WO1995026521A1 - Time domain adaptive control system - Google Patents
Time domain adaptive control system Download PDFInfo
- Publication number
- WO1995026521A1 WO1995026521A1 PCT/GB1995/000666 GB9500666W WO9526521A1 WO 1995026521 A1 WO1995026521 A1 WO 1995026521A1 GB 9500666 W GB9500666 W GB 9500666W WO 9526521 A1 WO9526521 A1 WO 9526521A1
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- WO
- WIPO (PCT)
- Prior art keywords
- filter means
- output signal
- plant
- filter
- adaptive
- Prior art date
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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
- G10K11/17813—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 acoustic paths, e.g. estimating, calibrating or testing of transfer functions or cross-terms
- G10K11/17817—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 acoustic paths, e.g. estimating, calibrating or testing of transfer functions or cross-terms between the output signals and the error signals, i.e. secondary path
-
- 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
-
- 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
- G10K2210/3018—Correlators, e.g. convolvers or coherence calculators
-
- 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
- G10K2210/3035—Models, e.g. of the acoustic system
-
- 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
- G10K2210/3053—Speeding up computation or convergence, or decreasing the computational load
Definitions
- the present invention relates to an adaptive control system and method for controlling a plant.
- the present invention relates to an adaptive control system which uses adaptive response filter means operating in the time domain.
- the basic principles of the closed loop adaptive control of a plant is to monitor the output of the plant and modify the plant control signals in order that the signals output from the plant converge to a desired level. Thus the plant is being controlled to operate as desired.
- plant is used as a control system term to describe a system having at least one input and at least one output, where each input may effect to some degree each output.
- FIG. 1 illustrates the principles behind a control system operating using the filtered x algorithm for a feed forward arrangement wherein a reference signal x(n) is fed into an adaptive filter W which generates a drive or control signal y(n) for a plant denoted by C. The output of the plant is compared with a desired signal which in a feed forward arrangement is the reference signal x(n) having passed through an alternative path A.
- the error signal e(n) is the difference between the desired signal and the output of the plant C and this is fed to a least-mean-squared (LMS) algorithm which updates the coefficients of the adaptive response filter W in order to reduce the difference or error signal e(n).
- LMS least-mean-squared
- the LMS algorithm requires an input from the reference signal x(n) in order that only errors signals e(n) which correlate to the reference signal x(n) are used in the update of the adaptive response filter W coefficients.
- the error signals e(n) will be delayed and thus time shifted with regard to the reference signal x(n).
- the reference signal x(n) is passed through a model of the plant to generate a filtered reference signal r(n).
- the filtered reference signal r(n) will be delayed by the same amount as the error signal e(n).
- the filtered reference signal r(n) is used by the LMS algorithm for correlation of the error signal e(n) with reference signal x(n).
- the model must now model not merely a single response of an error signal e(n) to a control signal y(n), but must instead model a plurality of responses of each error signal e l (n) to each control signal y m (n).
- a convenient way to model the plant response is to use an adaptive response filter having a suitable number J of coefficients to model the delay.
- the multichannel filtered x algorithm there will thus be L x M x J, filter coefficients.
- the LMS algorithm in general is well known and is discussed at length in Chapter 6 (page 99 to 116) of the Widrow and Stearns text book.
- This algorithm updates the coefficients of the adaptive response filter W in order to minimise the sum of the squared values of the L error signals, where each error signal is affected by the output of each drive signal M.
- the conventional multiple error LMS algorithm requires the generation of K x L x M filtered reference signals to perform the adaption of the adaptive response filter W.
- d l (n) is the disturbance at the l error signal in the absence of control
- y m (n) is the n th input signal
- response of the l th error signal to the n th drive signal is modelled as a J coefficient finite impulse response (FIR) filter with co-efficients .
- the m th control signal can be written as
- x k (n) is the sampled k th reference signal and w mki is the i th coefficient of the FIR control filter providing the m th control signal from the k th reference signal.
