DOES ACTIVE NOISE CONTROL HAVE A FUTURE? Manuscript Number: 1226U Colin H HANSEN Dept Mechanical Engineering, University of Adelaide, SA 5005, AUSTRALIA
[email protected] ABSTRACT Research activities in active noise control (ANC) continue to expand with new developments being reported in scientific journals on a regular basis. Although there are a number of commercial applications that have been implemented successfully, the widespread application of ANC technology remains elusive and one is left wondering whether ANC is ever going to be a truly practical noise control tool. In this paper, recent developments in ANC technology are reviewed and the technical problems still standing in the way of widespread application are discussed. Progress made in solving some of the problems is summarized, current international research directions are outlined and the future of the discipline is examined. In addition, some of 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 a specific part of an active system such as signal processing or actuator design. Very little success has been reported on the development of complete generic systems that can be used for a range of applications. Each new application requires a significant research and development effort to achieve any success. An application may be defined as "new" even if it is a similar physical system but with a different type of noise problem. End users of any technology would like to be able to use it without having much understanding of the physics behind its operation. With passive noise control, in many cases, there is a simplicity that makes it attractive. Even though it may require someone with expertise in acoustics and vibration to design passive noise control devices, it does not require anyone with this sort of expertise to install a muffler in a duct or engine exhaust, a noise barrier along a highway, an enclosure around a machine or a set of vibration isolators under a machine. The item responsible for controlling the noise is visually apparent and the physical mechanism is easily understood by anyone with an engineering background or technical education. On the other hand, expertise in signal processing, electronics, acoustics and vibration is necessary to ensure a successful installation of an active system and this makes it expensive and suitable only for special applications where passive control is 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 "10 or 20 years or even longer before we see active noise control used routinely in consumer products, vehicles and 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 will be in common use is getting closer. Continuing research on all aspects of the general technology as well as on 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 helping to make active control technology more practical and affordable. What is needed to generate a significant increase in the use of active control technology is the availability of an inexpensive, clever, commercial control system which includes a selection of source and sensor transducers to satisfy most problems as well as software to guide users in the correct choice and location of such transducers. The word “clever” used above to describe the commercial control system which does not yet exist needs some explanation. If a controller is to be useful to a wide range of people, the effort involved in setting it up must be very small. This means that the controller must itself be controlled by a high level expert system or neural network which automatically sets input and output gains to maximise system dynamic range, convergence coefficients to optimise convergence speed and stability trade-offs, control filter type and weight numbers to optimise noise reduction, as well as leakage coefficients and system ID algorithms, filter types and configurations to maximise controller performance and stability. In addition, the controller should also be able to perform as an adaptive feedforward or adaptive feedback controller, be extendable to a large number of channels simply by adding together identical modules and provide advice on the suitability of feedforward control compared to feedback control based on the quality of the available reference signal. Finally the controller/user interface during set-up (which should really be a question/answer session) should be Microsoft Windows based for maximum flexibility. The ability to connect a modem to the controller to allow remote access and interrogation of current performance and the state of transducers and other system components is also an important labour saving device. Also, to make the controller more universally useful, it should be capable of being programmed with new algorithms and filters by anyone with a knowledge of the "C" programming language. Software must also be developed which is user friendly and allows the user to determine optimum control source and error sensor types, configurations and locations based on measured transfer functions between potential control source and error sensor locations as well as a measure of the primary field strength at these locations. If the field of active noise control is to continue to grow, and if industry is to lose its suspicion of this technology, there needs to be considerably more effort devoted to the development of user friendly commercial 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 less unfounded 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 of providing context to the discussion on recent advances and continuing research that is to follow. The discussion will focus on work presented at "Active 2002" conference in Southampton in July, 2002 as well as 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 digital and also frequency domain and time domain controllers. Then there are hybrid systems that incorporate both a feedback and feedforward part or an analogue and a digital part. Other types of hybrid system incorporate passive as well as active control (for example, active/passive mufflers). Each type of system has its advantages 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 the time-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 this application. The simplest example to consider for the illustration of the principles of feedforward and feedback 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) consists of a reference sensor, a control source, an error sensor, a control algorithm and an electronic controller, as illustrated in Figure 1(a). The reference sensor samples the incoming signal which is transmitted to the controller and processed to produce the desired cancelling signal to drive the control actuator (loudspeaker
in this case). The controller usually consists of an adaptive digital filter, and an adaptive algorithm that sets the weights in the adaptive digital filter. The error sensor senses the remainder of the signal after control and provides an input to the control algorithm that allows the control filter weights to be updated to minimise this signal. The signal processing time of the Reference controller must be less than the time for the Error microphone microphone acoustic signal to propagate from the reference sensor to the control source for Reference Error broadband noise control, but for tonal noise Signal Control Signal Signal control, there is no maximum permitted Electronic processing time as the signal is repetitive. Controller The cancellation path is the electroacoustic path from the loudspeaker input to the error microphone output. The transfer function of this path must be taken into Error microphone account in most controller algorithms and thus it must be measured for every installed Error system. Indeed, it is essential in most Control Signal Signal practical commercial systems that some means is implemented in the controller to Electronic Controller measure this transfer function regularly while the controller is operating as it can change quite quickly in some cases. Fig. 1. Basic active noise control systems. Because of their inherent stability, (a) Feedforward system (b) Feedback system feedforward controllers are generally preferred over feedback controllers when a reference signal which is correlated with the error signal is available. One exception is the active ear muff case for which it has been found that an adaptive feedback controller seems to cope better than an adaptive feedforward controller to head movement of the wearer [1]. One disadvantage of feedforward controllers which is not shared by feedback controllers is the often encountered problem of feedback of the control source output to the reference sensor via an acoustic path. Unless this is compensated for in the controller adaptation algorithm and control filter, instability is likely to result. Appropriate algorithms and filters are Error microphone discussed in the next section. One Tachometer Signal way of avoiding the problem is to use Error a non-acoustic reference sensor such Control Signal Reference Signal as a tachometer on the machine Conditioning Signal Electronic electronics Controller causing the noise, as shown in Figure 2. The electronic controller part in Fig. 2. Basic active noise control system with tachometer reference signal. Figure 1(a) is the only component that is different for a time domain compared to a frequency domain system. The frequency domain system is only suitable for tonal (single or multi) sound and involves converting the time domain signal to the frequency domain using narrow band digital filters or an FFT. The required cancelling signals for each of the various frequency components are then combined and converted to a time domain signal prior to being output through the control transducer. The advantage of the frequency domain controller is the ability to treat each tonal 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 reference signal is passed through a digital filter to produce the control signal for the loudspeaker which is passed through a digital to analog converter and smoothing filter prior to being fed into the loudspeaker amplifier.
