DOES ACTIVE NOISE CONTROL HAVE A FUTURE?

Manuscript Number: 1226U

Colin H HANSENDept Mechanical Engineering, University of Adelaide, SA 5005, AUSTRALIAchansen@mecheng.adelaide.edu.au

ABSTRACT

Research activities in active noise control (ANC) continue to expand with new developments being reportedin scientific journals on a regular basis. Although there are a number of commercial applications that havebeen implemented successfully, the widespread application of ANC technology remains elusive and one isleft wondering whether ANC is ever going to be a truly practical noise control tool. In this paper, recentdevelopments in ANC technology are reviewed and the technical problems still standing in the way ofwidespread application are discussed. Progress made in solving some of the problems is summarized, currentinternational research directions are outlined and the future of the discipline is examined. In addition, someof the research currently being undertaken at The University of Adelaide is highlighted.

INTRODUCTION

Research in active noise and vibration control is almost invariably directed at a specific application or at aspecific part of an active system such as signal processing or actuator design. Very little success has beenreported on the development of complete generic systems that can be used for a range of applications. Eachnew application requires a significant research and development effort to achieve any success. Anapplication may be defined as "new" even if it is a similar physical system but with a different type of noiseproblem. End users of any technology would like to be able to use it without having much understandingof the physics behind its operation.

With passive noise control, in many cases, there is a simplicity that makes it attractive. Even thoughit may require someone with expertise in acoustics and vibration to design passive noise control devices, itdoes not require anyone with this sort of expertise to install a muffler in a duct or engine exhaust, a noisebarrier along a highway, an enclosure around a machine or a set of vibration isolators under a machine. Theitem responsible for controlling the noise is visually apparent and the physical mechanism is easilyunderstood by anyone with an engineering background or technical education. On the other hand, expertisein signal processing, electronics, acoustics and vibration is necessary to ensure a successful installation ofan active system and this makes it expensive and suitable only for special applications where passive controlis impractical or comes with a large cost penalty (such as weight in an aircraft).

In a previous paper on the future of active control (Hansen, 1997), it was predicted that it could be "10or 20 years or even longer before we see active noise control used routinely in consumer products, vehiclesand in industry". Nothing has happened in the past six years that would contradict this prediction.Nevertheless, there have been significant advances in many aspects of active control and the day when it willbe in common use is getting closer. Continuing research on all aspects of the general technology as well ason specific applications is needed if progress is to be made. Continuing advances in computer processor

power and reductions in costs of this power and advances in actuator and sensor technology are also helpingto make active control technology more practical and affordable.

What is needed to generate a significant increase in the use of active control technology is theavailability of an inexpensive, clever, commercial control system which includes a selection of source andsensor transducers to satisfy most problems as well as software to guide users in the correct choice andlocation of such transducers. The word “clever” used above to describe the commercial control system whichdoes not yet exist needs some explanation. If a controller is to be useful to a wide range of people, the effortinvolved in setting it up must be very small. This means that the controller must itself be controlled by a highlevel expert system or neural network which automatically sets input and output gains to maximise systemdynamic range, convergence coefficients to optimise convergence speed and stability trade-offs, control filtertype and weight numbers to optimise noise reduction, as well as leakage coefficients and system IDalgorithms, filter types and configurations to maximise controller performance and stability. In addition, thecontroller should also be able to perform as an adaptive feedforward or adaptive feedback controller, beextendable to a large number of channels simply by adding together identical modules and provide adviceon the suitability of feedforward control compared to feedback control based on the quality of the availablereference signal. Finally the controller/user interface during set-up (which should really be a question/answersession) should be Microsoft Windows based for maximum flexibility. The ability to connect a modem tothe controller to allow remote access and interrogation of current performance and the state of transducersand other system components is also an important labour saving device. Also, to make the controller moreuniversally useful, it should be capable of being programmed with new algorithms and filters by anyone witha knowledge of the "C" programming language.

Software must also be developed which is user friendly and allows the user to determine optimumcontrol source and error sensor types, configurations and locations based on measured transfer functionsbetween potential control source and error sensor locations as well as a measure of the primary field strengthat these locations.

If the field of active noise control is to continue to grow, and if industry is to lose its suspicion of thistechnology, there needs to be considerably more effort devoted to the development of user friendlycommercial systems which allow non-expert or at least semi-expert personnel to successfully install them,in most cases by following a carefully prepared manual. There also needs to be more honesty and lessunfounded optimism in statements made in the media about the potential applications of the technology.

In this paper, the basic components of an active control system will be introduced for the purpose ofproviding context to the discussion on recent advances and continuing research that is to follow. Thediscussion will focus on work presented at "Active 2002" conference in Southampton in July, 2002 as wellas current work being undertaken at The University of Adelaide.

BASIC SINGLE CHANNEL ACTIVE CONTROL SYSTEM ARRANGEMENT

Active noise and vibration control systems can be divided into two basic types: feedforward and feedback.Each of these types can be further sub-divided into adaptive and non-adaptive as well as analogue and digitaland also frequency domain and time domain controllers. Then there are hybrid systems that incorporate botha feedback and feedforward part or an analogue and a digital part. Other types of hybrid system incorporatepassive as well as active control (for example, active/passive mufflers). Each type of system has itsadvantages and disadvantages and each is the optimum solution for a particular application.

It is useful to begin with an overview of single channel adaptive feedforward and feedback control.Non-adaptive feedforward systems are generally impractical for most industrial applications, because of thetime-variability in the physical system being controlled, and thus will not be considered further here.However, non-adaptive feedback systems are often used for ear-muffs and seem to work very well in thisapplication. The simplest example to consider for the illustration of the principles of feedforward andfeedback control is the active control of plane wave sound propagation in a duct.

A single-channel adaptive feedforward active noise control system (the most common type) consistsof a reference sensor, a control source, an error sensor, a control algorithm and an electronic controller, asillustrated in Figure 1(a). The reference sensor samples the incoming signal which is transmitted to thecontroller and processed to produce the desired cancelling signal to drive the control actuator (loudspeaker

ReferenceSignal Control

SignalError

Signal

Referencemicrophone

Errormicrophone

ControlSignal

ErrorSignal

Errormicrophone

ElectronicController

ElectronicController

Fig. 1. Basic active noise control systems. (a) Feedforward system (b) Feedback system

Conditioningelectronics

ReferenceSignal

TachometerSignal

ControlSignal

ErrorSignal

Errormicrophone

ElectronicController

Fig. 2. Basic active noise control system with tachometer reference signal.

in this case). The controller usually consists of an adaptive digital filter, and an adaptive algorithm that setsthe weights in the adaptive digital filter. The error sensor senses the remainder of the signal after control andprovides an input to the control algorithm that allows the control filter weights to be updated to minimisethis signal.

The signal processing time of thecontroller must be less than the time for theacoustic signal to propagate from thereference sensor to the control source forbroadband noise control, but for tonal noisecontrol, there is no maximum permittedprocessing time as the signal is repetitive.The cancellation path is the electro-acoustic path from the loudspeaker input tothe error microphone output. The transferfunction of this path must be taken intoaccount in most controller algorithms andthus it must be measured for every installedsystem. Indeed, it is essential in mostpractical commercial systems that somemeans is implemented in the controller tomeasure this transfer function regularlywhile the controller is operating as it canchange quite quickly in some cases.

Because of their inherent stability,feedforward controllers are generallypreferred over feedback controllers when areference signal which is correlated with the error signal is available. One exception is the active ear muffcase for which it has been found that an adaptive feedback controller seems to cope better than an adaptivefeedforward controller to head movement of the wearer [1]. One disadvantage of feedforward controllerswhich is not shared by feedback controllers is the often encountered problem of feedback of the controlsource output to the reference sensor via an acoustic path. Unless this is compensated for in the controlleradaptation algorithm and controlfilter, instability is likely to result.Appropriate algorithms and filters arediscussed in the next section. Oneway of avoiding the problem is to usea non-acoustic reference sensor suchas a tachometer on the machinecausing the noise, as shown in Figure2.

The electronic controller part inFigure 1(a) is the only component thatis different for a time domain compared to a frequency domain system. The frequency domain system is onlysuitable for tonal (single or multi) sound and involves converting the time domain signal to the frequencydomain using narrow band digital filters or an FFT. The required cancelling signals for each of the variousfrequency components are then combined and converted to a time domain signal prior to being outputthrough the control transducer. The advantage of the frequency domain controller is the ability to treat eachtonal frequency individually, thus optimising the convergence speed of the controller. Johanssen et al.(2000) have used frequency domain control to reduce interior noise in propeller driven aircraft.

For a time domain system, the electronic controller for the single channel system shown in Figure 1(a)consists of an analog to digital converter for the reference signal and one for the error signal. The referencesignal is passed through a digital filter to produce the control signal for the loudspeaker which is passedthrough a digital to analog converter and smoothing filter prior to being fed into the loudspeaker amplifier.

w(k�1) � w(k) [1 � µα ]�2µe(k) [c(k)�x(k)] (1)

µ �β

x T(k)x(k)(2)

z-1 z-1 z-1

w1 w2wL-1w0

� � �+ +

++ ++

x(k) x(k-1) x(k-(N-1))

SampledInput

Outputy(k)

Fig. 3. FIR filter architecture.

�

z-1

z-1

z-1

z-1

z-1

z-1

b1

a1

b2

a2aM

b0

+ +

+

++

+

+

+

+

+

x(k) x(k-1)

y(k)y(k-1)y(k-2)y(k-M)

x(k-(N-1))Sampled

Input

Output

�

�S S

bN-1

�++

Feedforward part

b +b z +b z +...0 1 2-1 -2

Feedback part

a z +a z +...1 2-1 -2

SampledInput Output

Fig. 4. IIR filter architecture.

The digital filter weights are adjusted by an algorithm that uses the reference and error signals as inputs. Thealgorithm also needs to take into account the impulse response of the path between the control source outputfrom the controller and the errormicrophone input to the controller.Estimation of the characteristics of thispath is known as "cancellation pathmodelling" and this is one area ofcontinuing research. The simplest typeof digital filter used in ANC systems isthe finite impulse response (FIR) filterillustrated in Figure 3. In the figure, z-1

represents a delay of one (input)sample, x(k) is the input signal at timesample, k and wi represents filter weighti. The structure in Figure 3 is sometimes referred to as a transversal filter, or a tapped delay line. The numberof "stages" in the filter (the number of present and past input samples used in the output derivation) isusually referred to as the number of filter "taps".