- the l th error signal can be expressed as a quadruple summation
- the object of the adaptive LMS algorithm is to adjust each of the control filter coefficients to minimise the sum of the squared error signals. This can be termed a cost function.
- the derivation of this cost function with respect to the coefficient w mki can be written as
- Equation 3 By differentiating Equation 3 with respect to w mki and substituting into Equation 4 the gradient of the cost function can be given by
- Equation 5 an instantaneous estimate of the gradient of the cost function is used as in the filtered x algorithm of Widrow and Stearns.
- each k th reference signal is filtered by the J coefficient FIR filter model to give a filtered reference signal
- each adaptive response filter coefficient w mki is updated every sample n by an amount proportional to the negative of this instantaneous gradient estimate to give
- Equations 6 and 8 It can be seen particularly from Equations 6 and 8 that the conventional multiple error LMS filtered x algorithm requires the generation of K x L x M filtered reference signals to perform the adaption.
- the inventor of the present application has realised that if the time realignment of the error signal and the reference signal could be achieved by filtering the error signal then for a control system having a large number of reference signals a reduction in filtering operations by a factor of K could be achieved, thus greatly reducing the computation required for adaptive control.
- the present invention provides an adaptive control system for controlling a plant comprising input signal means adapted to provide at least one input signal for control of said plant; adaptive response filter means having a plurality of time domain filter coefficients and operative to filter the or each input signal to provide at least one control signal for said plant to control said plant to generate at least one required output signal; model filter means having a plurality of time domain filter coefficients which model the response of said plant, and adapted to filter the or each output signal using said filter coefficients of said model filter means in relative time reversed order to provide at least one filtered output signal; and delay means adapted to receive the or each input signal and delay the or each input signal for a predetermined time period; said adaptive response filter means being further operative to receive and collate the or each delayed input signal with a respective filtered output signal, and to adjust said filter coefficients of said adaptive response filter means using the outcome of the collation to adjust the or each control signal such that the or each output signal of said plant converges towards a required level.
- the present invention has the advantage that the error signals and not the input or reference signals are filtered by the model filter. For an adaptive control system having a large number of input or reference signals this represents a considerable computational saving.
- the adaptive response filter means is operative to collate the or each delayed input signal with a respective filtered output signal by forming a respective cross correlation estimate therebetween.
- the or each cross correlation estimate is multiplied by a convergence coefficient which is sufficiently small to smooth out the effect of random errors in the or each cross correlation estimate on the adjustment of the filter coefficients of the adaptive response filter means.
- the adaptive response filter means is operative to adjust the filter coefficients of said adaptive response filter means using a least-mean-squares algorithm such as a derivative of Widrow's LMS algorithm.
- the delay means is adapted to delay the or each input signal by a period of time substantially equal to a maximum time delay provided by said model filter means.
- the model filter means is a J coefficient FIR filter this delay corresponds to the time delay associated with the J coefficient and the or each output signal is filtered by the coefficients in time reversed order, i.e. instead of from the 0 to the (J-1) coefficient from the (J-1) to 0 coefficient.
- the delay means is adapted to delay the or each input signal by a period of time substantially equal to the sum of a maximum time delay provided by said model filter means and a maximum time delay provided by said adaptive response filter means.
- the adaptive response filter means is an I coefficent FIR filter and the model filter means is a J coefficient FIR filter the input signal is delayed by a time period corresponding to the maximum time delay given by the convolution of a J and an I coefficient FIR filter.
- the present invention is particularly applicable to adaptive feed forward control wherein said input means is adapted to provide at least one signal indicative of at least selected signals entering said plant.
- the signal need not provide full information on the amplitude and phase of the selected signals. For example for periodic signals an indication of the frequency is sufficient.
- the adaptive response filter means is operative to filter the or each input signal so as to provide at least one control signal to said plant so that the undesired signals within said plant are actively attenuated.