The digital filter weights are adjusted by an algorithm that uses the reference and error signals as inputs. The algorithm also needs to take into account the impulse response of the path between the control source output from the controller and the error microphone input to the controller. Sampled Input x(k-1) x(k) x(k-(N-1)) Estimation of the characteristics of this z -1 z -1 z -1 path is known as "cancellation path modelling" and this is one area of w w w w continuing research. The simplest type of digital filter used in ANC systems is + + + y(k) Output + the finite impulse response (FIR) filter + + S S S illustrated in Figure 3. In the figure, z-1 represents a delay of one (input) sample, x(k) is the input signal at time Fig. 3. FIR filter architecture. sample, k and wi represents filter weight i. The structure in Figure 3 is sometimes referred to as a transversal filter, or a tapped delay line. The number of "stages" in the filter (the number of present and past input samples used in the output derivation) is usually referred to as the number of filter "taps". The filter weights are updated at each time sample using the following equation: 0
1
L-1
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w(k%1) ' w(k) [1 & µ α ]&2 µe(k) [c(k)( x(k)]
(1)
where w(k) is the vector of filter weights at time, k, e(k) is the error signal at time, k, c(k) represents the coefficients of the FIR filter used to model the cancellation path (see Hansen, 2001), α is a leakage coefficient 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 parameter were set equal to zero, it would be called the filtered-x LMS algorithm. In many cases it is desirable to normalise µ with the reference signal as follows: β µ ' (2) T x (k) x(k) where β is a constant between 0 Sampled and 2. When this is implemented, Input Output Feedforward part the algorithm is called the S b +b z +b z +... + + normalised filtered-x LMS algorithm. Feedback part Another filter type that is a z +a z +... commonly used where there is a possibility that the reference signal may be contaminated by the control source sound is an IIR Sampled filter, illustrated in Figure 4, which Input x(k) x(k-1) x(k-(N-1)) z z z has a structure that attempts to bN-1 b b b compensate for the feedback from + + + the control source to the reference Output + + + S S S sensor. y(k-M) y(k-2) y(k-1) z z z A feedback system is y(k) illustrated in Figure 1(b). For an a a a adaptive system, the electronic + + + S S controller is an adaptive filter and algorithm, whereas for a nonadaptive system, the electronic Fig. 4. IIR filter architecture. controller consists of a fixed low pass 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. -1
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Referring to the figure, it can be seen that the error microphone picks up the incoming signal which is processed 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 signal is good. Thus resonances in structural and acoustical systems can be controlled, and noise which has a high normalised autocorrelation for any time delay greater than the delay through the control system (which includes the control source and error sensor) is also controllable. Tonal noise is characterised by a high autocorrelation and thus is well suited to feedback control. Adaptive feedback controllers are usually implemented digitally using a DSP chip, as implementation using analog circuitry is not really practical. On the other hand, non-adaptive feedback controllers are usually implemented in analog circuitry to minimise the delay through the controller, thus increasing the maximum autocorrelation value of the noise to be controlled and hence the maximum achievable performance. A disadvantage of feedback controllers is their inherent instability at higher frequencies where the phase response is not easily controlled. This can cause serious acoustic noise problems in the presence of high frequency noise or if the physical system being controlled changes too much from the design condition (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 is adjusted on the head of the wearer or if the wearer enters an environment characterised mainly by impulsive noise or high frequency noise. The instability problems of these types of controller are usually minimised by keeping the controller gain within reasonable bounds, (which has the effect of limiting the controller noise reduction performance) and using low pass filters to attenuate incoming high frequency signals which cannot be controlled. To maximise robustness (or minimise instability problems) it is essential that the microphone be placed as close as possible to the control source which will have the effect of minimising system delays and thus maximising the autocorrelation of the noise at time delays greater than the control system time delay. The disadvantage of placing the error sensor close to the control source is that because of near field effects, the sound pressure at large distances from the error microphone may not be significantly reduced. This is not a problem, of course for active ear muffs because of the close proximity of the ear drum to 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 (including virtual 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 active control field is the large development time needed to keep up with advances in processing speed and memory capability of the electronic hardware used to implement the control systems. Modern DSP chips have such a high pin density that printed circuit board manufacture requires special expensive tooling which is only economical if boards are manufactured in large quantities. There are three approaches that may be used. The first approach is to use an embedded system that is based on a Pentium 4 processor and a Windows 2000 interface. The advantage of a system such as this is that once it is programmed, the hardware can be upgraded 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 mean that such a system is relatively slow and lacks the capability of the second and third approaches described below. Second, a special purpose ANC system could be purchased from one of the few manufacturers of such systems. 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 ANC systems is that they are expensive, because very few are sold, resulting in a high unit cost for development and production. It is expected that the next generation of special purpose ANC systems will be sufficiently powerful to allow very large numbers of filter taps and channels as well as a high enough processing speed to implement feedback as well as feedforward control, while allowing users to implement special algorithms
using the "C" programming language. Less flexible systems are currently available that allow users to program 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 separate input/output boards housed in a single enclosure. Users will also need band pass filters for the input to improve the performance of the controller in the frequency range of interest as well as signal conditioning electronics for reference signals generated by tachometers. Of course power amplifiers are always needed to drive the loudspeaker or vibration control sources. Such general purpose controllers are much too expensive 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 time a more powerful processor is released, the development process has to be repeated in both hardware and software. The third approach is to use general purpose DSP boards that are built into a stand-alone unit or plug-in to a PC. The main disadvantages are that the boards are relatively expensive, they include a lot of hardware not needed by ANC systems, they often have specifications that cannot be relied upon and require backplane circuits 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 be programmed from scratch using the "C" programming language and suffer from the problems of the second approach 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, a considerable proportion of the software development is for general house-keeping rather than being directly related to active noise control. Such requirements include safeguards to prevent excessive noise in the event that the controller becomes unstable, regular measurement of the cancellation path transfer function, detection and automatic indication of failed actuators and sensors, detection of overloads on inputs and outputs and automatic resetting of the controller and/or disconnection of troublesome inputs or outputs. Actuators. In recent times, the availability of new materials has opened up the scope for novel actuator systems suited specifically to active control applications. Loudspeaker cones made from polypropylene, carbon fibre and aluminium are available for hostile environments and low frequency applications. Lower cost alternatives have involved the use of paper cone loudspeakers sprayed first with engine gasket material and then with metallic paint (Leclercq, 2002). For some applications, special loudspeakers have been specifically developed. An example is the flat speaker developed by Ultra Electronics to fit behind aircraft trim panels (see Figure 5). Inertial actuators are becoming more popular in active Fig. 5. Thin loudspeaker in aircraft trim panel. control systems involving the reduction of structural (Courtesy Ultra Electronics) vibration due to their convenience in application, ease of tuning so that their resonance frequency closely matches the frequency of the tone to be controlled and in some cases their low cost. A low cost inertial actuator that has been used for active vibration isolation (Lee, et al., 2001) is shown in Figure 6. Another inertial actuator recently developed by Ultra Electronics for their aircraft active noise control system is shown in Figure 7. This actuator is applied to the frame/stringer interfaces in the aircraft fuselage and reduces the frame and fuselage vibration in such a way as to reduce the noise radiated to the aircraft interior. To reduce the sharpness of the resonance peak of the actuator, a velocity feedback Fig. 6. Low cost inertial actuator