The filter weights are updated at each time sample using the following equation:

where w(k) is the vector of filter weights at time, k, e(k) is the error signal at time, k, c(k) represents thecoefficients of the FIR filter used to model the cancellation path (see Hansen, 2001), α is a leakagecoefficient to prevent weight build up as a result of quantisation errors and µ is the convergence coefficient.This filter weight update equation is referred to as the filtered-x leaky LMS algorithm. If the α parameterwere set equal to zero, it would be called the filtered-x LMS algorithm. In many cases it is desirable tonormalise µ with the reference signal as follows:

where β is a constant between 0and 2. When this is implemented,the algorithm is called thenormalised filtered-x LMSalgorithm.

Another filter type that iscommonly used where there is apossibility that the reference signalmay be contaminated by thecontrol source sound is an IIRfilter, illustrated in Figure 4, whichhas a structure that attempts tocompensate for the feedback fromthe control source to the referencesensor.

A feedback system isillustrated in Figure 1(b). For anadaptive system, the electroniccontroller is an adaptive filter andalgorithm, whereas for a non-adaptive system, the electroniccontroller consists of a fixed lowpass filter and an amplifier. Non-adaptive systems are generally impractical for most industrial applications(except perhaps for active ear muffs) because of the variability in the physical system being controlled.

Referring to the figure, it can be seen that the error microphone picks up the incoming signal which isprocessed to derive a suitable control signal for the control source such that the error signal is minimised.Thus it is clear that this type of controller will function best when the predictability of the incoming signalis good. Thus resonances in structural and acoustical systems can be controlled, and noise which has a highnormalised autocorrelation for any time delay greater than the delay through the control system (whichincludes the control source and error sensor) is also controllable. Tonal noise is characterised by a highautocorrelation and thus is well suited to feedback control.

Adaptive feedback controllers are usually implemented digitally using a DSP chip, as implementationusing analog circuitry is not really practical. On the other hand, non-adaptive feedback controllers areusually implemented in analog circuitry to minimise the delay through the controller, thus increasing themaximum autocorrelation value of the noise to be controlled and hence the maximum achievableperformance. A disadvantage of feedback controllers is their inherent instability at higher frequencies wherethe phase response is not easily controlled. This can cause serious acoustic noise problems in the presenceof high frequency noise or if the physical system being controlled changes too much from the designcondition (for non-adaptive feedback control) or too rapidly between states (for adaptive feedback control).An example may be the unstable oscillation (or screeching) in an active headset with analog control as it isadjusted on the head of the wearer or if the wearer enters an environment characterised mainly by impulsivenoise or high frequency noise. The instability problems of these types of controller are usually minimisedby keeping the controller gain within reasonable bounds, (which has the effect of limiting the controllernoise reduction performance) and using low pass filters to attenuate incoming high frequency signals whichcannot be controlled. To maximise robustness (or minimise instability problems) it is essential that themicrophone be placed as close as possible to the control source which will have the effect of minimisingsystem delays and thus maximising the autocorrelation of the noise at time delays greater than the controlsystem time delay. The disadvantage of placing the error sensor close to the control source is that becauseof near field effects, the sound pressure at large distances from the error microphone may not be significantlyreduced. This is not a problem, of course for active ear muffs because of the close proximity of the ear drumto the error sensor.

RECENT ADVANCES IN ACTIVE CONTROL

Current research in active control can be divided into the following categories: generic system design,distributed control, algorithms (both control and cancellation path modelling), sound quality (includingvirtual acoustics), sensors and actuators (including virtual sensing), semi-active systems and applications.Current work being undertaken will be discussed under these headings.

Generic System Design. One of the most difficult issues facing researchers and consultants in the activecontrol field is the large development time needed to keep up with advances in processing speed and memorycapability of the electronic hardware used to implement the control systems. Modern DSP chips have sucha high pin density that printed circuit board manufacture requires special expensive tooling which is onlyeconomical if boards are manufactured in large quantities. There are three approaches that may be used. Thefirst approach is to use an embedded system that is based on a Pentium 4 processor and a Windows 2000interface. The advantage of a system such as this is that once it is programmed, the hardware can beupgraded as new Pentium processors are released without having to rewrite any software. Unfortunately,the huge overheads associated with Windows 2000 and the non-DSP nature of the Pentium processor meanthat such a system is relatively slow and lacks the capability of the second and third approaches describedbelow.

Second, a special purpose ANC system could be purchased from one of the few manufacturers of suchsystems. At the moment, these special purpose systems cannot be programmed with new algorithms by users,although they can be applied to many different types of problem. The difficulty with special purpose ANCsystems is that they are expensive, because very few are sold, resulting in a high unit cost for developmentand production. It is expected that the next generation of special purpose ANC systems will be sufficientlypowerful to allow very large numbers of filter taps and channels as well as a high enough processing speedto implement feedback as well as feedforward control, while allowing users to implement special algorithms

Fig. 6. Low cost inertial actuator

Fig. 5. Thin loudspeaker in aircraft trim panel.(Courtesy Ultra Electronics)

using the "C" programming language. Less flexible systems are currently available that allow users toprogram controller parameters through a graphical user interface (Qiu, et al., 2002).

It is likely that the next generation of controllers will comprise a central DSP board and separateinput/output boards housed in a single enclosure. Users will also need band pass filters for the input toimprove the performance of the controller in the frequency range of interest as well as signal conditioningelectronics for reference signals generated by tachometers. Of course power amplifiers are always neededto drive the loudspeaker or vibration control sources. Such general purpose controllers are much tooexpensive for consumer products; for these, special purpose low cost solutions will need to be developed.This may be possible for simple systems when millions of units are being produced. Unfortunately each timea more powerful processor is released, the development process has to be repeated in both hardware andsoftware.

The third approach is to use general purpose DSP boards that are built into a stand-alone unit or plug-into a PC. The main disadvantages are that the boards are relatively expensive, they include a lot of hardwarenot needed by ANC systems, they often have specifications that cannot be relied upon and require backplanecircuits for communication between DSP boards, filter boards and input/output boards. In many cases,communications between DSP boards and I/O boards is not always reliable. These systems need to beprogrammed from scratch using the "C" programming language and suffer from the problems of the secondapproach whereby the software development has to be repeated each time the hardware is upgraded.

It is worth noting that in practical applications of active noise control outside of the laboratory, aconsiderable proportion of the software development is for general house-keeping rather than being directlyrelated to active noise control. Such requirements include safeguards to prevent excessive noise in the eventthat the controller becomes unstable, regular measurement of the cancellation path transfer function,detection and automatic indication of failed actuators andsensors, detection of overloads on inputs and outputs andautomatic resetting of the controller and/or disconnection oftroublesome inputs or outputs.

Actuators. In recent times, the availability of new materialshas opened up the scope for novel actuator systems suitedspecifically to active control applications. Loudspeakercones made from polypropylene, carbon fibre and aluminiumare available for hostile environments and low frequencyapplications. Lower cost alternatives have involved the useof paper cone loudspeakers sprayed first with engine gasketmaterial and then with metallic paint (Leclercq, 2002). Forsome applications, special loudspeakers have beenspecifically developed. An example is the flat speakerdeveloped by Ultra Electronics to fit behind aircraft trimpanels (see Figure 5).

Inertial actuators are becoming more popular in activecontrol systems involving the reduction of structuralvibration due to their convenience in application, ease oftuning so that their resonance frequency closely matches thefrequency of the tone to be controlled and in some cases theirlow cost. A low cost inertial actuator that has been used foractive vibration isolation (Lee, et al., 2001) is shown in Figure6. Another inertial actuator recently developed by UltraElectronics for their aircraft active noise control system isshown in Figure 7. This actuator is applied to theframe/stringer interfaces in the aircraft fuselage and reducesthe frame and fuselage vibration in such a way as to reduce thenoise radiated to the aircraft interior. To reduce the sharpnessof the resonance peak of the actuator, a velocity feedback

Acoustic Foam

Piezoelectric Film

Electrical Leads

Acoustic Foam Radiating Structure

Radiated Sound

Smart Foam Cell

Fig. 8. Configuration for a smart foam actuator (Courtesy Fuller, 2002a)

Fig. 7. Inertial actuator used by Ultra Electronics for turbo-prop aircraft active noise control. (Courtesy Ultra Electronics)

2“ 5“

4“

R 1-3/4“Weight = 36.1 g

4-3/4“

3“ 4-1/2“

R 2“

3/4“

Weight = 52.5 g

Small Actuator (NASA expts.)

Large Actuator (VPI expts.)

0 250 500 750 1000 1250 1500 1750 200090

100

110

120

130

140

Frequency (Hz)

Soun

d p

owe

r lev

el (

dB

re 1

0-12

W)

Fig. 9. Sound power output of smart foam actuators. (Upper curve represents the large actuator)

system was implemented on the actuator, which hadthe effect of increasing the actuator damping. Otherinertial actuators have been used to reduce gearboxnoise radiated from patrol boats and engine noise inluxury yachts. However, none of theseexperimental systems have yet been implementedpermanently on any vessel.

Piezo-electric crystal actuators (both stack andthin plate types) and PVDF sensors are now widelyavailable at a relatively low cost, making theconstruction of "smart" structures technicallyfeasible and also economically feasible for someaerospace applications. PVDF is a flexible piezo-electric film that is low in cost and low in its forceproducing capability; thus, it is usually only used asa sensor and not as an actuator.The one well known exceptionis the use of PVDF as anactuator embedded in smartfoam (Gentry, 1998; Fuller etal., 2000, Marcotte, et al.,2002) and used to modify theradiation impedance of avibrating surface. Such anactuator is illustrated in Figure8. The radiation efficiency ofthe actuator may be increasedby placing it in a balsa woodbox. The sound power outputcapability of twoactuator designs isshown in Figure 9.A d i s t r i b u t e dversion of the smartfoam actuator isshown in Figure10.

