Classifications

• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
  • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
  • G10K11/1781—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions
  • G10K11/17821—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions characterised by the analysis of the input signals only
  • G10K11/17823—Reference signals, e.g. ambient acoustic environment
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
  • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
  • G10K11/1785—Methods, e.g. algorithms; Devices
  • G10K11/17853—Methods, e.g. algorithms; Devices of the filter
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
  • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
  • G10K11/1785—Methods, e.g. algorithms; Devices
  • G10K11/17853—Methods, e.g. algorithms; Devices of the filter
  • G10K11/17854—Methods, e.g. algorithms; Devices of the filter the filter being an adaptive filter
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
  • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
  • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
  • G10K11/1787—General system configurations
  • G10K11/17879—General system configurations using both a reference signal and an error signal
  • G10K11/17883—General system configurations using both a reference signal and an error signal the reference signal being derived from a machine operating condition, e.g. engine RPM or vehicle speed
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
  • G10K2210/10—Applications
  • G10K2210/121—Rotating machines, e.g. engines, turbines, motors; Periodic or quasi-periodic signals in general
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
  • G10K2210/30—Means
  • G10K2210/301—Computational
  • G10K2210/3028—Filtering, e.g. Kalman filters or special analogue or digital filters
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
  • G10K2210/30—Means
  • G10K2210/301—Computational
  • G10K2210/3032—Harmonics or sub-harmonics
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
  • G10K2210/30—Means
  • G10K2210/301—Computational
  • G10K2210/3051—Sampling, e.g. variable rate, synchronous, decimated or interpolated
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
  • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
  • G10K2210/50—Miscellaneous
  • G10K2210/512—Wide band, e.g. non-recurring signals