Sound power level (dB re 10-12 W)
system was implemented on the actuator, which had the effect of increasing the actuator damping. Other inertial actuators have been used to reduce gearbox noise radiated from patrol boats and engine noise in luxury yachts. However, none of these experimental systems have yet been implemented permanently on any vessel. Piezo-electric crystal actuators (both stack and thin plate types) and PVDF sensors are now widely available at a relatively low cost, making the construction of "smart" structures technically feasible and also economically feasible for some aerospace applications. PVDF is a flexible piezoelectric film that is low in cost and low in its force Fig. 7. Inertial actuator used by Ultra Electronics for turboproducing capability; thus, it is usually only used as prop aircraft active noise control. (Courtesy Ultra Electronics) a sensor and not as an actuator. The one well known exception Radiated Sound Acoustic Foam is the use of PVDF as an actuator embedded in smart foam (Gentry, 1998; Fuller et Piezoelectric Film Smart al., 2000, Marcotte, et al., Foam Cell 2002) and used to modify the Electrical radiation impedance of a Leads vibrating surface. Such an actuator is illustrated in Figure 8. The radiation efficiency of the actuator may be increased Acoustic Foam Radiating Structure by placing it in a balsa wood box. The sound power output Fig. 8. Configuration for a smart foam actuator (Courtesy Fuller, 2002a) capability of two actuator designs is 140 shown in Figure 9. Small Actuator (NASA expts.) A distributed Weight = 36.1 g 130 R 1-3/4“ version of the smart foam actuator is 2“ 5“ shown in Figure 120 4“ 10. Another type of actuator is a Large Actuator (VPI expts.) 110 Weight = 52.5 g piezo-electric R 2“ element used as the 100 active part of an 3“ 4-1/2“ active/passive tile for reducing launch 3/4“ 4-3/4“ 90 noise transmission 0 250 500 750 1000 1250 1500 1750 2000 into launch Frequency (Hz) vehicles and preventing damage Fig. 9. Sound power output of smart foam actuators. (Upper curve represents the large actuator) to sensitive satellite payloads. This is illustrated in Figure 11. Piezo-electric actuators suffer from non-linearities that affect the performance of active vibration attenuation systems. Pasco and Berry (2002) developed a non-linear model to describe the behaviour of a piezo-electric stack actuator. They conducted active isolation experiments on a single-axis isolator for
single-frequency perturbations using a piezoceramic stack as control actuator and a feedforward controller. They found that the best control results were obtained using a synthesised single-frequency reference passing through the non-linear model of the stack in order to generate harmonic components in the control input. In this case, the isolator proved to be effective both at the fundamental and higher harmonics of the disturbance. They implied
Active elastic layer
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Fig. 10. Configuration for a smart foam actuator (Courtesy Fuller et al., 2000)
Fig. 11. Piezo-electric actuator configured to act as an acoustic tile. (Courtesy Goldstein and Fuller, 2002)
that they have also had success using the same approach on a MIMO 6-axis isolation platform based on a cubic Stewart platform. Magneto-rheological fluids are currently a popular choice of actuator in semi-active dampers and have proved to be much more effective than electrorheological fluids (Sapinski and Pilat, 2002; Sireteanu, et al., 2002). When a magnetic field is applied to such a fluid, magnetic particles within it become aligned and the fluid becomes more viscous. These fluids are ideal in damper applications where a variable damping force is needed to continuously minimise the vibration transmission through the damper. For vibration isolation applications, a special purpose 6-degree-of-freedom active element based on piezoelectric Fig. 12. Vibration isolation or structural interface element stack actuators terminated with flexible (Courtesy, A. Premont, 2002) tips is now commercially available (Horodinca, et al., 2002; Premont, 2002). This actuator is illustrated in Figure 12.
For applications requiring a softer mount, a system that uses voice coil actuators (Ahmed, 2002) as illustrated in Figure13, may be used. In these systems, the design of the flexible joints that connect the actuators to the frames is very important. Ahmed (2002) used a membrane element in the connection to provide the required flexibility and this is illustrated in Figure 14.
Fig. 13. Soft, 6 DOF active isolation mount using voice coil actuators (Courtesy Ahmed, 2002)
Fig. 14. Flexible joint to connect the actuators to the frames (Courtesy Ahmed, 2002)
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. Temperaturecompensated elements are the most suitable and these are available for tens of dollars. The expense comes in all the hardware that must surround the electret element to enable it to survive a typical ANC environment. An illustration of a typical construction is shown in Figure 15.
Fig. 15. Microphone assembly used by Ultra Electronics in their aircraft ANC system (Courtesy Ultra Electronics)
Other sensors include PVDF piezoelectric film for vibration sensing and industrial accelerometers, also for vibration 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 by controlling structural radiation modes rather than structural vibration modes. Structural radiation modes are made up of various proportions of a range of structural modes and represent structural vibration patterns that are orthogonal in terms of their contribution to sound radiation, as opposed to structural vibration modes that
are orthogonal in terms of their contribution to structural vibration levels. Radiation modes are derived from the 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 strongly dependent on frequency. However, as the cost function to be reduced is the level of sound radiation and not the amplitudes of the radiation modes, the effect of the radiation efficiencies of the radiation modes is taken into 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 or piezo-electric patch actuators. Large patch actuators can act as spatial filters and reduce spillover, which is the introduction of unwanted energy into higher order structural modes as a result of active control. When multiple actuators are used, their positions can be optimized in such a way that they only excite the structural modes that can be measured by the error sensors, thus minimising the spillover effect. However, the optimum actuator locations, when a small number are used, are quite sensitive to changes in the system being controlled. Thus Berkhoff (2002a) has suggested the idea of using fixed actuator arrays, containing many actuators, where tuning for optimum control in different environments or application areas is achieved through modification of the controller coefficients. Berkhoff describes a tuning method for the case where the number of actuators exceeds the number of error outputs. It is based on the use of modal error signals with frequency dependent weightings, which are optimized for acoustic radiation (radiation modes) plus the use of novel actuator constraint conditions. Traditional actuator constraints involve including the actuator efforts in the cost function or including the control filter weights. Berkhoff's novel method is to include in the cost function, the actuator contribution to radiation modes that are efficient in the frequency range outside of the range to be controlled and inefficient in the range to be controlled. Note that these modes are orthogonal (in terms of sound radiation) to each other and also to the radiation modes that are being controlled. Berkhoff's general cost function, which includes all possible actuator constraints may be written as (Berkhoff, 2002a): 4 T
J ' E{e (t) e(t) } % β1tr.