Another typeof actuator is ap i e z o - e l e c t r i celement used as theactive part of anactive/passive tilefor reducing launchnoise transmissioni n t o l a u n c hv e h i c l e s a n dpreventing damageto sensitive satellitepayloads. This is illustrated in Figure 11.

Piezo-electric actuators suffer from non-linearities that affect the performance of active vibrationattenuation systems. Pasco and Berry (2002) developed a non-linear model to describe the behaviour of apiezo-electric stack actuator. They conducted active isolation experiments on a single-axis isolator for

Mass DistributionActive elastic

layer

Structure

Fig. 10. Configuration for a smart foam actuator (Courtesy Fuller et al., 2000)

Fig. 12. Vibration isolation or structural interface element (Courtesy, A. Premont, 2002)

Fig. 11. Piezo-electric actuator configured to act as an acoustic tile. (Courtesy Goldstein and Fuller, 2002)

single-frequency perturbations using a piezoceramic stackas control actuator and a feedforward controller. Theyfound that the best control results were obtained using asynthesised single-frequency reference passing throughthe non-linear model of the stack in order to generateharmonic components in the control input. In this case,the isolator proved to be effective both at the fundamentaland higher harmonics of the disturbance. They implied

that they have also had success using thesame approach on a MIMO 6-axisisolation platform based on a cubicStewart platform.

Magneto-rheological fluids arecurrently a popular choice of actuator insemi-active dampers and have proved tobe much more effective than electro-rheological fluids (Sapinski and Pilat,2002; Sireteanu, et al., 2002). When amagnetic field is applied to such a fluid,magnetic particles within it becomealigned and the fluid becomes moreviscous. These fluids are ideal in damperapplications where a variable dampingforce is needed to continuously minimisethe vibration transmission through thedamper.

For vibration isolation applications,a special purpose 6-degree-of-freedomactive element based on piezoelectricstack actuators terminated with flexibletips is now commercially available(Horodinca, et al., 2002; Premont, 2002). This actuator is illustrated in Figure 12.

Fig. 15. Microphone assembly used by Ultra Electronics in their aircraft ANC system (Courtesy Ultra Electronics)

Fig. 13. Soft, 6 DOF active isolation mount using voice coil actuators(Courtesy Ahmed, 2002)

Fig. 14. Flexible joint to connect the actuators tothe frames (Courtesy Ahmed, 2002)

For applications requiring a softer mount, a system that uses voice coil actuators (Ahmed, 2002) asillustrated in Figure13, may be used. In these systems, the design of the flexible joints that connect theactuators to the frames is very important. Ahmed (2002) used a membrane element in the connection toprovide the required flexibility and this is illustrated in Figure 14.

Sensors. Special purpose microphones have been developed especially designed for rugged environments.The basis of most microphones that are used is a low-cost pre-polarised electret element. Temperature-compensated elements are the most suitable and these are available for tens of dollars. The expense comesin all the hardware that must surround the electret element to enable it to survive a typical ANCenvironment. An illustration of a typical construction is shown in Figure 15.

Other sensors include PVDF piezoelectric film for vibration sensing and industrial accelerometers, also forvibration sensing.

Radiation Mode Sensing and Control. For some time (Morgan, 1991; Cazzolato and Hansen, 1999),researchers have been controlling sound radiation from structures into free field and into enclosures bycontrolling structural radiation modes rather than structural vibration modes. Structural radiation modes aremade up of various proportions of a range of structural modes and represent structural vibration patterns thatare orthogonal in terms of their contribution to sound radiation, as opposed to structural vibration modes that

J � E{e T(t) e(t)} � β1tr. ��

0

W T(t) W(t) dt � β2 E u T(t) u(t) � β3 E s T(t) s �(t) (3)

are orthogonal in terms of their contribution to structural vibration levels. Radiation modes are derived fromthe structural vibration modes using an orthonormal transformation as described in the preceding references.Their mode shapes are weakly dependent on frequency and their radiation efficiencies are quite stronglydependent on frequency. However, as the cost function to be reduced is the level of sound radiation and notthe amplitudes of the radiation modes, the effect of the radiation efficiencies of the radiation modes is takeninto account by pre-filtering prior to passing the modal amplitude data into the controller

When controlling sound radiation from vibrating structures, it is common to use inertial actuators orpiezo-electric patch actuators. Large patch actuators can act as spatial filters and reduce spillover, whichis the introduction of unwanted energy into higher order structural modes as a result of active control. Whenmultiple actuators are used, their positions can be optimized in such a way that they only excite the structuralmodes that can be measured by the error sensors, thus minimising the spillover effect. However, theoptimum actuator locations, when a small number are used, are quite sensitive to changes in the system beingcontrolled. Thus Berkhoff (2002a) has suggested the idea of using fixed actuator arrays, containing manyactuators, where tuning for optimum control in different environments or application areas is achievedthrough modification of the controller coefficients. Berkhoff describes a tuning method for the case wherethe number of actuators exceeds the number of error outputs. It is based on the use of modal error signalswith frequency dependent weightings, which are optimized for acoustic radiation (radiation modes) plus theuse of novel actuator constraint conditions. Traditional actuator constraints involve including the actuatorefforts in the cost function or including the control filter weights. Berkhoff's novel method is to include inthe cost function, the actuator contribution to radiation modes that are efficient in the frequency rangeoutside of the range to be controlled and inefficient in the range to be controlled. Note that these modes areorthogonal (in terms of sound radiation) to each other and also to the radiation modes that are beingcontrolled. Berkhoff's general cost function, which includes all possible actuator constraints may be writtenas (Berkhoff, 2002a):

The first term in the equation corresponds to minimisation of the error signals, which in this case areradiation mode amplitudes. The second term corresponds to minimising the control filter weights with aweighting coefficient of β1 , the third term corresponds to minimising the controller output with a weightingof β2 and the fourth term corresponds to minimising the cancellation path signals that are not measured bythe error sensors. This last term corresponds to minimising the controller contribution to radiation modesnot sensed by the error sensors.

Using his method, Berkhoff demonstrated a feedback controller design for a honeycomb sandwichpanel of dimensions 0.6 m × 0.75 m with 16 piezoelectric patch actuators and 16 accelerometers. The panelwas excited with a sound pressure signal that was recorded in-flight in a jet aircraft. He obtained a largereduction (9.5 dB overall) in broadband radiated sound power (up to 450 Hz) with no significant evidenceof spillover outside the controller bandwidth and a huge performance improvement over the use of actuatoreffort control by itself. Best results were obtained by setting β1 = 10-4, β2 = 0 and β3 =30. However, evenbetter results (11 dB overall reduction) were obtained by configuring the actuators to drive only the modescontributing to the low frequency sound radiation (below 450 Hz) with no constraint on the actuator output.

Virtual Sensing. This sensing method involves estimating the sound field at a location remote from thephysical sensors and using an active control system to minimise the sound field at the remote location. Thereare two ways of estimating the remote sound field. The first involves measuring the transfer functionbetween a microphone placed temporarily at the remote location and the physical error microphone (Garcia-Bonito, et al., 1996, Holmberg et al., 2002). The second involves estimating the sound field at the remotelocation using a linear extrapolation or higher order extrapolation based on the signals from the physicalsensors (Kestell, et al., 2000) as shown in Figure 16.

The extrapolation was implemented in practice by weighting the outputs of the physical microphonesby different amounts and then adding the weighted outputs together to provide an estimate of the soundpressure level at the remote location. The weights were determined using simple linear or quadratic

Linear Extrapolation Quadratic Extrapolation

212

2Px

hPPPv �

�

� Pv �

x(x � h)2h 2

P1 �x(x � 2h)

h 2P2 �

(x � h)(x � 2h)2h 2

P3

Fig. 16. Illustration of linear and quadratic extrapolation from the physicalmicrophones, P1, P2 and P3 to the virtual location, Pv

1PressureEstimate

Error

W5

Weight5

W4

Weight4

W3

Weight3

W2

Weight2

W1

Weight1

6DesiredLocation

5Mic5

4Mic4

3Mic3

2Mic2

1Mic1

PressureEstimate

Desired Pressure

Error

Fig. 17. Adaptive LMS microphone (Courtesy Cazzolato, 2002)

extrapolation formulae(Kestell, et al., 2001),shown in the figure.Previous work found thatthe accuracy of theextrapolation in anenclosure was affected byshort wavelength spatialnoise due to the forcedresponse of resonantenclosure modes at thetest frequencies. This alsomeant that in practice, thelinear prediction methodgave better results thanthe quadratic prediction.

In an attempt to obtain better results, more physical microphones were used and a least squares fit wasused to estimate the sound pressure level at the remote location (Munn, et al., 2002). However, this amplifiederrors caused by inaccuracies in the physical microphone locations as well as errors caused by phase andamplitude mismatches between microphones in the physical array. To minimise the effect of these errors,Cazzolato (2002) presented a method that involved placing a temporary physical sensor at the remotelocation and then using an adaptive LMS algorithm to determine the weights to be applied to the physicalerror microphones thatwould result in the sum ofthe weighted signals fromthe error microphones beingequal to the signal from thetemporary microphone asshown in Figure 17. He usedsimulations in a 1-D soundfield to show that theadaptive system cancompletely compensate foramplitude mismatches andspatial errors, and mayhelp in compensating forphase mismatches betweenthe microphone elements.Experimental verification ofthe simulation results iscurrently in progress. Inaddition, the work iscurrently being extended to3-Dimensional fields and tovirtual energy density sensing.