Definitions

• the invention relates to a harmonic filter which is a signal processing means for obtaining the complex amplitude of a single harmonic component from a signal which contains one or more harmonic components.
• the filter can be used in active or adaptive control systems for attenuating disturbances.
• the approaches differ in the way the controller output is obtained and adjusted.
• the output is generated by filtering reference signals.
• the amplitude and phase of each signal is adjusted in the time domain by a variable filter as in Swinbanks, while in the other approach the controller output is updated in the frequency domain using the Discrete Fourier Transform of the residual signal as in Chaplin for varying frequencies, and for fixed frequencies in "Adaptive Filtering in the Frequency Domain” by Dentino et al, IEEE Proceedings, Vol 69, No. 12, pages 474-75 (1978).
• the first approach can be implemented digitally by using a frequency sampling filter followed by a two-coefficient FIR filter or by using a frequency sampling filter followed by a Hilbert transformer and two single coefficient filters.
• synchronous sampling has two disadvantages. Firstly, the anti-aliasing and smoothing filters must be set to cope with the slowest sampling rate. Since the upper control frequency is fixed, a large number of points may be required per cycle. Secondly, because of the varying sample rate, continuous system identification is complicated.
• the system of this invention provides a method for obtaining the complex harmonic amplitudes of a single with varying fundamental frequency without the need for synchronous sampling.
• the system can be used for both feedforward and feedback control.
• a further object of this invention is to provide a harmonic filter control system for both feedforward and feedback systems.
• Fig. 1 is a flow diagram of a harmonic filter comprising the invention
• Fig. 2 shows an output processor for one harmonic
• Fig. 3 is a diagrammatic view of a control system
• Fig. 4a is a representative showing of a moving average FIR filter
• Fig. 4b is a representative showing of a moving average recursive filter
• Fig. 5 is a diagrammatic showing of a recursive harmonic filter
• Fig. 6 is a diagram of a control system with on-line system identification.
• This invention relates to a harmonic filter, and its use as part of a control system.
• the harmonic filter is shown in Figure 1. It consists of a pair of multipliers and low-pass filters.
• the input signal is multiplied by sinusoidal signals at the frequency of the harmonic component to be identified.
• the resulting signals are passed through the low-pass filters.
• the output from the low-pass filters are estimates of the real and imaginary parts of the desired complex harmonic amplitude.
• the phase of the sinusoidal signal is determined from a phase signal (from a tachometer or a phase locked loop for example) or from integrating a frequency signal.
• the bandwidth of the low-pass filter is variable and is determined by the fundamental frequency of the input signal.
• sensors are used to provide signals indicative of the performance of the system. These signals are sent to harmonic filters and the complex output from the filters are used to adapt the controller output.
• harmonic filters are combined with output processors and an adaptive controller.
• the output processor for one harmonic is shown in Figure 2.
• the real and imaginary parts of the complex amplitude of the output are determined by the controller. These are then multiplied by sinusoidal signals and summed to provide one harmonic of the output signal.
• the sinusoidal signals are the same as those used in the harmonic filters.
• Each harmonic of the controller output is generated by an output processor (01, 02, 03,.7) which combines a complex amplitude, Y with sine and cosine signals.
• the controller output is obtained by summing these components. If the controller is to be used as part of an active control system, this output is then converted to the required form and sent to an actuator which produces the canceling disturbance.
• the input to the controller is a residual or error signal r(t).
• r(t) is responsive to the combination of the original disturbance and the canceling disturbance as measured by a sensor.
• the residual signal is then passed to one or more harmonic filters (HF1, HF2, HF3, ).
• the harmonic components, (Rl, R2, R3, ), of this residual signal are then used to adjust the complex amplitudes, (Yl, Y2, Y3, ), of the output.
• a steady state, periodic signal r(t) can be written as a sum of harmonic components
• the purpose of the harmonic filter is to determine the complex amplitudes R .
• the complex amplitudes R are obtained by multiplying by a complex exponential and integrating over one or more complete cycles of the signal, so that
• the harmonic filter is designed to provide a real-time estimate of the harmonic components of a signal.
• the basic approach is to multiply the signal by the appropriate cosine and sine values and then to low-pass filter the results. This process, shown in
• Figure 2 is equivalent to multiplying by a complex exponential signal, exp(ik ⁇ t), and then passing the result through a complex low-pass filter. The process is sometimes called heterodyning.
• the multiplication by the complex exponential acts as demodulator, and the resulting signal has components at d.c. (zero frequency) and at twice the original frequency, for harmonic signals the harmonic frequencies are all shifted by +/- the frequency of the exponential signal, therefore the resulting signal may have components at the fundamental frequency. These must be filtered out to leave only the d.c. component.
• the bandwidth of the filter With a fixed low-pass filter, the bandwidth of the filter must be set to cope with highest fundamental frequency likely to be encountered. When the system is operating at the lower frequencies, the low-pass filter is then much sharper than necessary, and therefore introduces much more delay than is necessary.
• the bandwidth of the filter according to the current fundamental frequency it can be ensured that the harmonic filter has minimum delay. This is particularly important for use with control systems where any delay adversely affects the controller performance.
• One way of implementing the low-pass filter is by a moving average process.
• period P is defined as the time taken for the phases to change by 2 ⁇ radians, i.e.
• the method is complicated by the fact that the period P is not generally an exact number of samples. If the sampling rate is high enough compared to the frequency of the harmonic being identified the truncation error can be neglected and the integral approximated by using the M samples in the current cycle. At time mT, the estimate can be obtained using a Finite Impulse Response (FIR) filter with M+l coefficients.
• FIR Finite Impulse Response
• Equation (5) The summation in equation (5) can be calculated recursively, that is, the next estimate can be calculated from the current estimate by adding in the new terms and subtracting off the old terms.
• R k ((m+l)T) (P m /2) .R k (mT)
• R k ((m+ 1 )T) ( 1 - e - a ⁇ T )r((m+ 1 )T)e -* «* + e ⁇ TR- ⁇ (mT), ( 10)