m
W T(t) W(t) dt % β2 E u T(t) u(t) % β3 E s T(t) s )(t)
(3)
0
The first term in the equation corresponds to minimisation of the error signals, which in this case are radiation mode amplitudes. The second term corresponds to minimising the control filter weights with a weighting coefficient of β1 , the third term corresponds to minimising the controller output with a weighting of β2 and the fourth term corresponds to minimising the cancellation path signals that are not measured by the error sensors. This last term corresponds to minimising the controller contribution to radiation modes not sensed by the error sensors. Using his method, Berkhoff demonstrated a feedback controller design for a honeycomb sandwich panel of dimensions 0.6 m × 0.75 m with 16 piezoelectric patch actuators and 16 accelerometers. The panel was excited with a sound pressure signal that was recorded in-flight in a jet aircraft. He obtained a large reduction (9.5 dB overall) in broadband radiated sound power (up to 450 Hz) with no significant evidence of spillover outside the controller bandwidth and a huge performance improvement over the use of actuator effort control by itself. Best results were obtained by setting β1 = 10-4, β2 = 0 and β3 =30. However, even better results (11 dB overall reduction) were obtained by configuring the actuators to drive only the modes contributing 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 the physical sensors and using an active control system to minimise the sound field at the remote location. There are two ways of estimating the remote sound field. The first involves measuring the transfer function between a microphone placed temporarily at the remote location and the physical error microphone (GarciaBonito, et al., 1996, Holmberg et al., 2002). The second involves estimating the sound field at the remote location using a linear extrapolation or higher order extrapolation based on the signals from the physical sensors (Kestell, et al., 2000) as shown in Figure 16. The extrapolation was implemented in practice by weighting the outputs of the physical microphones by different amounts and then adding the weighted outputs together to provide an estimate of the sound pressure level at the remote location. The weights were determined using simple linear or quadratic
extrapolation formulae Linear Extrapolation Quadratic Extrapolation (Kestell, et al., 2001), shown in the figure. Previous work found that the accuracy of the extrapolation in an enclosure was affected by short wavelength spatial noise due to the forced P - P1 response of resonant Pv = 2 x + P2 Pv ' x (x % h) P1 % x (x % 2h) P2 % (x % h)(x % 2h) P3 2h enclosure modes at the h2 2h 2 2h 2 test frequencies. This also meant that in practice, the Fig. 16. Illustration of linear and quadratic extrapolation from the physical microphones, P1, P2 and P3 to the virtual location, Pv linear prediction method gave better results than the quadratic prediction. In an attempt to obtain better results, more physical microphones were used and a least squares fit was used to estimate the sound pressure level at the remote location (Munn, et al., 2002). However, this amplified errors caused by inaccuracies in the physical microphone locations as well as errors caused by phase and amplitude 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 remote location and then using an adaptive LMS algorithm to determine the weights to be applied to the physical error microphones that would result in the sum of 6 Desired Pressure the weighted signals from Desired the error microphones being Location equal to the signal from the 1 W1 temporary microphone as Mic1 shown in Figure 17. He used Weight1 simulations in a 1-D sound 2 W2 field to show that the
adaptive system can completely compensate for amplitude mismatches and spatial errors, and may help in compensating for phase mismatches between the microphone elements. Experimental verification of the simulation results is currently in progress. In addition, the work is currently being extended to 3-Dimensional fields and to virtual energy density sensing.
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Fig. 17. Adaptive LMS microphone (Courtesy Cazzolato, 2002)
Decentralised Control. One of the problems associated with the control of complex sound fields or complex structural vibration using traditional active control approaches is that a large number of control channels are needed to handle the large numbers of sensors and actuators that are required. A controller with a large number of channels needs a large amount of computing power, can be slow to converge and may suffer stability problems. There are four main approaches that are currently being researched to reduce the number of dependent controller channels, while at the same time maintaining the same number of sensors and control actuators.
The simplest approach is to partition the control system into a number of independent controllers, each of which is concerned only with the control sources and error sensors from part of the total system. The partitioning may be based on the physical locations of the control sources and error sensors as done by Cordioli et al. (2002). They investigated the effect of partitioning a controller for a large electrical transformer into four independent controllers, one for each side. They also examined the case of two independent systems for each side, a total of eight independent systems. The analysis used quadratic optimisation and measured transfer functions between control sources and error sensors. They found that the controller performance decreased as the number of independent control systems increased. The partitioning could also be done on the basis of selecting actuator/sensor pairs where the sensor signal was dominated by the particular actuator output. Alternatively a genetic algorithm could be used to determine which 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 smaller number of control system inputs and outputs. This is done by describing the structural radiation as being due to a combination of simple sources; that is, a monopole, a dipole, a quadrupole, etc. The various multipoles may be realised using a uniform distribution of simple monopoles over the radiating surface and phasing them appropriately to produce the required multipole. For example, if all of the sources were in phase, then they would represent the global monopole. If a group consisting of half of the monopole sources all located on one half of the structure were 180 degrees out of phase with the remaining monopoles on the other 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 of each of these multipole source types (up to order 8) to the radiated sound field. He found that, similar to the behaviour of structural vibration modes, the multipoles are not orthogonal in terms of their contribution to the radiated sound field. This means that an orthonormal transformation is required to produce a set of modes that are orthogonal in terms of sound radiation. The difference between the resulting set of orthogonal radiation functions and the radiation modes (see previous section) derived using an orthonormal transformation of the structural vibration modes is that for the multi-pole case, no a priori knowledge of the structural vibration mode shapes is required. Burgan et al. (2002) random demonstrated their method using a noise intensity measurements lowpass filter flat panel in an infinite baffle driven to estimate radiated power at 325 Hz by a single point shaker input as the disturbance input primary source and controlled by a control input second single point shaker input. IIR control plate filter Eight monopole sources were used to derive global multipoles and error reductions of broadband noise were adaptive signals Q Y Z 8 accelerometer signals algorithm demonstrated. In practice, the modal 1-3 multipole modal filtering required to provide the filtering signals sensing system orthonormal transformed multipole amplitudes effectively involved Fig. 18. Burgan et al's multipole sensing system combining the eight accelerometer signals together with different weightings for each order of multipole. Burgan's system is shown in Figure 18 and the results he obtained with a single control source are shown in Figure 19. Hill, et al. (2002) used a similar Fig. 19. Results obtained using Burgan et al's multipole sensing system approach, except that T
-1
32 sensors evenly distributed at the sensing system used to derive the each of f = 10, 20, 30, 40 degrees multipole amplitudes on the radiating panel consisted of an array of microphones mounted above the panel. His system is shown in Figure 20. 128 sensor signals 10 meter radius A second approach for reducing the -1 Modal filtering to resolve Yn number of dependent controller channels is multipole amplitudes described by Gardonio, et al. (2002) who 1-8 error signals (multipole amplitudes) used a large number of independent single minimize the channel feedback controllers to control weighted signals Planar structure sound transmission through a panel as infinite baffle illustrated in Figure 21. He used piezoelectric crystal actuators (PZT) as control sources and an accelerometer mounted on Fig. 20. Acoustic multipole sensing system of Hill et al. (2002) top of each actuator to provide the error signal for each controller. When feedback controllers are used, it is important that the sensor and actuator are collocated to minimise instability problems (Preumont and Bossens, 2002). Recent work (Variyart, et al., 2002) has discussed the optimisation of Fig. 21. Multiple single-channel independent feedback controllers to improve feedback gain and structural kinetic the low-frequency transmission loss of a panel (from Gardonio et al., 2002) energy control. Lee, et al. (2002a) investigated five different arrangements as shown in Figure 22 for collocation of sensors and actuators and showed that most configurations are characterised by the problem of coupling effects between the sensor and actuator that are additional to the coupling due to the bending of the structure. The worst configuration was type 5 and the best was type 1. Lee et al. (2002b) also showed that for a real structure, the in-plane coupling between the sensor and actuator, severely limits the performance of collocated configurations and must be included in any analysis in addition to out-of-plane coupling.