Decentralised Control. One of the problems associated with the control of complex sound fields or complexstructural vibration using traditional active control approaches is that a large number of control channels areneeded to handle the large numbers of sensors and actuators that are required. A controller with a largenumber of channels needs a large amount of computing power, can be slow to converge and may sufferstability problems. There are four main approaches that are currently being researched to reduce the numberof dependent controller channels, while at the same time maintaining the same number of sensors and controlactuators.

intensity measurementsto estimate radiated power

8 accelerometer signals

plate

modalfiltering

Q ZT -1�

1-3 multipolesignals

errorsignals

sensing system

adaptivealgorithm

IIR controlfilter

randomnoise

lowpass filterat 325 Hz

disturbance input

control input

Fig. 18. Burgan et al's multipole sensing system

Fig. 19. Results obtained using Burgan et al's multipole sensing system

The simplest approach is to partition the control system into a number of independent controllers, eachof which is concerned only with the control sources and error sensors from part of the total system. Thepartitioning may be based on the physical locations of the control sources and error sensors as done byCordioli et al. (2002). They investigated the effect of partitioning a controller for a large electricaltransformer into four independent controllers, one for each side. They also examined the case of twoindependent systems for each side, a total of eight independent systems. The analysis used quadraticoptimisation and measured transfer functions between control sources and error sensors. They found thatthe controller performance decreased as the number of independent control systems increased. Thepartitioning could also be done on the basis of selecting actuator/sensor pairs where the sensor signal wasdominated by the particular actuator output. Alternatively a genetic algorithm could be used to determinewhich actuators and sensors should be clustered into a single independent controller.

A second approach involves pre-processing the sensor and actuator signals to produce a smallernumber of control system inputs and outputs. This is done by describing the structural radiation as beingdue to a combination of simple sources; that is, a monopole, a dipole, a quadrupole, etc. The variousmultipoles may be realised using a uniform distribution of simple monopoles over the radiating surface andphasing them appropriately to produce the required multipole. For example, if all of the sources were inphase, then they would represent the global monopole. If a group consisting of half of the monopole sourcesall located on one half of the structure were 180 degrees out of phase with the remaining monopoles on theother half of the structure, then they would represent the global dipole.

Burgan et al. (2002) used vibration sensors on the structure to estimate the relative contributions ofeach of these multipole source types (up to order 8) to the radiated sound field. He found that, similar to thebehaviour of structural vibration modes, the multipoles are not orthogonal in terms of their contribution tothe radiated sound field. This means that an orthonormal transformation is required to produce a set ofmodes that are orthogonal in terms of sound radiation. The difference between the resulting set oforthogonal radiation functions and the radiation modes (see previous section) derived using an orthonormaltransformation of the structural vibration modes is that for the multi-pole case, no a priori knowledge of thestructural vibration mode shapes isrequired. Burgan et al. (2002)demonstrated their method using aflat panel in an infinite baffle drivenby a single point shaker input as theprimary source and controlled by asecond single point shaker input.Eight monopole sources were used toderive global multipoles andreductions of broadband noise weredemonstrated. In practice, the modalfiltering required to provide theorthonormal transformed multipoleamplitudes effectively involvedcombining the eight accelerometersignals together with differentweightings for eachorder of multipole.Burgan's system isshown in Figure 18and the results heobtained with a singlecontrol source areshown in Figure 19.

Hill, et al. (2002)u s e d a s i m i l a rapproach, except that

Fig. 21. Multiple single-channel independent feedback controllers to improvethe low-frequency transmission loss of a panel (from Gardonio et al., 2002)

infinite baffle

10 meter radius

Planar structure

32 sensors evenly distributed at each of = 10, 20, 30, 40 degrees�

128 sensor signals

�n-1 Modal filtering to resolve

multipole amplitudes

1-8 error signals(multipole amplitudes)

minimize theweighted signals

Fig. 20. Acoustic multipole sensing system of Hill et al. (2002)

Fig. 22. Possible configurations for collocated piezo-electric sensors and actuators for feedback control (from Lee, et al., 2002)

the sensing system used to derive themultipole amplitudes on the radiating panelconsisted of an array of microphonesmounted above the panel. His system isshown in Figure 20.

A second approach for reducing thenumber of dependent controller channels isdescribed by Gardonio, et al. (2002) whoused a large number of independent singlechannel feedback controllers to controlsound transmission through a panel asillustrated in Figure 21. He used piezo-electric crystal actuators (PZT) as controlsources and an accelerometer mounted ontop of each actuator to provide the errorsignal for each controller.

When feedback controllers areused, it is important that the sensorand actuator are collocated tominimise instability problems(Preumont and Bossens, 2002).Recent work (Variyart, et al., 2002)has discussed the optimisation offeedback gain and structural kineticenergy control. Lee, et al. (2002a)investigated five different arrangements as shown in Figure 22 for collocation of sensors and actuators andshowed that most configurations are characterised by the problem of coupling effects between the sensorand actuator that are additional to the coupling due to the bending of the structure. The worst configurationwas type 5 and the best was type 1. Lee et al. (2002b) also showed that for a real structure, the in-planecoupling between the sensor and actuator, severely limits the performance of collocated configurations andmust be included in any analysis in addition to out-of-plane coupling.

An interesting recent application of collocated control is illustrated in Figure 23, where two collocatedposition actuators (piezo-electric stacks) and force sensing are used to supplement passive vibration isolationfor a lens mount in a microlithography machine used by IC manufacturers.

A method for reducing the order of the control system that has the simplicity advantages of the single-channel feedback controllers described above, yet at the same time can optimise the various feedback gainsto satisfy a global rather than local cost function is described by Fuller (2002a) as a hierarchial controller.

Fig. 24. Schematic of hierarchical control arrangement. (Courtesy Fuller, 2002b)

ψtot(t) � ψk(t) � ψp(t) �ρ2u 2x (t) � u 2

y (t) � u 2z (t) � p 2(t)

(ρc)2(4)

Fig. 23. Collocated actuators and sensors used for vibration isolation of a lens mount.(From Holterman, et al., 2002). The black arrows indicate the direction of actuation andsensing.

This controller consists oflocal, independent single-c h a n n e l f e e d b a c kcontrollers acting oncollocated sensor andactuator pairs. Thesemultiple independentcontrollers all haveprogrammable feedbackgains which are providedby the local controllershown in Figure 24. Thetop level controllerprovides the input to thelocal controllers so that theindependent controller gains areadjusted to minimise a global costfunction such as minimisation ofradiation mode amplitudes.

Enclosure Noise Control. Thecontrol of noise in enclosures such asmachinery rooms, truck cabins andaircraft cabins is now fairly wellunderstood and the feasibility ofcontrol can be determined from thephysical situation. The questions thatmust be answered before thefeasibility of active noise control canbe estimated are the following:• Are the enclosure walls rigid or

flexible?• Are the noise sources outside the

enclosure or within it?• How large is the enclosure compared to the wavelength at the frequency to be controlled?• Is the noise to be controlled periodic, tonal or broadband?• Is the enclosure acoustic field well damped or only lightly damped?Answers to the preceding questions will determine whether or not it is possible to achieve global soundcontrol, local control or no significant control. Guo et al. (2002) discussed the boundaries between theachievement of local or global control. Valentini and Scamoni (2002) reported some success with bothtransparent and opaque active windows and are currently developing a commercial version that will becapable of reducing periodic noise transmission (such as transformer hum) through windows and openingsin buildings.

When actively controlling acoustic fields, the most common quantity to minimise is the acoustic pressureat one or more locations. When only local control is possible due to a high modal overlap in an enclosure,minimisation of acoustic pressure can provide very high levels of reduction very close to the sensor at theexpense of increases elsewhere in the enclosure. This is undesirable as the large pressure gradient in thevicinity of the sensor is subjectively unacceptable. For this reason, energy density, ψtot , as defined inEquation (2) as the sum of the kinetic and potential energies is emerging as the preferred quantity tominimise.

This effectively requires the minimisation of particle velocities (or pressure gradient) in three orthogonaldirections as well as the acoustic pressure. The result is a much lower pressure gradient in the vicinity ofthe error sensors, a smaller reduction in pressure at the actual error sensor and a larger volume over whichsubstantial noise reduction is obtained (sphere of radius half a wavelength as opposed to one-tenth forpressure only minimisation).

Algorithm Improvements - Robustness. Performance and Convergence Speed. Improvements toalgorithms continue to be made in an effort to reduce computing power requirements (thus allowing fastertracking of rapidly varying systems), increase stability and increase noise reduction performance. However,many newer algorithms require detailed adjustment of parameters for each new application.

Ishimitsu and Elliott (2002) classify existing gradient decent adaptation algorithms into two maincategories: one which minimizes the mean square error and the other which minimizes a filtered version ofthe mean square error, thus leading to a biased result. They provide a good summary of the performance inreducing low-frequency sound in a ship of a range of algorithms (the filtered reference LMS algorithm,thefiltered error LMS algorithm, the filtered-LMS algorithm, the filtered reference - filtered error LMSalgorithm and the preconditioned LMS algorithm), after correcting one of them to satisfy causality duringthe update process. For their application, they found that the pre-conditioned LMS algorithm (Elliott, 2002)exhibited the best performance and convergence rate.

Meurers and Veres (2002) presented an algorithm for tonal feedback control with estimation of thesecondary path dynamics. In this method, the tonal components are estimated in advance and then a gradientapproach is used to update controller and model coefficients for each tonal frequency to be controlled. Thisapproach avoids a priori modelling, which is a characteristic of feedback control systems, and no referencesignal, which is an essential part of a feedforward system, is needed. Also for each frequency to becontrolled, there are only two controller and model coefficients to be tuned, representing amplitude andphase shifts. The disadvantage of this method is that the error signal needs to be passed through one filterfor each frequency component and an amplitude and phase of each determined. These are then used by thealgorithm to calculate the required amplitudes and phases of the frequency domain control signal for eachfrequency. The required time domain control signal for each frequency is then determined using

, where urn and uin are the real and imaginary coefficients determinedu(t) � urn cos(ωn t) � uin sin(ωn t)by the algorithm and ωn is the corresponding frequency.

Qiu and Hansen (2001a) discuss various modifications that must be made to the filtered-x LMSalgorithm to make it work in various applications. They also discuss the application of the waveformsynthesis algorithm to the active control of electrical transformer noise (Qiu et al., 2002). The applicationof control output constraints in practical implementations of the filtered-x LMS algorithm has also receivedattention (Qiu and Hansen, 2001b, 2002a). This allows the limited driving capability of loudspeakers andactuators to be included directly in the control algorithm and ensures that they are never overdriven.