• a a positive constant which determines the effective integration time
• T the sampling period
• ⁇ the fundamental frequency. Note that the bandwidth of the 'filter, i.e. the effective integration time, is scaled by the period of the noise. This is essential to obtain a uniform degree of independence of the harmonics.
• the filter is shown in Figure 5. It can be implemented in analog or sampled data form.
• Another advantage is that a can be varied dynamically to reduce the integration time during transients.
• the bandwidth of the filter In order to separate out the different harmonic components, the bandwidth of the filter must be adjusted as the fundamental frequency of the disturbance varies. Note that the bandwidth of the filter is varied according to the fundamental frequency, not the frequency of the harmonic being identified.
• the low-pass filter is designed to have zeros in its frequency response at multiple fundamental frequency.
• There are many other ways of implementing low-pass filters with these properties which will be obvious to those skilled in the art of analog or digital filter design.
• the exponential terms and sinusoidal terms used in the computation can be stored in a table.
• the resolution of the table must be chosen carefully to avoid errors.
• the exponential terms could be calculated at each output time, using interpolation from tabulated values, trigonometric identities or expansion formulae for example.
• the controller output varies on the same time scale as the output from the harmonic filters (see co-pending patent application [13]).
• the outputs from the harmonic filters are used directly as inputs to a non ⁇ linear control system.
• the controller output In active control systems the controller output must have a particular phase relative to the disturbance to be controlled. In this case some output processing is required, which is effectively an inverse heterodyner.
• nT which is calculated by the output processor
• y(nT) ⁇ Re(Y k ) . cos(k ⁇ nT) - Im(Y k ) . sin(k ⁇ nT) ⁇ (11) where co is the fundamental radian frequency, Re denotes the real part and Im denotes the imaginary part, and where k is the harmonic number, K is the total number of harmonics in the signal and Y is the complex amplitude of the output at the appropriate harmonic.
• the values Y can be stored in memory and the output calculated at each output time, as described by Ziegler.
• the output processor uses the same sine and cosine terms as the input heterodyner.
• the algorithms for adjusting the output values Y require knowledge of the harmonic components of the residual or error signal. These are provided by the outputs from the harmonic filters. Adaptive Algorithm
• the known frequency domain adaptive algorithms can be used to update the complex amplitudes of the output.
• a common choice for multichannel systems is to use
• Y k n (l - ⁇ )Yr - ⁇ .B( ⁇ ).I ⁇ - > (12)
• Y is the vector of outputs at the n-th update and the k-th harmonic
• R is vector of residual components
• ⁇ is the convergence step size
• ⁇ is a leak applied to the output coefficients
• B( ⁇ ) is a complex matrix related to the system transfer function matrix at the current frequency of this harmonic.
• ⁇ can be a complex matrix related to A( ⁇ ) and B( ⁇ ). If the system transfer function is A( ⁇ ), then for the LMS algorithm,
• a pseudo-inverse form is preferred since it allows the harmonic components to converge at equal rates - which is one of the main advantages of frequency domain algorithms. It is also preferred for multichannel systems since it allows for various spatial modes of the system to converge at a uniform rate.
• the convergence step sizes for the algorithms which update at every sample are determined by the response time of the whole system. This is the settling time of the physical system (the time taken for the system to reach a substantially steady state) plus a variable delay due to the low-pass filter.
• the constant ⁇ in (12) must be replaced by frequency dependent parameter, ⁇ ( ⁇ ). This parameter must take account of the effective delay in variable filter.
• the constant ⁇ can also be replaced by a frequency dependent parameter ⁇ ( ⁇ ). This parameter can be adapted to limit the amplitude of the output.
• the adaption process is performed every sample interval or at a rate determined by the cycle length (fundamental period) of the noise.
• the first approach has the disadvantage that the sampling rate and/or the number of harmonics to be controlled is limited by the processing power of the controller.
• the second approach has the disadvantage the computational requirements vary with the frequency, which may not be known in advance, and also the adaption rate is limited by the fundamental period of the disturbance.
• the harmonic components are available every sample and the controller output is calculated every sample, but the adaption process can be performed at a slower rate if required.
• this slower rate is determined in advance to be a fixed fraction of the sampling rate, in another embodiment of the invention the adaption is performed as a background task by the processor. This ensures that optimal use is made of the available processing power.
• the sampled data control systems described above use constant sampling rates. This facilitates the use of on-line system identification techniques to determine the system impulse response (and hence it transfer function matrix). Some of these techniques are well known for time domain control systems. Tretter describes some techniques for multichannel periodic systems.
• a random (uncorrelated) test signal is added to the controller output after the output processor but before the Digital to Analog Converter (DAC).
• the response at each sensor is then measured before the heterodyner, but after the Analog to Digital Converter (ADC).
• ADC Analog to Digital Converter
• This response is then correlated with the test signal to determine a change to the relevant impulse response.
• the correlation is estimated from a single sample.
• Figure 6 This can be extended to multichannel system by applying the test signal to each actuator in turn or by using a different (uncorrelated) test signals for each actuator and driving all actuators simultaneously.
• the plant in Figure 6 includes the DAC, smoothing filter, power amplifier, actuator, physical system, sensor, signal conditioning, anti-aliasing filter and ADC.

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• Physics & Mathematics (AREA)
• Engineering & Computer Science (AREA)
• Acoustics & Sound (AREA)
• Multimedia (AREA)
• Soundproofing, Sound Blocking, And Sound Damping (AREA)
• Feedback Control In General (AREA)
• Networks Using Active Elements (AREA)

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• 1992
  • 1992-06-25 CA CA002138552A patent/CA2138552C/fr not_active Expired - Fee Related
  • 1992-06-25 DE DE69229282T patent/DE69229282T2/de not_active Expired - Fee Related
  • 1992-06-25 WO PCT/US1992/005228 patent/WO1994000911A1/fr active IP Right Grant
  • 1992-06-25 EP EP92914435A patent/EP0647372B1/fr not_active Expired - Lifetime
  • 1992-06-25 DK DK92914435T patent/DK0647372T3/da active

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