Fig. 22. Possible configurations for collocated piezo-electric sensors and actuators for feedback control (from Lee, et al., 2002)
An interesting recent application of collocated control is illustrated in Figure 23, where two collocated position actuators (piezo-electric stacks) and force sensing are used to supplement passive vibration isolation for 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 singlechannel feedback controllers described above, yet at the same time can optimise the various feedback gains to satisfy a global rather than local cost function is described by Fuller (2002a) as a hierarchial controller.
This controller consists of local, independent singlechannel feedback controllers acting on collocated sensor and actuator pairs. These multiple independent controllers all have programmable feedback gains which are provided by the local controller Fig. 23. Collocated actuators and sensors used for vibration isolation of a lens mount. shown in Figure 24. The (From Holterman, et al., 2002). The black arrows indicate the direction of actuation and top level controller sensing. provides the input to the local controllers so that the independent controller gains are adjusted to minimise a global cost function such as minimisation of radiation mode amplitudes. Enclosure Noise Control. The control of noise in enclosures such as machinery rooms, truck cabins and aircraft cabins is now fairly well understood and the feasibility of control can be determined from the physical situation. The questions that must be answered before the feasibility of active noise control can be estimated are the following: • Are the enclosure walls rigid or flexible? Fig. 24. Schematic of hierarchical control arrangement. (Courtesy Fuller, 2002b) • 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 sound control, local control or no significant control. Guo et al. (2002) discussed the boundaries between the achievement of local or global control. Valentini and Scamoni (2002) reported some success with both transparent and opaque active windows and are currently developing a commercial version that will be capable of reducing periodic noise transmission (such as transformer hum) through windows and openings in buildings. When actively controlling acoustic fields, the most common quantity to minimise is the acoustic pressure at 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 the expense of increases elsewhere in the enclosure. This is undesirable as the large pressure gradient in the vicinity of the sensor is subjectively unacceptable. For this reason, energy density, ψtot , as defined in Equation (2) as the sum of the kinetic and potential energies is emerging as the preferred quantity to minimise. ψtot(t) ' ψk(t) % ψp(t) '
ρ 2 p 2(t) 2 2 ux (t) % u y (t) % uz (t) % 2 (ρ c)2
(4)
This effectively requires the minimisation of particle velocities (or pressure gradient) in three orthogonal directions as well as the acoustic pressure. The result is a much lower pressure gradient in the vicinity of the error sensors, a smaller reduction in pressure at the actual error sensor and a larger volume over which substantial noise reduction is obtained (sphere of radius half a wavelength as opposed to one-tenth for pressure only minimisation). Algorithm Improvements - Robustness. Performance and Convergence Speed. Improvements to algorithms continue to be made in an effort to reduce computing power requirements (thus allowing faster tracking 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 main categories: one which minimizes the mean square error and the other which minimizes a filtered version of the mean square error, thus leading to a biased result. They provide a good summary of the performance in reducing low-frequency sound in a ship of a range of algorithms (the filtered reference LMS algorithm,the filtered error LMS algorithm, the filtered-LMS algorithm, the filtered reference - filtered error LMS algorithm and the preconditioned LMS algorithm), after correcting one of them to satisfy causality during the 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 the secondary path dynamics. In this method, the tonal components are estimated in advance and then a gradient approach is used to update controller and model coefficients for each tonal frequency to be controlled. This approach avoids a priori modelling, which is a characteristic of feedback control systems, and no reference signal, which is an essential part of a feedforward system, is needed. Also for each frequency to be controlled, there are only two controller and model coefficients to be tuned, representing amplitude and phase shifts. The disadvantage of this method is that the error signal needs to be passed through one filter for each frequency component and an amplitude and phase of each determined. These are then used by the algorithm to calculate the required amplitudes and phases of the frequency domain control signal for each frequency. The required time domain control signal for each frequency is then determined using u(t) ' urn cos(ωn t) % uin sin(ωn t) , where urn and uin are the real and imaginary coefficients determined by the algorithm and ωn is the corresponding frequency. Qiu and Hansen (2001a) discuss various modifications that must be made to the filtered-x LMS algorithm to make it work in various applications. They also discuss the application of the waveform synthesis algorithm to the active control of electrical transformer noise (Qiu et al., 2002). The application of control output constraints in practical implementations of the filtered-x LMS algorithm has also received attention (Qiu and Hansen, 2001b, 2002a). This allows the limited driving capability of loudspeakers and actuators 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 to the error sensor input to the controller) is an essential part of maintaining the stability of a feedforward control system. If the cancellation path is time invariant it only needs to be modelled when the controller is started up. However, there are many cases where the cancellation path impulse response varies with time and its model must be updated on a regular basis. Using the control signal as the on-line modelling signal is one method that has been used in the past, but this is really only successful when there is only one control source, due to interference from other control source signals in a multi-channel configuration and due to the correlation of the control signal with the primary noise. The other way of on-line modelling that has been commonly used in the past is to introduce low level random noise into the system through the control sources when the model needs updating. This can cause loss of controller performance and can be subjectively annoying, so it has met with varying degrees of success. Veena and Narasimhan (2002) successfully used an escalator lattice predictor based noise
canceller for modelling the cancellation path for both feedforward and feedback systems in the presence of narrowband noise fields. Their method uses the control signal for modelling, but unlike the traditional method, it uses a the noise canceller to remove the influence of the primary noise field, resulting in a more accurate and more robust estimate of the cancellation path, even for a non-