Cancellation Path ID Methods. Cancellation path modelling (the path from the control source output tothe error sensor input to the controller) is an essential part of maintaining the stability of a feedforwardcontrol system. If the cancellation path is time invariant it only needs to be modelled when the controlleris started up. However, there are many cases where the cancellation path impulse response varies with timeand its model must be updated on a regular basis. Using the control signal as the on-line modelling signalis one method that has been used in the past, but this is really only successful when there is only one controlsource, due to interference from other control source signals in a multi-channel configuration and due to thecorrelation of the control signal with the primary noise. The other way of on-line modelling that has beencommonly used in the past is to introduce low level random noise into the system through the control sourceswhen the model needs updating. This can cause loss of controller performance and can be subjectivelyannoying, so it has met with varying degrees of success.

Veena and Narasimhan (2002) successfully used an escalator lattice predictor based noisecanceller for modelling the cancellation path for both feedforward and feedback systems in thepresence of narrowband noise fields. Their method uses the control signal for modelling, but unlikethe traditional method, it uses a the noise canceller to remove the influence of the primary noisefield, resulting in a more accurate and more robust estimate of the cancellation path, even for a non-

Dummy head

1

2

E1

E2

I1

C2

I2

C1

Fig. 25. Binaural sound reproduction using inversion filters and cross-talkcancelling filters.

stationary primary noise field.Qiu and Hansen (2002b) took a different approach to use of the control signal for modelling. They

looked at improving the method that involves introducing an external signal into the control outputs, whichis uncorrelated with the primary noise. Although white noise is often used for this, other signals such asswept sine, multi-sine, periodic noise, maximum length binary sequence, multi-frequency binary sequence,pulse, random burst and pseudo random noise have all been used. In addition, various ways of controllingthe level of the introduced modelling signal have been used. One involves using a small modelling signalwhen the cancellation path appears to be changing by a small amount and using a larger signal as the changeappears larger. In addition, researchers have used a modelling signal with a similar spectral shape to theprimary noise to be controlled so that the modelling effort is concentrated in the most important frequencies.Qiu and Hansen (2002b) investigated the optimisation of both the spectral shape and peak amplitude of themodelling signal. They found that low level modelling signals could provide a good cancellation pathestimate with levels less than 20 to 30 dB below the primary noise signal. However, the model took a verylong time to converge to the correct solution. On the other hand, they found that a high level modellingsignal resulted in very fast convergence (less than 0.5 seconds for their system) but the level of the modellingsignal needed to be 10 dB above the primary noise signal for that short time. Thus they suggested that whena system is first turned on or when a large change in cancellation path model is detected, a large modellingsignal amplitude be used to provide a useable cancellation path model in a short time and at other times avery low level modelling signal should be used. They found this method to be much more reliable than useof the control signal for cancellation path modelling.

Semi-Active Control. Semi-active control is the terminology applied to systems that use active control tochange system parameters to reduce the noise or vibration of a system without the introduction of anyadditional control forces or sound. Johnson and Esteve (2002) provided simulation results showing therelative effectiveness of various local cost functions in tuning multiple resonators to reduce the kineticenergy in a cylindrical shell (structural mass-spring resonators) and the potential energy in a cylindricalenclosure (Helmholtz resonators) for excitation by a low frequency external acoustic source. The adaptivealgorithms tuned the stiffness of the structural resonator and the length of the Helmholtz resonator neck. Thebest cost function was found to be the cross product of the absorber mass velocity and the absorber basevelocity for the structural vibration absorber and the cross product of the Helmholtz resonator internalpressure and the external pressure immediately outside its neck.

Monaco et al. (2002) used magnetostrictive active elements in a semi-active vibration absorber toproduce an absorber for reducing structural vibration and sound transmission into aircraft.

Another area where semi-active systems have found use is in vibration isolation systems and aconsiderable body of work exists that is devoted to this topic. Liu et al. (2002) provided a review of existingsemi-active vibration isolation systems and presented a system for shock and vibration isolation of a baseexcited system (eg electron microscope). They showed that their semi-active system performed much betterthan passive systems and had a performance close to the ideal “skyhook” damper

Sound Reproduction. There is considerable current interest in the use of active noise cancellation toenhance the sound reproduction experience so that the apparent direction from which sound comes can becontrolled using signal processingin such a way that sound from twospeakers placed side by side canappear to originate from any desireddirection (Nelson, 2002, Kim et al.,2002). The set up to achieve this isshown in block diagram form inFigure 25. In the figure, the actualsound is recorded using the twomicrophones in a binaural dummyhead. The signal thus recorded isinverted, then passed to speakers to

Fig. 26. Illustration of the effect of frequency and source span on the condition number of theinversion matrix used to reprodice a binaural sound field. The source span is the angle subtendedby the two loudspeakers at the ear. The light or white lines represent a low condition number andthe black lines represent a high condition number. For good reproduction a low condition numberis necessary. The condition number is denoted κ (CN).

reproduce the required sound at the listener’s ears. Inversion is necessary to derive the signal to drive thespeaker so that the signal at the ear will be the same as sensed in the dummy head. In practice, some soundfrom speaker 1 actually appears at ear 2 and similarly sound from speaker 2 appears at ear 1. This is calledcross-talk and this where active noise cancellation comes in; ANC is used to cancel the crosstalk andoptimum algorithms to achieve this are the subject of on-going research. These algorithms are used to adjustthe weights of the digital filters shown as C1 and C2 in the figure. Filter C1 acts on the signal sent to speaker1 (prior to inversion) to produce a signal out of speaker 2 to cancel the speaker cross talk at ear 2 fromspeaker 1. Similarly for filter C2.

An important parameter is the condition number of the matrix that is inverted to produce the signal todrive the loudspeakers. A high condition number results in inaccurate reproduction of the original sound.In Figure 26, the condition number is plotted as a function of frequency and separation between the twospeakers of figure25. It can be seent h a t f o r h i g hfrequencies a lowcondition number(white areas) can beachieved using as m a l l a n g u l a rseparation betweenthe sources, while atlow frequencies alarger separation isnecessary. In thefigure a suggestedscheme using 3 pairsof speakers at 10, 30and 180 degreeseparation results inl o w c o n d i t i o nnumbers over thee n t i r e a u d i ofrequency range.

RECENT APPLICATIONS

Applications that are currently being studied include, but are not limited to, the following: aircraft interiornoise, jet engine noise, sound in rooms, higher order mode propagation in ducts, headsets, engine exhausts,car interior noise and large structure vibration control.

Aircraft Interior Noise. Control of aircraft interior noise may be divided into periodic noise applications(propeller aircraft) and correlated random noise applications. Control of periodic noise has been the subjectof recent research (Johansson, et al., 1999, 2000), in which control is performed on the propeller blade passfrequency and three higher order harmonics using frequency domain control with 32 loudspeakers and 48error sensors. They obtained reductions of 17, 10, 6 and 6 dB for the fundamental and first three harmonicsrespectively, averaged over the error sensors, which were mounted at passenger ear locations. Note thatnoise levels at locations other than the error sensor locations were not included in the evaluation. Johansson,et al. (2002) have also used a similar technique to control the lateral vibration of high-speed train cars andfound that the multiple-reference controller performed dramatically better than the single-reference.

Ultra Electronics (Hinchliffe, et al., 2002) have been installing commercial systems in propeller-drivenaircraft for a number of years and there are now several hundred systems flying. Their active control systemuses up to 96 input channels (reference and error sensors) and has up to 48 output channels for driving the

5 x 3 array of microphones (4 used as error sensors)

Turbulent boundary layer excitation

Actuators

Frames Panel

Tunnel walls xcm reference sensors

Fig. 27. Schematic of smart foam system for controlling boundary layer induced noise in aircraftpanels. (Courtesy, Fuller, 2002a).

Fig. 28. Performance of active smart foam for controlling boundary layer noise. (Courtesy, Fuller, 2002a)

control actuators, which may be inertial actuators attached to the frame/stringer interfaces of the fuselageor loudspeakers mounted in the trim panels. Most of the error sensors are microphones mounted behind thetrim panels (just above window height on the side and along the centreline of the ceiling panel) and also inthe overhead lockers, but when inertial actuators are used as control sources, some accelerometers are alsoused as error sensors to ensure that fuselage vibration levels do not increase. Systems installed by UltraElectronics typically achieve 10 dB, 7 dB and 3 dB noise reductions for the fundamental, first harmonic andsecond harmonic respectively of the propeller blade passing frequency. The measured reductions areaveraged over a plane in the cabin at passenger head height. Greater reductions that mentioned above areachieved at noisier locations and lesser reductions are obtained at locations that were initially quieter.

The control of airflow noise over aircraft fuselages has been the subject of considerable research effortin recent years. As airflow noise is a random phenomenon, it cannot be effectively controlled withfeedforward methods, since it is difficult to obtain a sufficiently time-advanced reference signal that is wellcorrelated to the random disturbance. The advantage of the feedback approach is that it does not require anyexternal referencesignal and has thepotential to beimplemented as adecentralised self-contained controlsystem suitable fort h e a i r c r a f tindustry (Maury etal., 2002).

Fuller (2002a)has used smartfoam wi th af e e d b a c kc o n t r o l l e r t oreduce flow noisetransmission usingthe facility shownin Figure 27. The effect of active control is shown in Figure 28.

1 5

11 156

Accel

FeedbackCompensator

GPC

PZTActuator

u(k)

Tensioned Panel

Radiated Sound

Power, y(k)

32

54

1

FrequencyWeightedAverage

Fig. 29. Arrangement for single channel feedback controlof turbulent boundary layer interior noise radiation(Courtesy NASA Langley)

Table 1 Calculated attenuation (dB) in the total sound power radiated by the stiffened double-panel systemcalculated up to 3 kHz and up to 1 kHz for various control scenarios.

Recent work being undertaken at NASA Langley(Gibbs and Cabell, 2002) has demonstrated thatturbulent boundary layer noise generated by the airflow over the fuselage can be reduced using feedbackactive control. A schematic representation of their testrig is shown in Figure 29. They showed that usingindependent, single channel control of each of sixpanels, they could achieve a 15 dB reduction in interiornoise radiation at panel resonances and 7.5 dB forbroadband noise. Interactions between adjacent panelsdo reduce the performance slightly and this may beimproved slightly with a multi-channel controller.However, experiments on a single panel showed thatincreasing the controller complexity to three actuatorsand nine sensors (rather than one of each) and using amulti-channel controller increased the performance bybetween 1 and 2 dB. It is important to realise that whenundertaking analyses of aircraft interior noise and itscontrol, the fuselage is under tension due to the cabininternal pressure and this does have a significant effecton structural resonance frequencies which in turnaffects the control system noise reductionperformance.