- the present invention is applicable to an active control system for any plant
- the present invention can be applied to an active control system wherein said plant comprises at least one first transducer adapted to receive a respective said control signal, an acoustic medium, and at least one second transducer responsive to outputs from the or each first transducer to provide respective said output signals, said model filter means being adapted to model the response by first and second transducers and the acoustic medium.
- An adaptive control system for use with such a plant is particularly suited to active noise control and the filter coefficients of the adaptive of the adaptive response filter means can be adapted so as to reduce the sum of the squares of the output signals to zero or to some desired level.
- the present invention also provides a method of adaptively controlling a plant comprising the steps of filtering at least one input signal using an adaptive response means filter having time domain filter coefficients to generate at least one control signal for said plant to control said plant to generate at least one output signal; filtering the or each output signal using model filter means having a plurality of time domain filter coefficients which model the response of said plant, said filtering step comprising filtering the or each output signal using said filter coefficients of said model filter means in relative time reversed order; delaying the or each input signal by a predetermined amount; collating the or each delayed input signal with a respective filtered output signal; and adjusting said filter coefficients of said adaptive response filter means using the outcome of the collation to adjust the or each control signal such that the or each output signal of said plant converges towards a required level.
- Figure 1 is a schematic illustration of a control system operating according to the filtered x LMS algorithm of the prior art
- Figure 2 illustrates schematically the operation of a single channel adaptive control system utilising the new filtered error LMS algorithm according to one embodiment of the present invention
- Figure 3a illustrates a theoretic impulse response for the plant C modelled as a J coefficient FIR filter model
- Figure 3b illustrates the time reversed version of the J coefficient FIR filter model
- Figure 3c illustrates a delayed version of the time reversed version of the J cofficient FIR filter model
- Figure 4 illustrates schematically a multichannel adaptive control system having two input or reference signals and which uses the new filtered error LMS algorithm according to one embodiment of the present invention
- Figure 5 illustrates schematically a multichannel adaptive control system having two error signals and which utilises the new filtered error LMS adaptive algorithm according to one embodiment of the present invention
- Figure 6 illustrates schematically a multichannel adaptive control system having two output signals and which operates according to the new filtered error LMS adaptive control algorithm according to one embodiment of the present invention
- Figure 7 is a schematic drawing of an active vibration control system for practical implementation according to one embodiment of the present invention.
- FIG. 2 many components of this diagram are similar to those shown in Figure 1 and described hereinabove with regard to the prior art filtered x LMS algorithm. What is different in Figure 2 is the use of a delay Z - ⁇ to delay the reference signal x(n) received by the LMS algorithm and the use of a delayed and time reversed J coefficient FIR filter model to filter the error signal e(n) before input into the LMS algorithm.
- the delay ⁇ is equally applied to both the reference signal x(n) and the error signal e(n) and the delay ⁇ is combined with a time reversed filter
- the LMS algorithm does not require the update to be carried out as soon as possible, although this is thought conventionally to be highly desirable.
- the inventor has realised that it is possible to change the time or sample number at which the update is performed, i.e. delay the component parts of the update in order to work out the correlation between reference and error signals in the most convenient way.
- the reference signal x is thus now not dependent on j and the instantaneous gradient estimate can be expressed as
- Equation 15 this algorithm for updating the coefficients of the adaptive response filter is more computationally efficient since the reference signal x is not included in the summation as is required in the filtered x LMS algorithm of Equation 8.
- Figure 3a illustrates a theoretical impulse response for the plant C which is modelled as a J coefficient FIR filter having J time domain coefficients where t is the time delay.
- Figure 3b illustrates the time reversed version of the J coefficient FIR filter, i.e. the time reversed impulse response which is a time advanced response. Implementation of such a filter requires advanced knowledge of the error signal e(n) which is not available.