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, which is uncorrelated with the primary noise. Although white noise is often used for this, other signals such as swept 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 controlling the level of the introduced modelling signal have been used. One involves using a small modelling signal when the cancellation path appears to be changing by a small amount and using a larger signal as the change appears larger. In addition, researchers have used a modelling signal with a similar spectral shape to the primary 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 the modelling signal. They found that low level modelling signals could provide a good cancellation path estimate with levels less than 20 to 30 dB below the primary noise signal. However, the model took a very long time to converge to the correct solution. On the other hand, they found that a high level modelling signal resulted in very fast convergence (less than 0.5 seconds for their system) but the level of the modelling signal needed to be 10 dB above the primary noise signal for that short time. Thus they suggested that when a system is first turned on or when a large change in cancellation path model is detected, a large modelling signal amplitude be used to provide a useable cancellation path model in a short time and at other times a very low level modelling signal should be used. They found this method to be much more reliable than use of the control signal for cancellation path modelling. Semi-Active Control. Semi-active control is the terminology applied to systems that use active control to change system parameters to reduce the noise or vibration of a system without the introduction of any additional control forces or sound. Johnson and Esteve (2002) provided simulation results showing the relative effectiveness of various local cost functions in tuning multiple resonators to reduce the kinetic energy in a cylindrical shell (structural mass-spring resonators) and the potential energy in a cylindrical enclosure (Helmholtz resonators) for excitation by a low frequency external acoustic source. The adaptive algorithms tuned the stiffness of the structural resonator and the length of the Helmholtz resonator neck. The best cost function was found to be the cross product of the absorber mass velocity and the absorber base velocity for the structural vibration absorber and the cross product of the Helmholtz resonator internal pressure and the external pressure immediately outside its neck. Monaco et al. (2002) used magnetostrictive active elements in a semi-active vibration absorber to produce 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 a considerable body of work exists that is devoted to this topic. Liu et al. (2002) provided a review of existing semi-active vibration isolation systems and presented a system for shock and vibration isolation of a base excited system (eg electron microscope). They showed that their semi-active system performed much better than 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 to enhance the sound reproduction experience so that the apparent direction from which sound comes can be controlled using signal processing in such a way that sound from two speakers placed side by side can 1 I1 E1 appear to originate from any desired direction (Nelson, 2002, Kim et al., C2 2002). The set up to achieve this is shown in block diagram form in I2 Figure 25. In the figure, the actual E2 sound is recorded using the two C1 2 Dummy head microphones in a binaural dummy head. The signal thus recorded is Fig. 25. Binaural sound reproduction using inversion filters and cross-talk inverted, then passed to speakers to cancelling filters.
reproduce the required sound at the listener’s ears. Inversion is necessary to derive the signal to drive the speaker so that the signal at the ear will be the same as sensed in the dummy head. In practice, some sound from speaker 1 actually appears at ear 2 and similarly sound from speaker 2 appears at ear 1. This is called cross-talk and this where active noise cancellation comes in; ANC is used to cancel the crosstalk and optimum algorithms to achieve this are the subject of on-going research. These algorithms are used to adjust the weights of the digital filters shown as C1 and C2 in the figure. Filter C1 acts on the signal sent to speaker 1 (prior to inversion) to produce a signal out of speaker 2 to cancel the speaker cross talk at ear 2 from speaker 1. Similarly for filter C2. An important parameter is the condition number of the matrix that is inverted to produce the signal to drive 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 two speakers of figure 25. It can be seen that for high frequencies a low condition number (white areas) can be achieved using a small angular separation between the sources, while at low frequencies a larger separation is necessary. In the figure a suggested scheme using 3 pairs of speakers at 10, 30 and 180 degree separation results in Fig. 26. Illustration of the effect of frequency and source span on the condition number of the inversion matrix used to reprodice a binaural sound field. The source span is the angle subtended low condition by the two loudspeakers at the ear. The light or white lines represent a low condition number and numbers over the the black lines represent a high condition number. For good reproduction a low condition number entire audio is necessary. The condition number is denoted κ (CN). frequency range.
RECENT APPLICATIONS Applications that are currently being studied include, but are not limited to, the following: aircraft interior noise, 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 subject of recent research (Johansson, et al., 1999, 2000), in which control is performed on the propeller blade pass frequency and three higher order harmonics using frequency domain control with 32 loudspeakers and 48 error sensors. They obtained reductions of 17, 10, 6 and 6 dB for the fundamental and first three harmonics respectively, averaged over the error sensors, which were mounted at passenger ear locations. Note that noise 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 and found 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-driven aircraft for a number of years and there are now several hundred systems flying. Their active control system uses up to 96 input channels (reference and error sensors) and has up to 48 output channels for driving the
control actuators, which may be inertial actuators attached to the frame/stringer interfaces of the fuselage or loudspeakers mounted in the trim panels. Most of the error sensors are microphones mounted behind the trim panels (just above window height on the side and along the centreline of the ceiling panel) and also in the overhead lockers, but when inertial actuators are used as control sources, some accelerometers are also used as error sensors to ensure that fuselage vibration levels do not increase. Systems installed by Ultra Electronics typically achieve 10 dB, 7 dB and 3 dB noise reductions for the fundamental, first harmonic and second harmonic respectively of the propeller blade passing frequency. The measured reductions are averaged over a plane in the cabin at passenger head height. Greater reductions that mentioned above are achieved 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 effort in recent years. As airflow noise is a random phenomenon, it cannot be effectively controlled with feedforward methods, since it is difficult to obtain a sufficiently time-advanced reference signal that is well correlated to the random disturbance. The advantage of the feedback approach is that it does not require any external reference signal and has the potential to be implemented as a Turbulent boundary layer excitation decentralised selfTunnel walls xcm reference sensors contained control system suitable for the aircraft Frames Panel industry (Maury et al., 2002). Fuller (2002a) 5 x 3 array of microphones Actuators has used smart (4 used as error sensors) foam with a f e e d b a c k controller to reduce flow noise Fig. 27. Schematic of smart foam system for controlling boundary layer induced noise in aircraft transmission using panels. (Courtesy, Fuller, 2002a). the facility shown in Figure 27. The effect of active control is shown in Figure 28.