Maury et al., (2002) undertook a detailed analysis of the potential for feedback active control to reducethe transmission of flow induced noise through a double panel into an aircraft cabin. The double panel wasmodelled as a stiffened external skin, backed by a cavity and a trim panel, typical of an aircraft structure.They examined two frequency ranges; 0 to 1000 Hz and 0 to 3000 Hz. In the lower frequency range,suppressing the first acoustic mode in the cavity resulted in 14 dB attenuation, whereas controlling the firsttrim panel mode resulted in 17 dB attenuation. The results they obtained are summarised in Table 1. Fromthis initial work, the control of flow-induced cabin noise in aircraft appears to be a promising applicationthat will be implemented in aircraft in the relatively short term.

Aircraft Jet Engine Noise. Considerable progress has been made in recent years on the active control ofaircraft jet engine noise. One possible method relies on placing radial rods upstream of the stator blades.The length and angular orientation of the rods can be actively controlled to produce an acoustic field thatcancels the field produced by interaction of the air flow with the stator vanes.

NASA (Silcox, 2002) have been working with BBN in the USA to actively excite stator blades in anattempt to reduce noise from a jet engine. The details of the controller arrangement and actuator placementare shown in Figures 30 and 31.

Fig. 32. Cross-section of a hybrid Nacelle liner for a jet engine (Hilbrunneret al., 2002)

Stator Vanes (28) 4 ft Fan (16 Blades)

ICD

Rotating Rake mode measurements

inlet & exit plane

Contro l Micropho nesfore & aft spoo l sections (1 6 each ro w, 9 6 t ot al)

PZT (THUNDER) Act uato rs 6 per vane, 4 Channels of Co ntrol

Fig. 30. NASA 48” ANC fan rig with BBN active vanes. (Courtesy Silcox, 2002).

Fig. 31. Detail of piezo-electric actuators on jet engine fan blades, (CourtesySilcox, 2002).

Hilbrunner, et al. (2002) have useda different approach, which involvesenhancing the absorptive characteristicsof the Nacelle (or wall of the engine)using an active element and a feedbackcontrol system as shown in the nacellecross-section in Figure 32. Thecancelling sound is generated by a bi-morph PZT actuator causing the plate towhich it is bonded to vibrate laterally asshown in the figure.

Car and Van Interior Noise. There area number of research groups currentlyworking in this area and the work hasbeen elegantly summarised by Sano etal. (2002). Active engine mounts (calledACM by Sano) have been developed andused to control engine harmonics beingstructurally transmitted and radiated asnoise in the cabin. Loudspeakers in thevehicle cabin (ANC) have been usedcommercially as early as 1991 to controlbooming noise generated by the engineand later to control road noise. Thehistory of commercial application issummarised briefly in Table 2.

1991

ANC forbooming noise

by Nissan

1997

ACM foridling NVby Toyota

1998

ACM fordiesel engine

by Nissan

2000

ANC forlow-frequency

road noiseby Honda

Table 2. Development of commercial ANC systems in motor vehicles (Courtesy Sano, et al., 2002)

The 1991 system developed by Nissan had 2 loudspeakers and 4 microphones. It obtained a referencesignal from the engine crank rotation pulse and used an adaptive feedforward architecture with a multipleerror FXLMS algorithm. The 1997 system developed by Toyota was a fixed (non-adaptive) feedforwardsystem using a hydraulic active engine mount and had the effect of reducing booming noise and idlingvibration by 5 to 10 dB.

The 1998 system developed by Nissan uses active engine mounts driven by electromagnetic actuatorsand operates over the range, 20 to 130 Hz. It attacks 2nd, 4th and 6th order engine components and uses a 16-bit microprocessor (not a DSP system) to implement the control algorithm.

The 2000 system developed by Honda for road noise control is about as low cost as it is currentlypossible to get. It consists of the use of the 4 standard car audio speakers as the control sources and usesa single microphone attached to the control circuitry which is located under the front driver's seat. A fixed(non-adaptive) feedback controller is used to reduce drumming noise at around 40 Hz which is heard in thefront seats. The feedback controller is single channel and sends the same signal to both front loudspeakers.The feedback controller drives the two front seat speakers. A single -channel fixed (non-adaptive)feedforward controller uses the microphone signal as a reference signal and drives both rear speakers withthe same signal. The purpose of the feedforward controller is to counteract the increase in rear seat noiselevels as a result of the feedback controller operation to reduce the front seat noise levels. A 10 dB reductionin drumming noise was achieved. Costs were further reduced by using analog circuits for the controllersand a limiter circuit was applied to the control output to prevent excessive levels.

Sano et al., (2002) state that the reason that ANC is not widely used in the automotive industry is thatit is not considered by management or consumers to be cost effective. That is, the perceived benefit of ANCdoes not equate to its cost. Thus, it is important to concentrate research effort in finding lower cost ways ofachieving the same results. An example of a low cost system is provided by Pfann, et al. (2002) who set upa demonstrator in a headrest in a science museum using a Pentium III, 700 MHz computer which interfacedto the amplifiers, speakers and microphones through a standard sound card. An adaptive normalised FXLMSalgorithm was implemented on the PC using the C++ programming language. The large group delay of 1/8second in the controller was OK for this application which used recorded noise, but it would not normallybe workable in a real system. Even for this ideal system the large group delay did have a significant effecton the convergence speed.

In other recent work on vehicles, Ramos, et al. (2002) have tested an ANC system inside a van, achievingattenuation between 15 and 20 dB for the main harmonics of the engine noise at error sensors placed in therear seats. Future work includes a more advanced controller for more channels that will allow the front seatsto be treated as well. However, no mention was made of the amount of control achieved at locationsremoved from the error sensors.

Another approach to interior noise in vehicles is to actively generate desirable noise so that to the driver,the vehicle sounds pleasing. For some drivers, this would be a vehicle as quiet as possible, while for otherdrivers, it would be desirable if their standard production car could be made to sound like a Porsche orFerrari, during acceleration, idle and cruising. Schirmacher et al. (2002) have developed such a system fora volkswagen, a schematic of which is shown in Figure 33. This system uses the car audio system as thesound sources and 6 microphones mounted in the roof liner as the error sensors. Active sound design differs

+

rpm

residual signal target signal

Engine rpm, load,additionalquantities

-

Fig. 33. Schematic of a vehicle active sound design system (Courtesy Schirmacheret al., 2002)

Fig. 35. Schematic of an oscillating flapper valve in atruck exhaust. (Courtesy Fohr et al., 2002)

Fig. 34. Prototype oscillating exhaust valve. (CourtesyFohr et al., 2002)

from active noise control in thatthe goal is not maximum soundreduction but the generation of atarget sound. Thus an activesound design system requires atarget sound generation systemin addition to the active noisecontrol subsystem. The targetsound generation subsystem usesengine load and rpm data as wellas rate of change of speed data togenerate the required targetsound at any given instant. Oneimportant aspect of the activesound design approach is that thesound of a car can be modifiedwith software only, making themanufacture of different modelsand custom models relativelylow cost.

Car Suspensions. Active and semi-active suspensions on automobiles have been the subject of research formany years and still continues with the aim of increasing the bandwidth of control and the performance(Lauwerys et al., 2002)

Truck Exhaust Noise. Some of the problemsassociated with the active control of truck exhaustnoise include the hostile environment for the actuators(usually loudspeakers) and the high levels of sound thatmust be generated by the actuators. A new type ofactuator is illustrated in Figure 35. It does not sufferfrom the same problems as an oscillating flapper valvethat is placed in the exhaust line and driven in such away as to inhibit transmission of sound past it (Fohr etal., 2002). In practice, the valve oscillates about thehorizontal position with a maximum amplitude of 12degrees.

Boonen and Sas (2002) also reported some resultsusing an exhaust valve to control noise transmitted downthe exhaust pipe. They found that inserting an active valvein the flow of a volume velocity source, like a combustionengine, has little effect on the attenuation of noise if nocapacitive elements such as volume chambers are presentbetween the source and the valve. The same conclusionwas reached by Fohr, et al. Also, without additionalcapacitive elements, a high back pressure will begenerated (Boonen and Sas, 2002). However, with theadditional upstream capacitative element, they showed ona cold engine simulator that a noise reduction of 13 dBL(4 dBA) was possible for a consumption of about 5 W ofelectrical power and a back pressure of 10 kPa.

Fig. 36. (1) Control loudspeaker, (2) headrest, (3) virtual microphoneposition and (4) error microphone. (Holmberg et al., 2002)

Headsets, Ear Muffs and Headrests. These devices have such wide application that there are continuingefforts being made to improve their performance. Traditional headsets have used feedback configurationsand analog electronics to minimise the time delays associated with signal processing. However, someproblems still remain such as “squealing” when subjected to very loud sound fields and acceptableperformance only over approximately two octaves. More recently, better performance has been demonstratedwith digital feed forward systems and work on improving practical implementation of the control algorithmsis continuing (Cartes et al., 2002).

Zimpfer-Jost (2002) explain how use of digital rather than analog feedback control allows the systemto be optimally configured for any particular noise environment by simply changing software. The choiceof an IIR (rather than FIR) filter in the feedback loop reduces processing time and subsequent delay throughthe controller which can lead to stability problems. However, to be able to use this type of filter effectively,quantisation errors arising from rounding of division products must be compensated for, particularly if fixedpoint (low cost) processors are used. Zimpfer-Jost (2002) developed a new algorithm called the “adapted”algorithm to compensate for this problem and achieved reasonably good results over 2 octaves. The newalgorithm involves adding a term to the expression used to calculate the filter output, which compensatesfor the systematic part of the error and leaves just the random part.

Pawelczyk (2002) applied a hybrid digital / analog feedback system in an attempt to improve theperformance of a headset. The analog part is directed at controlling random noise, while the digital part isdirected at controlling dominant tones in the spectrum. He obtained in reductions in excess of 10 dB over2 octaves for broadband noise and up to 60 dB for tonal noise.