- Figure 3c illustrates a delayed version of the time reversed filter of Figure 3b delayed sufficiently to only require present and past values of the error signal e(n) for its implementation.
- the filtered error algorithm according to the present invention requires no more computational power. It does however require a buffer for both the reference input signal x(n) and for the error signal e(n) for use as a delay line.
- the filtered error algorithm according to the present invention is far more computationally efficient as discussed hereinafter.
- the delay of the reference and error signals there will be a delay in the update of the coefficients of the adaptive response filter w dependent upon the delay in the model of the plant C.
- the update of the coefficients of the adaptive response filter w should not be performed within the maximum delay time provided by the plant. Thus this is not a limitation in practice.
- Equation 5 The gradient in Equation 5 then becomes
- the reference signal is outside the double summation and hence the double summation only need be evaluated once and not for each reference sign as is done in the filtered x algorithm.
- both the reference signal and the error signal are delayed by J+I-2.
- the delay for the error signal is incorporated within the filter operation.
- This second algorithm does not use the standard LMS type update in which one value of an error signal is multiplied by a sequence of past values of reference signal to form the update for the adaptive response filter w.
- one value of the reference signal is multiplied by a time advanced sequence of the error signal in order to form the update of the adaptive response filter w.
- the reference signal x(n) is delayed and a time reversed and delayed version of the model filter is used to filter the error signal e(n).
- the filtering in the adaptive response filter and in the model filter is performed in the time domain.
- the filtered error algorithm uses dummy time variables to achieve time alignment of the reference and error signals. Although time alignment is required, since the product of the two variables is only an instantaneous estimate of the gradient of the averaged errors, this does not have to be exactly coincident with the time of adaption.
- This second filtered error algorithm although equally as computationally efficient as the first, requires more memory since a larger buffer is required to store the reference and error signals delayed by J+I-2.
- K is the number of reference signals
- M is the number of control signals
- L is the number of error signals
- the response of each error signal to each control signal being modelled as J coefficient FIR filter, and with the adaptive response filter having I coefficients for each control signal and reference signal.
- This comparison of speed is a comparison of the time taken to update the coefficients of the adaptive response filter W. This estimate of increased speed is likely to be conservative since the amount of digital data needed to be moved around by the adaptive control system is reduced.
- FIGS 4, 5 and 6 illustrate respectively three control systems
- 5 and 6 are multichannel versions of the single channel system shown in Figure 2.
- the J coefficient FIR filter models all the paths between the secondary vibration sources and the error sensors and thus provides a model of the delay and reverberant response of the plant.
- Figure 7 A practical active vibration control system for use in a motor vehicle is illustrated in Figure 7 schematically.
- Figure 7 illustrates a multichannel system with four reference signal generators 31 1 through 31 ,
- the present invention is particularly suited to a multichannel system having more than one reference signal since this provides for the greatest computational saving.
- the reference signal generator 31 1 through 31 4 comprise four transducers such as accelerometers placed on the suspension of the vehicle. These transducers provide signals indicative of the vibrational noise transmitted from the road wheel to the vehicle cabin. The outputs of the transducers 31 through 31
- the reference signals are then multiplexed by the multiplexer 34 and digitised using the analogue to digital converter 35. This provides reference signals x k (n) to the processor 36 which is provided with memory 61.
- Four error sensors 42 1 through 42 4 are provided within the vehicle cabin at spaced locations such as around the headlining. These microphones 42 1 through 42 4 detect the noise within the cabin.
- the output of the microphones 42 1 through 42 4 is then amplified by the amplifiers 43 and low pass filtered by the low pass filter 44 in order to avoid aliasing.
- the output of the low pass filters 44 is then multiplexed by the multiplexer 45 before being digitised by the analogue to digital converter 46.
- the output of the analogue to digital converter e (n) l is then in into the processor 36.
- Drive signals y m (n) are output from the processor 36 and converted into analogue signals by the digital to analogue converter 41.
