA Business Office 11130 Sunrise Valley Drive Suite 350 Reston, VA 20190 USA Phone: +1 703 234 4124 Fax: +1 703 435 4390 [email protected] Additional copies of this publication are available from: Curran Associates, Inc. 57 Morehouse Lane Red Hook, NY 12571 USA Phone: 845-758-0400 Fax: 845-758-2633 Email: [email protected] Web: www.proceedings.com INTER-NOISE 2018 – Pleanary, Keynote and Technical Sessions Papers are listed by Day, Morning/Afternoon, Session Number and Order of Presentation Select the underlined text (in18_xxxx.pdf) to link to paper Papers without links are available from the American Society of Mechanical Engineers (ASME) - Noise Control and Acoustics Division (NCAD) Sunday – 26 August, 2018 Opening Plenary: Barry Marshall Gibbs Session Chair: Stuart Bolton, Room: Chicago D and E 16:00 in18_4002.pdf Structure-Borne Sound in Buildings: Application of Vibro-Acoustic Methods for Measurement and Prediction .....1 Barry Marshall Gibbs, University of Liverpool Monday Morning – 27 August, 2018 Keynote: Jean-Louis Guyader Session Chair: Steffen Marburg Room: Chicago D 08:00 in18_5001.pdf Building Optimal Vibro-Acoustics Models From Measured Responses .....26 Jean-Louis Guyader, LVA/INSA de Lyon; Michael Ruzek, LAMCOS INSA de Lyon; Charles Pezerat, LAUM Universite du Maine Keynote: Amiya R.
    [Show full text]
  • Ultra-Broadband Local Active Noise Control with Remote Acoustic Sensing
    www.nature.com/scientificreports OPEN Ultra‑broadband local active noise control with remote acoustic sensing Tong Xiao *, Xiaojun Qiu & Benjamin Halkon One enduring challenge for controlling high frequency sound in local active noise control (ANC) systems is to obtain the acoustic signal at the specifc location to be controlled. In some applications such as in ANC headrest systems, it is not practical to install error microphones in a person’s ears to provide the user a quiet or optimally acoustically controlled environment. Many virtual error sensing approaches have been proposed to estimate the acoustic signal remotely with the current state‑ of‑the‑art method using an array of four microphones and a head tracking system to yield sound reduction up to 1 kHz for a single sound source. In the work reported in this paper, a novel approach of incorporating remote acoustic sensing using a laser Doppler vibrometer into an ANC headrest system is investigated. In this “virtual ANC headphone” system, a lightweight retro‑refective membrane pick‑up is mounted in each synthetic ear of a head and torso simulator to determine the sound in the ear in real‑time with minimal invasiveness. The membrane design and the efects of its location on the system performance are explored, the noise spectra in the ears without and with ANC for a variety of relevant primary sound felds are reported, and the performance of the system during head movements is demonstrated. The test results show that at least 10 dB sound attenuation can be realised in the ears over an extended frequency range (from 500 Hz to 6 kHz) under a complex sound feld and for several common types of synthesised environmental noise, even in the presence of head motion.
    [Show full text]
  • Active Noise Control System Based on the Improved Equation Error Model
    acoustics Article Active Noise Control System Based on the Improved Equation Error Model Jun Yuan 1, Jun Li 1, Anfu Zhang 2, Xiangdong Zhang 2,* and Jia Ran 3,* 1 Department of Microelectronics Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; [email protected] (J.Y.); [email protected] (J.L.) 2 Wuhan Second Ship Design and Research Institute, Wuhan 430064, China; [email protected] 3 Department of Electromagnetic Field and Wireless, Chongqing University of Posts and Telecommunications, Chongqing 400065, China * Correspondence: [email protected] (X.Z.); [email protected] (J.R.) Abstract: This paper presents an algorithm structure for an active noise control (ANC) system based on an improved equation error (EE) model that employs the offline secondary path modeling method. The noise of a compressor in a gas station is taken as an example to verify the performance of the proposed ANC system. The results show that the proposed ANC system improves the noise reduction performance and convergence speed compared with other typical ANC systems. In particular, it achieves 28 dBA noise attenuation at a frequency of about 250 Hz and a mean square error (MSE) of about −20 dB. Keywords: active noise control; improved equation error; mean square error 1. Introduction Citation: Yuan, J.; Li, J.; Zhang, A.; Zhang, X.; Ran, J. Active Noise In order to reduce acoustic noise in workplaces and living environments, passive Control System Based on the and active approaches [1] have been employed. For passive approaches, the energy of the Improved Equation Error Model. noise is absorbed by dampers, barriers, mufflers, or sonic crystals [1–4].