Feedforward systems have been shown in the past to address stability and performance problemsrelated to insufficient excitation, nonstationary noise fields, and time-varying signal-to-noise ratio associatedwith using feedback control in headsets. Ray et al. (2002) showed using simulation that the performance ofthe feedforward systems could be further improved using a Lyapunov-tuned, normalised filtered-x LMSalgorithm with leakage, which is designed to accommodate both a non-constant forward path transferfunction and measurement noise on the reference input provides additional noise reduction performancegains.

The development of active noise control in headrests continues to attract considerable attention (Tsenget al., 2002; Holmberg et al., 2002). The mostpopular arrangement involves the use ofvirtual microphones, with the physicalsensors and actuators embedded in theheadrest and the points of sound fieldminimisation being located at theapproximate ear positions. Again feedbacksystems seem to dominate the work in thisarea as the direction of incident sound is notas critical as it is for feedforward controllerswhich require a reference sensor to belocated between the noise source and theerror sensor and at a significant distance fromthe error sensor if broadband noise is to becontrolled. A typical arrangement showingonly one side of the headrest and one ear isshown in Figure 36.

Duct Noise. The active control of sound propagating and exiting from ducts has been the subject of researchand application for decades. There are over 100 such installations in industrial facilities in the USA, butpractically all of them are directed at controlling plane waves. Above the cut-on frequency of the first highermode, plane wave control systems will become decreasingly effective as the frequency of the disturbanceincreases. Thus current research on active control of sound propagating in ducts is focussing on the controlof higher order modes. For large ducts, the cut on frequency of the first higher order mode can be quite low.For example, for the spray dryer exhaust stack (diameter, 1.6 m; temperature 90 �C), which is the subject

Fig. 37. Hybrid active/passive duct silencer (Kruger, 2002).

of the development of an active noise control system at the University of Adelaide, the first higher ordermode cuts on at 140 Hz and the noise problem is at 190 Hz, at which two higher order modes as well as theplane wave are propagating. In addition, the noise source, which is a 200 kW centrifugal fan, produced anunsteady tone that fluctuated wildly in level at any particular location in the duct. Theoretically, control ofthe tone at 190 Hz should be possible with three control sources and three error sensors, but in practice, sixcontrol sources and 12 error sensors were found to give the best results. Also a fast version of the Filtered-xLMS algorithm had to be developed to allow tracking of the rapidly varying level of the tone. This isdiscussed in more detail by Qiu and Hansen (2003).

The other area in which active control of duct noise has attracted recent research effort is in thedevelopment of relatively low cost hybrid active/passive systems, where the duct liner, which represents thepassive part is effective in the mid- to high-frequency range and the active part is effective in the lowfrequency range. The active part of these systems, which are now commercially available, consists of ananalog feedback controller and a single loudspeaker and error microphone. More recently, Kruger (2002)used multiple independent feedback control units in a lined duct over a 1 m length. The passive part providedan insertion loss of between 20 and 30 dB in the frequency range 400 to 1000 Hz and when the active partwas switched on the insertion loss increased to between 20 and 55 dB in the frequency range, 100 to 1000Hz. Kruger's system is illustrated in Figure 37, where the passive liner can be seen to be located on theopposite side of the duct to the active system as well as behind the speakers making up the active system.The liner behind the speakers is typically 50 mm thick while the liner on the opposite wall of the duct is 100mm. Thinner liners will result in reduced insertion loss performance. It can also be seen that eachloudspeaker is controlled by an independent feedback system with an independent error microphone.

Cable-Stayed Structures. Premont and Bossens (2002) have applied collocated sensing and actuation tothe active control of vibration in both large space structures and cable-stayed bridges (see Figure 38). Thedifference between the two applications is that for large space structures, piezo-electric actuation is used asit is lightweight and easy to implement, while for bridges, hydraulic actuation is used due to the larger forcesrequired.

top mass

intermediatemass

bottom mass

rotating machinesimulated withprimary shakers

accelerometers

control shakers

z y

x

Fig. 39. Two stage hybrid vibration isolation system.

Fig. 38. Example of active control of a cable-stayed bridge (Courtesy Premont and Bossens, 2002)

Active Vibration Isolation. The activeisolation of sensitive instrumentation fromsupport structure vibration such as the lensmount isolator described by Holterman, etal. (2002) continues to attract attentionfrom researchers. The complementaryproblem of active isolation of vibratingequipment from support structures is alsothe subject of continuing research. At TheUniversity of Adelaide (Li et al., 2002), theuse of active control to enhance theperformance of a two stage passivevibration mount, shown in Figure 39, tominimise the transmission of low-frequency tonal vibratory energy from diesel engines into a submarine hullis being investigated. The active system consists of seven actuators and seven sensors mounted on the 500kg intermediate mass of the two stage isolator. The actuators are being driven to control the amplitude ofthe six rigid-body modes of the intermediate mass. However, they found that best results in terms ofminimising the overall vibration level of the intermediate mass in all six degrees of freedom were obtainedusing kinetic energy control which involved minimising the sum of the squares of all error accleerometersignals. The results they obtained with real-time control in a laboratory environment are illustrated in

-90

-80

-70

-60

-50

-40

engine operating speed and harmonicsAc

cele

ratio

n le

vel (

dB re

: 1m

/s2 )

before control after kinetic energy control∆1 =31.1 dB; ∆1.5 =17.6 dB; ∆2 =5.2 dB; ∆2.5 =19.9 dB; ∆3 =6.5 dB

Fig. 40. Overall acceleration levels before and after kinetic control; the resonancefrequencies of control actuators were around 1st and 2.5th order engine speed.

Figure 40 where it can be seenthat substantial reductions invibration transmission arep o s s i b l e f o r t h e f i r s tfour engine harmonics. Higherorder harmonics were not targetedand the poor control at the 2nd

order was due to the shakers beingunable to generate sufficientoutput at this frequency.

CONCLUSIONS

Although there are many applications for which active noise control or active vibration control isimpractical, there are also many applications for which it is a highly cost effective and practical solution.Significant advances are continuing to be made in control theory and algorithms, DSP hardware, andacoustic modelling. Research also continues to push the application boundaries and some applications thatseemed impractical ten years ago are now beginning to look possible. So active noise control does indeedhave a very bright future and research will need to continue for some time to come to ensure that fulladvantage is taken of this exciting technology.

ACKNOWLEDGEMENTS

The author would like to acknowledge a number of researchers who provided copies of their Active 2002presentations and kindly gave permission to use their figures for this paper and the accompanyingpresentation at Wespac8. These people are Geoff Leventhall, Phil Nelson, Chris Fuller, Richard Silcox,Andrè Premont, Arthur Berkhoff, Marty Johnson, Hisashi Sano, Ben Cazzolato, Nick Burgan, Xiaojun Qiu,Simon Hill, Jan Krüger, Shunsuke Ishimitsu, Rolf Schirmacher and Ahmed Abu-Hanieh. I would also liketo thank other members of the University of Adelaide, School of Mechanical Engineering ANVC Group notmentioned above (particularly Xun Li) for permission to use their figures and results in this paper.

REFERENCES

Antila, M., Hakanen, K. and Kataja, J. 2002. Microcontroller-driven analogue filter for active noise control. Proceedings of Active2002, Southampton, UK, July 15-17, pp. 449–456.

Bao, C. and Pan, J. 1996. Use of adaptive filters in active ear defenders.Proceedings of Internoise ‘96, pp. 1175-1178.Berkhoff, A.P. 2002a. Design of actuator arrays for active structural acoustic control. Proceedings of Active 2002, Southampton,

UK, July 15-17, pp. 611–620.Berkhoff, A.P. 2002b. Broadband radiation modes: estimation and active control. J Acoust Soc Am, 111, 1295–1305.Boonen, R. and Sas, P. (2002). Design of an active exhaust attenuating valve for internal combustion engines. Proceedings of Active

2002, Southampton, UK, July 15-17, pp. 345–356.Burgan, N.C. Snyder, S.D., Tanaka, N. and Zander, A.C. 2002. Vibration sensing of orthogonal acoustic multipole radiation patterns

for active noise control. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 587–598.Cartes, D.A., Ray, L.R. and Collier, R.D. 2002. Experimental evaluation of leaky least mean square algorithms for active noise

reduction in communication headsets. Journal of the Acoustical Society of America, 111, 1758–1771.Cazzolato, B.S. and Hansen, C.H. 1999. Structural radiation mode sensing for active control of sound radiation into enclosed spaces.

Journal of the Acoustical Society of America, 106, 3732-3735.Cazzolato, B. 2002. An adaptive LMS virtual microphone. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 105–116.Cordioli, J.A., Hansen, C.H., Li, X.,and Qiu, X. 2002. Numerical evaluation of a decentralised feedforward active control system

for electrical transformer noise. International Journal of Acoustics and Vibration, 7, 100–104.

Elliott, S.J. 2002. Signal Processing for Active Control. Academic press.Fohr, F., Carme, C., Peube, J.-L., Vignassa, P. and Le Brazidec, F. 2002. Active exhaust line for truck diesel engine. Proceedings

of Active 2002, Southampton, UK, July 15-17, pp. 327–332.Fuller, C.R. Johnson, M.E. and Griffin, J.R. 2000. Active-passive control of aircraft interior boundary layer noise using smart foam.

AIAA Paper No. 2000-2041.Fuller, C.R. 2002a. Active-passive control of sound with smart skins. Presented at the 8th AIAA/CEAS Aeroacoustics Conference,

Breckenridge, Colorado, June 2002.Fuller, C.R. 2002b. Active control of sound radiation from structures: progress and future directions. Proceedings of Active 2002,

Southampton, UK, July 15-17, pp. 3–27.Garcia-Bonito, J., Elliott, S.J. and Boucher, C.C. 1996. A virtual microphone arrangement in a practical active headrest. In

Proceedings of Inter-noise 96, pp.1115–1120.Gardonio, P., Bianchi, E. and Elliott, S.J. 2002. Smart panel with multiple decentralised units for the control of sound transmission.

Part I: Theoretical predictions, Part II: Design of the decentralised control units, Part III: Control system implementation.Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 471–510.