- the output of the digital to analogue converter 41 is then demultiplexed by the demultiplexer 38.
- the demultiplexer 38 separates the drive signals y m (n) into separate drive signals for passage through low pass filters 39 in order to remove high frequency digital sampling noise.
- the signals are then amplified by the amplifier 40 output to the secondary vibration sources 37 1 and 37 2 which comprise loudspeakers provided within the cabin of the vehicle.
- the loudspeakers can comprise the loudspeakers of an in-car entertainment system of the vehicle.
- the drive signals are mixed with the in-car entertainment signals for output by the loudspeakers, as is disclosed in GB 2252657.
- the processor is provided with the reference signals x k (n) and the error signals e l (n) and outputs the drive signals y m (n) and is adapted to perform the algorithm as hereinbefore described.
- FIG 7 shows the processor receiving a clock signal 60 from a sample rate oscillator 47.
- the processor thus operates at a fixed frequency related to the frequencies of the vibrations to be reduced and the frequency is determined by the requirement to meet Nyquist's criterion.
- the processor 36 can be a fixed point processor such as the TMS 320 C50 processor available from Texas Instruments. Alternatively, the floating point processor TMS 320 C30 also available from Texas Instruments, can be used to perform the algorithm.
- the system shown in Figure 7 illustrates a system for cancelling road noise transmitted from the road wheel of a vehicle
- the system can also be used for cancelling engine noise where a reference signal is provided indicative of the noise generated by the engine of a vehicle. In this instance only a single reference signal is required and the full potential computational saving of the algorithm is not utilised.
- the arrangement shown in Figure 7 is not only applicable to a motor vehicle but also to any vehicle such as an aircraft which has a multiple engine. In such an arrangement multiple reference signals, one per engine is provided, thus facilitating use of the computational saving provided by the algorithm.
- the secondary vibration sources illustrated in Figure 7 are loudspeakers they could alternatively be vibrators or mix of both. Further, not only in this specific constructional control system of Figure 7, but in any control system according to the present invention, the input or reference signal need not accurately represent the desired signal entering the plant. By adaption of the filter coefficients of the adaptive response filter the correct amplitude and phase for the control signal can be achieved.
Abstract
Description
Claims
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP52503595A JP3646809B2 (en) | 1994-03-25 | 1995-03-24 | Time domain adaptive control system |
EP95912352A EP0752124A1 (en) | 1994-03-25 | 1995-03-24 | Time domain adaptive control system |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
GB9405953.2 | 1994-03-25 | ||
GB9405953A GB2287851A (en) | 1994-03-25 | 1994-03-25 | Time domain adaptive control system for active noise cancellation |
Publications (1)
Publication Number | Publication Date |
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WO1995026521A1 true WO1995026521A1 (en) | 1995-10-05 |
Family
ID=10752507
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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PCT/GB1995/000666 WO1995026521A1 (en) | 1994-03-25 | 1995-03-24 | Time domain adaptive control system |
Country Status (4)
Country | Link |
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EP (1) | EP0752124A1 (en) |
JP (1) | JP3646809B2 (en) |
GB (1) | GB2287851A (en) |
WO (1) | WO1995026521A1 (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE19632230A1 (en) * | 1996-08-09 | 1998-02-12 | Mueller Bbm Gmbh | Adaptive noise and vibration control system for motor vehicle interior |