    [Show full text]
  • Active Noise and Vibration Control - Smart Structures 98-001
    " Active Noise and Vibration Control - Smart Structures 98-001 Active Noise and Vibration Control 1. Objectives 2. Background 1. Active Noise Control 2. Active Vibration Control (A VC) 3. Importance 4. Alternatives 5. Maturity and Risk 1. Maturity of Technology 1. Noise Cancellation Headsets (Commercial) 2. Active Muffler or Silencer (prototype) 3. Noise Reduction in Ducts (Commercial) 4. Vehicles (Developmental and Commercial) 5. Turboprop Aircraft (Developmental) 6. Transformers (Commercial) 7. Noise Reduction in Engines (Commercial) 8. Precision Machining (Developmental and Commercial) 9. Active Strut Member (Developmental) 10. Active Suspension (Developmental and Commercial) 11. Airframe Vibration Control (Developmental) 12. Flutter Control (Developmental) 6. Technological Risks 7. Availability 8. Breadth of Application 9. Cost-Benefit Analysis 10. References Page 1 Active Noise and Vibration Control- Smart Structures 98-001 Active Noise and Vibration Control 1. Objectives Active Noise and Vibration Systems have the following objectives: • To improve passenger comfort and crew effectiveness by reducing cabin and cockpit noise and vibration levels. Currently, pilot flying times are limited by exposure to noise and vibrations in turboprop and helicopter aircraft. Future workplace health and safety regulations will establish more rigid standards for exposure to cabin noise. • To increase the performance characteristics of commercial and military aircraft by reducing vibration levels. Operational performance, manoeuvrability and fuel efficiencies are expected to increase with active vibration control of aircraft structures which are exposed to high levels of turbulence and buffet loads creating wing flutter, acoustic resonance in open bomb bays, rotor resonances and fuselage vibrations. • To increase the lifetime of aircraft and improve component life cyclc costs by decreasing fatigue loading which is produced by noise and vibration.
    [Show full text]
  • Simultaneous Variable Perturbation Method for the Active Noise Control System with a Wireless Error Microphone
    Proceedings, APSIPA Annual Summit and Conference 2020 7-10 December 2020, Auckland, New Zealand Simultaneous Variable Perturbation Method for the Active Noise Control System with a Wireless Error Microphone Chuang Shi, Zhongxing Yuan, Rong Xie, and Huiyong Li University of Electronic Science and Technology of China, Chengdu, China Corresponding E-mail: [email protected] d() n e() n Abstract—This paper proposes the use of a wireless error x() n P microphone in active noise control (ANC) systems. There are + several practical advantages of doing so. The target zone of + quiet (ZoQ) is allowed to be reconfigured by simply moving y() n y'( n ) the wireless error microphone. When the target ZoQ is large, W S many error microphones have to be placed. Using the wireless error microphone eases the deployment and maintenance work. S However, the drawback of using wireless error microphones is e'( n ) due to the jitter effect of the wireless communication. The error NLMS Wireless signal samples arrive at the ANC controller at random time r() n intervals, which sometimes are greater than the sampling interval. To solve this problem, this paper investigates the simultaneous Fig. 1. Block diagram of the feedforward ANC system with a wireless error perturbation (SP) method and proposes the simultaneous variable microphone. perturbation (SVP) method. Simulations of the ANC system with a wireless error microphone are carried out with acoustic paths measured in an actual setup. Simulation results demonstrate that when the SVP method reduces the broadband noise effectively the reference and error signals are fed to the ANC controller even when the jitter effect is severe.
    [Show full text]
  • A High Performance Active Noise Control System for Magnetic Fields
    A high performance active noise control system for magnetic fields Tadas Pyragius1, a) and Kasper Jensen1, b) 1School of Physics & Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom We present a system for active noise control (ANC) of environmental magnetic fields based on a Filtered-x Least Mean Squares (FxLMS) algorithm. The system consists of a sensor that detects the ambient field noise and an error sensor that measures the signal of interest contaminated with the noise. These signals are fed to an adaptive algorithm that constructs a physical anti-noise signal cancelling the local magnetic field noise. The proposed system achieves a maximum of 35 dB root-mean-square (RMS) noise suppression in the DC-1 kHz band and 50 dB and 40 dB amplitude suppression of 50 Hz and 150 Hz AC line noise respectively for all three axial directions of the magnetic vector field. Magnetically low noise environments are important (a) across many metrologically relevant areas ranging from medical imaging of biomagnetic fields from the heart and brain to non-destructive evaluation of car batteries and scanning electron microscopy to name just a few1{3. Currently, optically pumped magnetometers (OPMs) are state of the art magnetic field sensors and are a promising alternative to conventional SQUID and fluxgate magne- tometers in both shielded and unshielded conditions4,5. A wider adoption of quantum magnetometers for ultra- (b) low field precision measurements has been limited to magnetically shielded environments due to large exter- nal magnetic field noise, making such setups expensive. Conventionally, partial magnetic field noise cancellation is achieved via a combination of proportional-integral- derivative (PID) controllers which are sometimes com- bined with a feed-forward system6{10.
    [Show full text]