Gentry, C.A. 1998. A study of smart foam for active noise control applications. PhD Thesis, Dept of Mechanical Engineering,Virginia Tech, Blacksburg, Virginia, USA.

Gibbs, G.P. and Cabell, R.H. 2002.Active Control of Turbulent Boundary Layer Induced Sound Radiation from Multiple AircraftPanels. Paper no. AIAA 2002-2496, 8th AIAA/CEAS Aeroacoustics Conference, Breckenridge, CO, June 16–18, 2002.

Goldstein, A. and Fuller, C.R. 2002. Control of sound transmission with passive-active tiles. Proceedings of Active 2002,Southampton, UK, July 15-17, pp. 547–558.

Guo, J., Pan, J. and Hodgson, M. (2002). Local or global control - the applicable active noise control strategy in enclosedenvironments. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 93–104.

Hansen, C.H. 2001. Understanding Active Noise Cancellation. Spon Press, London.Hilbrunner, O., Galland, M.-A., Sellen, N. and Perisse, J. 2002. Optimisation of an hybrid acoustic liner for noise reduction of engine

aircraft nacelles. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 657–668.Hill, S.G., Snyder, S.D. and Cazzolato, B.S. 2002. Real time acoustic based modal filtering approach to sensing system design.

Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 599–610.Hinchliffe, R., Scott, I., Purver, M. and Stothers, I. 2002. Tonal active control in production on a large turbo-prop aircraft.

Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 369-376.Holmberg, U., Ramnér, N. and Slovak, R. 2002. Low complexity robust control of a headrest system based on virtual microphones

and the internal model principle. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 1243–1250.Holterman, J., de Vries, T.J.A and Auer, F. 2002. Active damping based on decoupled collocated control. Proceedings of Active

2002, Southampton, UK, July 15-17, pp. 827–838.Horodinca, M., Abu-Hanieh, A. and Preumont, A. 2002. A soft six degrees of freedom active vibration isolator based on Stewart

platform. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 1021–1032.Ishimitsu, S. and Elliott, S.J. 2002. Improvement in the convergence property of adaptive feedforward controllers and their

application to the active control of ship interior noise. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 781–792.Johansson, S., Lago, T., Nordebo, S. and Claesson, I. 1999. Control approaches for active noise control of propeller-induced cabin

noise evaluated from data from a Dornier 328 aircraft. Proceedings of the sixth International Congress on Sound and Vibration,Copenhagen, Denmark, July, pp. 1611–1618.

Johansson, S., Sjosten, P., Nordebo, S. and Claesson, I. 2000. Comparison of multiple- and single-reference MIMO active noisecontrol approaches using data measured in a Dornier 328 aircraft. International Journal of Acoustics and Vibration, 5, 77–88.

Johansson, S., Håkansson, L., Persson, P. and Claesson, I. 2002. The multiple-input/single-output feedforward control of lateralvibration in a high-speed train car. Proceedings of the ninth International Congress on Sound and Vibration, Orlando, Florida,July 8–11, pp. 440–447.

Johnson, M.E. and Estève, S. 2002. Comparison of local and global adaptive strategies for the control of broadband noise in anenclosure using adaptive Helmholtz resonators. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 1111–1120.

Kestell, C.D., Cazzolato, B.S. and Hansen, C.H. 2000. Active noise control with virtual sensors in a long narrow duct. InternationalJournal of Acoustics and Vibration, 5, 63-76.

Kestell, C.D., Cazzolato, B.S. and Hansen, C.H. 2001. Active noise control in a free field with virtual sensors. Journal of theAcoustical Society of America, 109, 232-243.

Krüger, J.J. 2002. The calculation of actively absorbing silencers in rectangular ducts. Journal of Sound and Vibration. In Press.Lauwerys, C., Swevers, J. and Sas, P. 2002. Model based active suspension control for a passenger car. Proceedings of Active 2002,

Southampton, UK, July 15-17, pp. 955–966.Leclercq, D.D. 2002. Private communication.Lee, J-W., Li, X., Cazzolato, B. and Hansen, C.H. 2001. Active vibration control to reduce the low frequency vibration transmission

through an existing passive isolation system. Proceedings of the 8th International Congress on Sound and Vibration, Hong Kong,July 3-6, 2001, pp. 325-331.

Lee, Y.-S., Elliott, S.J. and Gardonio, P. 2002a. Distributed four-layer PVDF sensor and actuator arrangement for the control of beammotion. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 863–874.

Lee, Y.-S., Gardonio, P. and Elliott, S.J. 2002b. Coupling analysis of a matched piezoelectric sensor and actuator pair for vibrationcontrol of a smart beam. Journal of the Acoustical Society of America, 111, 2715–2726.

Liu, Y., Mace, B. and Waters, T. 2002. Semi-active dampers for shock and vibration isolation: algorithms and performance.Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 1121–1132.

Marcotte, P., Fuller, C.R. and Johnson, M.E. 2002. Numerical modeling of distributed active vibration absorbers (DAVA) for controlof noise radiated by a plate. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 535–546.

Maury, C., Gardonio, P. and Elliott, S.J. 2002. Model for the active control of the flow-induced noise transmitted through a stiffeneddouble-panel. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 575–586.

Meurers, T. and Veres, S.M. 2002. Implementation aspects for FSF-based feedback control with secondary path estimation.Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 1327–1338.

Monaco, E., Franco, F. and Lecce, L. 2002. Vibrations control using hybrid dynamic vibration absorber (HDVAs) based onmagnetostrictive active material. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 1133–1140.

Morgan, D.R. 1991. An adaptive modal-based active control system. J Acoust Soc Am, 89, 248–256.Munn, J.M., Cazzolato, B.S., Hansen, C.H. and Kestell, C.D. 2002. Higher-order virtual sensing for remote active noise control.

Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 377–386.Nelson, P.A. 2002. Active control for virtual acoustics. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 67–89.Pasco, Y. and Berry, A. 2002. Effects of non-linearity on an X-LMS active vibration isolation platform using piezoelectric stack

actuators. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 1097–1107.Pawelczyk, M. 2002. Analogue-digital control for active headsets. Proceedings of Active 2002, Southampton, UK, July 15-17, pp.

1219–1230.Pfann, E. Rice, G., Stirling, I. and Stewart, R.W. 2002. Development of an ANC demonstrator for public exhibition and use in a

science museum. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 439–448.Premont, A. and Bossens, F. 2002. Active control of cable structures. Presentation at Active 2002, Southampton, UK, July 15-17,

based on the printed paper, pp. 43–66.Qiu, X. and Hansen, C.H. 2001a. Modifications for practical implementation of the adaptive feedforward control algorithm.

Proceedings of Inter-Active 2001.Qiu, X. and Hansen, C.H. 2001b. A study of time domain FX-LMS algorithms with control output constraint. Journal of the

Acoustical Society of America, 109, 2815–2823.Qiu, X. and Hansen, C.H. 2002a. Applying effort constraints on adaptive feedforward control using the active set method. Journal

of Sound and Vibration. In PressQiu, X. and Hansen, C.H. 2002b. Perturbation signals for active noise and vibration control. Proceedings of the Ninth International

Congress on Sound and Vibration, Florida, July, pp. 111–118.Qiu, X. and Hansen, C.H. 2003. Practical Implementation of Adaptive Feedforward Control Algorithms for Multichannel Active

Noise Control. Proceedings of Wespac8.Qiu, X., Li, X., Leclercq, D.J.J., Zander, A.C., Kestell, C.D. and Hansen, C.H. 2002. Robust design of active noise control system

on a spray dryer exhaust. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 749–760.Ramos, P., Salinas, A., Lopez, A. and Masgrau, E. 2002. Practical implementation of a multiple-channel FxLMS Active Noise

Control system with shaping of the residual noise inside a van. Proceedings of Active 2002, Southampton, UK, July 15-17, pp.303–314.

Ray, L.R., Collier, R.D. and Kaliski, K.H. 2002. Optimization of stability and performance of LMS filters for feedforward activenoise reduction in communication headsets. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 705–716.

Sano, H., Yamashita, T., Nakamura, M. 2002. Recent application of active noise and vibration control to automobiles. Proceedingsof Active 2002, Southampton, UK, July 15-17, pp. 29–42.

Sapinski, B. and Pilat, A. 2002. Inverse dynamic applications for MR seat damper vibration control. Proceedings of Active 2002,Southampton, UK, July 15-17, pp. 669–680.

Silcox, R. J. 2002. Concepts and applications of active noise & vibration control in aerospace. Presented at Active 2002,Southampton, UK, July 15-17.

Sireteanu, T., Stancioiu, D. and Stammers, C.W. 2002. Use of magnetorheological fluid dampers in semi-active driver seat vibrationcontrol. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 1055–1062.

Stein, G.J. 2002. Some results of research of an active electro-pneumatic suspension system. Proceedings of Active 2002,Southampton, UK, July 15-17, pp. 1085–1096.

Svensson, W. and Holmberg, U. 2002. Identification of an electro-pneumatic loudspeaker using Hammerstein models. Proceedingsof Active 2002, Southampton, UK, July 15-17, pp. 457–466.

Symes, M.K., Leclercq, D.J.J. and Hansen, C.H. 2002. Sensing systems for active control of aerodynamic noise. Proceedings ofActive 2002, Southampton, UK, July 15-17, pp. 135–146.

Tseng, W.-K., Rafaely, B. and Elliott, S.J. 2002. Performance limits and real-time implementation of a virtual microphone activeheadrest. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 1231–1242.

Valentini, F. and Scamoni, F. 2002. ANC applied to building elements: real scale realisation. Proceedings of Active 2002,Southampton, UK, July 15-17, pp. 123–134.

Variyart, W., Brennan, M.J. and Elliott, S.J. 2002. Active damping for a beam using feedback control. Proceedings of Active 2002,Southampton, UK, July 15-17, pp. 839–850.

Veena, S. and Narasimhan, S.V. 2002. A Laguerre escalator lattice predictor for secondary path identification of feedforward/feedback active noise control. Proceedings of Active 2002, Southampton, UK, July 15-17, pp. 813–824.

Zimpfer-Jost, V. 2002. Digital Feedback Control for Active Hearing Protection. Proceedings of Active 2002, Southampton, UK,July 15-17, pp. 1211–1218.