Fig. 28. Performance of active smart foam for controlling boundary layer noise. (Courtesy, Fuller, 2002a)
Recent work being undertaken at NASA Langley Tensioned Panel (Gibbs and Cabell, 2002) has demonstrated that 1 turbulent boundary layer noise generated by the air 2 3 flow over the fuselage can be reduced using feedback 1 5 active control. A schematic representation of their test 5 4 6 rig is shown in Figure 29. They showed that using 11 15 independent, single channel control of each of six Frequency panels, they could achieve a 15 dB reduction in interior Weighted noise radiation at panel resonances and 7.5 dB for Accel Average broadband noise. Interactions between adjacent panels do reduce the performance slightly and this may be PZT Radiated improved slightly with a multi-channel controller. Actuator Sound However, experiments on a single panel showed that u(k) Power, y(k) increasing the controller complexity to three actuators and nine sensors (rather than one of each) and using a Feedback multi-channel controller increased the performance by Compensator between 1 and 2 dB. It is important to realise that when GPC undertaking analyses of aircraft interior noise and its control, the fuselage is under tension due to the cabin Fig. 29. Arrangement for single channel feedback control internal pressure and this does have a significant effect of turbulent boundary layer interior noise radiation (Courtesy NASA Langley) on structural resonance frequencies which in turn affects the control system noise reduction performance. Maury et al., (2002) undertook a detailed analysis of the potential for feedback active control to reduce the transmission of flow induced noise through a double panel into an aircraft cabin. The double panel was modelled 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 first trim panel mode resulted in 17 dB attenuation. The results they obtained are summarised in Table 1. From this initial work, the control of flow-induced cabin noise in aircraft appears to be a promising application that will be implemented in aircraft in the relatively short term.
Table 1 Calculated attenuation (dB) in the total sound power radiated by the stiffened double-panel system calculated up to 3 kHz and up to 1 kHz for various control scenarios.
Aircraft Jet Engine Noise. Considerable progress has been made in recent years on the active control of aircraft 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 that cancels 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 an attempt to reduce noise from a jet engine. The details of the controller arrangement and actuator placement are shown in Figures 30 and 31.
4 ft Fan (16 Blades)
Stator Vanes (28)
ICD
Rotating Rake mode measurements inlet & exit plane
Contro l Micropho nes fo re & aft spo o l se c tions (1 6 eac h ro w, 9 6 t ot al)
PZT (THUNDER) Act uato rs 6 pe r vane , 4 Channe ls of Co ntrol
Fig. 30. NASA 48” ANC fan rig with BBN active vanes. (Courtesy Silcox, 2002).
Hilbrunner, et al. (2002) have used a different approach, which involves enhancing the absorptive characteristics of the Nacelle (or wall of the engine) using an active element and a feedback control system as shown in the nacelle cross-section in Figure 32. The cancelling sound is generated by a bimorph PZT actuator causing the plate to which it is bonded to vibrate laterally as shown in the figure. Car and Van Interior Noise. There are a number of research groups currently working in this area and the work has been elegantly summarised by Sano et al. (2002). Active engine mounts (called ACM by Sano) have been developed and used to control engine harmonics being structurally transmitted and radiated as noise in the cabin. Loudspeakers in the vehicle cabin (ANC) have been used commercially as early as 1991 to control booming noise generated by the engine and later to control road noise. The history of commercial application is summarised briefly in Table 2.
Fig. 31. Detail of piezo-electric actuators on jet engine fan blades, (Courtesy Silcox, 2002).
Fig. 32. Cross-section of a hybrid Nacelle liner for a jet engine (Hilbrunner et al., 2002)
1991 ANC for booming noise by Nissan
1997
1998
2000
ANC for ACM for ACM for idling NV diesel engine low-frequency road noise by Toyota by Nissan by 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 reference signal from the engine crank rotation pulse and used an adaptive feedforward architecture with a multiple error FXLMS algorithm. The 1997 system developed by Toyota was a fixed (non-adaptive) feedforward system using a hydraulic active engine mount and had the effect of reducing booming noise and idling vibration by 5 to 10 dB. The 1998 system developed by Nissan uses active engine mounts driven by electromagnetic actuators and operates over the range, 20 to 130 Hz. It attacks 2nd, 4th and 6th order engine components and uses a 16bit 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 currently possible to get. It consists of the use of the 4 standard car audio speakers as the control sources and uses a 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 the front 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 with the same signal. The purpose of the feedforward controller is to counteract the increase in rear seat noise levels as a result of the feedback controller operation to reduce the front seat noise levels. A 10 dB reduction in drumming noise was achieved. Costs were further reduced by using analog circuits for the controllers and 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 that it is not considered by management or consumers to be cost effective. That is, the perceived benefit of ANC does not equate to its cost. Thus, it is important to concentrate research effort in finding lower cost ways of achieving the same results. An example of a low cost system is provided by Pfann, et al. (2002) who set up a demonstrator in a headrest in a science museum using a Pentium III, 700 MHz computer which interfaced to the amplifiers, speakers and microphones through a standard sound card. An adaptive normalised FXLMS algorithm was implemented on the PC using the C++ programming language. The large group delay of 1/8 second in the controller was OK for this application which used recorded noise, but it would not normally be workable in a real system. Even for this ideal system the large group delay did have a significant effect on the convergence speed. In other recent work on vehicles, Ramos, et al. (2002) have tested an ANC system inside a van, achieving attenuation between 15 and 20 dB for the main harmonics of the engine noise at error sensors placed in the rear seats. Future work includes a more advanced controller for more channels that will allow the front seats to be treated as well. However, no mention was made of the amount of control achieved at locations removed 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 other drivers, it would be desirable if their standard production car could be made to sound like a Porsche or Ferrari, during acceleration, idle and cruising. Schirmacher et al. (2002) have developed such a system for a volkswagen, a schematic of which is shown in Figure 33. This system uses the car audio system as the sound sources and 6 microphones mounted in the roof liner as the error sensors. Active sound design differs
from active noise control in that the goal is not maximum sound reduction but the generation of a target sound. Thus an active sound design system requires a target sound generation system in addition to the active noise control subsystem. The target sound generation subsystem uses engine load and rpm data as well as rate of change of speed data to generate the required target sound at any given instant. One important aspect of the active sound design approach is that the sound of a car can be modified with software only, making the manufacture of different models and custom models relatively low cost.