US9020158B2 (en) | 2008-11-20 | 2015-04-28 | Harman International Industries, Incorporated | Quiet zone control system |
CN109541938A (en) * | 2017-09-22 | 2019-03-29 | 中国科学院理化技术研究所 | A kind of double sound source driving thermoacoustic system of adaptive active feedforward control |
Families Citing this family (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB2308898B (en) * | 1996-01-02 | 1999-06-23 | Wolfgang Johannes Hube Klippel | Adaptive nonlinear controller for an electromechanic or an electroacoustic system |
US5926405A (en) * | 1996-06-24 | 1999-07-20 | Lucent Technologies, Inc. | Multidimensional adaptive system |
US20030112981A1 (en) * | 2001-12-17 | 2003-06-19 | Siemens Vdo Automotive, Inc. | Active noise control with on-line-filtered C modeling |
US7450725B2 (en) * | 2001-12-17 | 2008-11-11 | Mahle International Gmbh | Digital filter modeling for active noise cancellation |
US8320581B2 (en) | 2010-03-03 | 2012-11-27 | Bose Corporation | Vehicle engine sound enhancement |
US9299337B2 (en) | 2011-01-11 | 2016-03-29 | Bose Corporation | Vehicle engine sound enhancement |
JP2018142210A (en) * | 2017-02-28 | 2018-09-13 | 株式会社デンソー | Signal processing system |
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US4715559A (en) * | 1986-05-15 | 1987-12-29 | Fuller Christopher R | Apparatus and method for global noise reduction |
WO1988002912A1 (en) * | 1986-10-07 | 1988-04-21 | Adaptive Control Limited | Active vibration control |
US4947356A (en) * | 1986-06-23 | 1990-08-07 | The Secretary Of State For Trade And Industry In Her Britannic Majesty's Government Of The United Kingdom Of Great Britain And Northern Ireland | Aircraft cabin noise control apparatus |
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US5251262A (en) * | 1990-06-29 | 1993-10-05 | Kabushiki Kaisha Toshiba | Adaptive active noise cancellation apparatus |
GB2271908B (en) * | 1992-10-21 | 1996-05-15 | Lotus Car | Adaptive control system |
-
1994
- 1994-03-25 GB GB9405953A patent/GB2287851A/en not_active Withdrawn
-
1995
- 1995-03-24 EP EP95912352A patent/EP0752124A1/en not_active Withdrawn
- 1995-03-24 JP JP52503595A patent/JP3646809B2/en not_active Expired - Fee Related
- 1995-03-24 WO PCT/GB1995/000666 patent/WO1995026521A1/en not_active Application Discontinuation
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4715559A (en) * | 1986-05-15 | 1987-12-29 | Fuller Christopher R | Apparatus and method for global noise reduction |
US4947356A (en) * | 1986-06-23 | 1990-08-07 | The Secretary Of State For Trade And Industry In Her Britannic Majesty's Government Of The United Kingdom Of Great Britain And Northern Ireland | Aircraft cabin noise control apparatus |
WO1988002912A1 (en) * | 1986-10-07 | 1988-04-21 | Adaptive Control Limited | Active vibration control |
Non-Patent Citations (1)
Title |
---|
DATABASE INSPEC, no. 93:4492759 IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, vol. 1, no. 2, April 1993 S.LAUGESEN et al. "Multi- channel active control of random noise in a small reverberant room" pages 241-249, * |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE19632230A1 (en) * | 1996-08-09 | 1998-02-12 | Mueller Bbm Gmbh | Adaptive noise and vibration control system for motor vehicle interior |
DE19632230C2 (en) * | 1996-08-09 | 1999-12-16 | Mueller Bbm Gmbh | Adaptive control for active noise reduction, use and procedures |
US9020158B2 (en) | 2008-11-20 | 2015-04-28 | Harman International Industries, Incorporated | Quiet zone control system |
CN109541938A (en) * | 2017-09-22 | 2019-03-29 | 中国科学院理化技术研究所 | A kind of double sound source driving thermoacoustic system of adaptive active feedforward control |
Also Published As
Publication number | Publication date |
---|---|
JPH09511081A (en) | 1997-11-04 |
GB2287851A (en) | 1995-09-27 |
EP0752124A1 (en) | 1997-01-08 |
JP3646809B2 (en) | 2005-05-11 |
GB9405953D0 (en) | 1994-05-11 |
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