Engine rpm, load, additional quantities
rpm target signal
-
+
residual signal
Fig. 33. Schematic of a vehicle active sound design system (Courtesy Schirmacher et al., 2002)
Car Suspensions. Active and semi-active suspensions on automobiles have been the subject of research for many 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 problems associated with the active control of truck exhaust noise include the hostile environment for the actuators (usually loudspeakers) and the high levels of sound that must be generated by the actuators. A new type of actuator is illustrated in Figure 35. It does not suffer from the same problems as an oscillating flapper valve that is placed in the exhaust line and driven in such a Fig. 35. Schematic of an oscillating flapper valve in a way as to inhibit transmission of sound past it (Fohr et truck exhaust. (Courtesy Fohr et al., 2002) al., 2002). In practice, the valve oscillates about the horizontal position with a maximum amplitude of 12 degrees. Boonen and Sas (2002) also reported some results using an exhaust valve to control noise transmitted down the exhaust pipe. They found that inserting an active valve in the flow of a volume velocity source, like a combustion engine, has little effect on the attenuation of noise if no capacitive elements such as volume chambers are present between the source and the valve. The same conclusion was reached by Fohr, et al. Also, without additional capacitive elements, a high back pressure will be generated (Boonen and Sas, 2002). However, with the additional upstream capacitative element, they showed on a cold engine simulator that a noise reduction of 13 dBL (4 dBA) was possible for a consumption of about 5 W of electrical power and a back pressure of 10 kPa. Fig. 34. Prototype oscillating exhaust valve. (Courtesy Fohr et al., 2002)
Headsets, Ear Muffs and Headrests. These devices have such wide application that there are continuing efforts being made to improve their performance. Traditional headsets have used feedback configurations and analog electronics to minimise the time delays associated with signal processing. However, some problems still remain such as “squealing” when subjected to very loud sound fields and acceptable performance only over approximately two octaves. More recently, better performance has been demonstrated with digital feed forward systems and work on improving practical implementation of the control algorithms is continuing (Cartes et al., 2002). Zimpfer-Jost (2002) explain how use of digital rather than analog feedback control allows the system to be optimally configured for any particular noise environment by simply changing software. The choice of an IIR (rather than FIR) filter in the feedback loop reduces processing time and subsequent delay through the 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 fixed point (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 new algorithm involves adding a term to the expression used to calculate the filter output, which compensates for 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 the performance of a headset. The analog part is directed at controlling random noise, while the digital part is directed at controlling dominant tones in the spectrum. He obtained in reductions in excess of 10 dB over 2 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 problems related to insufficient excitation, nonstationary noise fields, and time-varying signal-to-noise ratio associated with using feedback control in headsets. Ray et al. (2002) showed using simulation that the performance of the feedforward systems could be further improved using a Lyapunov-tuned, normalised filtered-x LMS algorithm with leakage, which is designed to accommodate both a non-constant forward path transfer function and measurement noise on the reference input provides additional noise reduction performance gains. The development of active noise control in headrests continues to attract considerable attention (Tseng et al., 2002; Holmberg et al., 2002). The most popular arrangement involves the use of virtual microphones, with the physical sensors and actuators embedded in the headrest and the points of sound field minimisation being located at the approximate ear positions. Again feedback systems seem to dominate the work in this area as the direction of incident sound is not as critical as it is for feedforward controllers which require a reference sensor to be located between the noise source and the error sensor and at a significant distance from the error sensor if broadband noise is to be Fig. 36. (1) Control loudspeaker, (2) headrest, (3) virtual microphone controlled. A typical arrangement showing position and (4) error microphone. (Holmberg et al., 2002) only one side of the headrest and one ear is shown in Figure 36. Duct Noise. The active control of sound propagating and exiting from ducts has been the subject of research and application for decades. There are over 100 such installations in industrial facilities in the USA, but practically all of them are directed at controlling plane waves. Above the cut-on frequency of the first higher mode, plane wave control systems will become decreasingly effective as the frequency of the disturbance increases. Thus current research on active control of sound propagating in ducts is focussing on the control of 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 EC), which is the subject
of the development of an active noise control system at the University of Adelaide, the first higher order mode cuts on at 140 Hz and the noise problem is at 190 Hz, at which two higher order modes as well as the plane wave are propagating. In addition, the noise source, which is a 200 kW centrifugal fan, produced an unsteady tone that fluctuated wildly in level at any particular location in the duct. Theoretically, control of the tone at 190 Hz should be possible with three control sources and three error sensors, but in practice, six control sources and 12 error sensors were found to give the best results. Also a fast version of the Filtered-x LMS algorithm had to be developed to allow tracking of the rapidly varying level of the tone. This is discussed 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 the development of relatively low cost hybrid active/passive systems, where the duct liner, which represents the passive part is effective in the mid- to high-frequency range and the active part is effective in the low frequency range. The active part of these systems, which are now commercially available, consists of an analog 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 provided an insertion loss of between 20 and 30 dB in the frequency range 400 to 1000 Hz and when the active part was switched on the insertion loss increased to between 20 and 55 dB in the frequency range, 100 to 1000 Hz. Kruger's system is illustrated in Figure 37, where the passive liner can be seen to be located on the opposite 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 100 mm. Thinner liners will result in reduced insertion loss performance. It can also be seen that each loudspeaker is controlled by an independent feedback system with an independent error microphone.
Fig. 37. Hybrid active/passive duct silencer (Kruger, 2002).
Cable-Stayed Structures. Premont and Bossens (2002) have applied collocated sensing and actuation to the active control of vibration in both large space structures and cable-stayed bridges (see Figure 38). The difference between the two applications is that for large space structures, piezo-electric actuation is used as it is lightweight and easy to implement, while for bridges, hydraulic actuation is used due to the larger forces required.
Fig. 38. Example of active control of a cable-stayed bridge (Courtesy Premont and Bossens, 2002)
Active Vibration Isolation. The active z y rotating machine isolation of sensitive instrumentation from simulated with support structure vibration such as the lens x primary shakers mount isolator described by Holterman, et top mass al. (2002) continues to attract attention intermediate accelerometers from researchers. The complementary mass problem of active isolation of vibrating equipment from support structures is also the subject of continuing research. At The bottom mass control shakers University of Adelaide (Li et al., 2002), the use of active control to enhance the performance of a two stage passive Fig. 39. Two stage hybrid vibration isolation system. vibration mount, shown in Figure 39, to minimise the transmission of low-frequency tonal vibratory energy from diesel engines into a submarine hull is being investigated. The active system consists of seven actuators and seven sensors mounted on the 500 kg intermediate mass of the two stage isolator. The actuators are being driven to control the amplitude of the six rigid-body modes of the intermediate mass. However, they found that best results in terms of minimising the overall vibration level of the intermediate mass in all six degrees of freedom were obtained using kinetic energy control which involved minimising the sum of the squares of all error accleerometer signals. The results they obtained with real-time control in a laboratory environment are illustrated in
Acceleration level (dB re: 1m/s2)
Figure 40 where it can be seen that substantial reductions in vibration transmission are possible for the first four engine harmonics. Higher order harmonics were not targeted and the poor control at the 2nd order was due to the shakers being unable to generate sufficient output at this frequency.
-40 -50 -60 -70 -80 -90 engine operating speed and harmonics 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 resonance frequencies of control actuators were around 1st and 2.5th order engine speed.
CONCLUSIONS Although there are many applications for which active noise control or active vibration control is impractical, 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, and acoustic modelling. Research also continues to push the application boundaries and some applications that seemed impractical ten years ago are now beginning to look possible. So active noise control does indeed have a very bright future and research will need to continue for some time to come to ensure that full advantage is taken of this exciting technology. ACKNOWLEDGEMENTS The author would like to acknowledge a number of researchers who provided copies of their Active 2002 presentations and kindly gave permission to use their figures for this paper and the accompanying presentation 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 like to thank other members of the University of Adelaide, School of Mechanical Engineering ANVC Group not mentioned 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 Active 2002, 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.
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