ADAPTIVE CONTROL SYSTEM

Abstract: An adaptive control system for reducing undesired signals comprises sensors (31) to provide signals indicative of the undesired signals, and a processor (36) which processes the first signal to provide a secondary signal for output to sources (37) to interfere with the undesired signals. Sensors (42) are provided to detect the residual signals which are indicative of the interference between the undesired and secondary signals. Within the processor the signals indicative of the undesired signals and the residual signals are transformed into the frequency domain and collated. The outcome of the collation is inverse transformed and the processor adjusts the secondary signal using this inverse transform to reduce the residual signal from the sensors (42).

Description: ADAPTIVE CONTROL SYSTEM The present invention relates to an adaptive control system and method for reducing undesired primary signals generated by a primary source of signals. The basic principle of adaptive control is to monitor the primary signals and produce a cancelling signal which interfers destructively with the primary signals in order to reduce them. The degree of success in cancelling the primary signals is measured to adapt the cancelling si r gnal to increase the reduction in the undesired primary signals. This idea is thus applicable to any signals such as electrical signals within an electrical circuit in which undesired noise is produced. One particular area which uses such adaptive control is in the reduction of unwanted acoustic vibrations in a region. It is to be understood that the term "acoustic vibration" applies to any acoustic vibration including sound. There has been much work performed in this area with a view to providing a control system which can adapt quickly to changes in amplitude and frequency of vibrations from a source. Prior art adaptive control systems either operate in the time or frequency domain on the drive signal to be output to cancel the noise. A time domain system is disclosed in O88/02912. In this document a controller is disclosed which is implemented as a digital adaptive finite impulse response (FIR) filter. In order for the filter to be adapted the filter coefficents must be modified based on the degree of success in cancelling the undesired vibrations. For such a control system disclosed in this document, where there are a large number of error signals, drive signals and reference signals, there are a large number of calculations which must be performed for each update of the coefficients. For instance, an estimate of the response of each sensor to each drive signal (the C filter) must be taken into consideration in the calculation of the update of the filter coefficients. WO88/02912 also discloses the operation of a digital filter in the frequency domain. Such a filter has complex filter coefficients and requires the reference signal and error signals to be transformed into the frequency domain and the output drive signal from the adaptive filter to be inverse transformed back to the time domain in order to provide the drive signal. . The transform which is conveniently used is the Fourier transform. In order for such a transform to be performed a number of data points within a window length are transformed and used to adapt the following window of data. Such a discrete Fourier transform provides good control if the length of the window (or number of data points) is long, but this provides a long delay in the update. A short window of data on the other hand provides for a quick adaption but poor control. It is an object of the present invention to provide an adaptive control system which is computationally efficient compared with time domain adaptive control systems and which overcomes the problems associated with frequency domain adaptive control systems. The present invention provides an adaptive control system for reducing undesired signals, comprising signal means to provide at least one first signal indicative of at least selected undesired signals; processing means adapted to use said at least one first signal to provide at least one secondary signal to interfere with the undesired signals; and residual means to provide for said processing means at least one residual signal indicative of the interference between said undesired and secondary signals; wherein said processing means is adapted to transform said at least one first signal and said at least one residual signal to provide the amplitude and phase of spectral components of said signals, to collate the transformed signals, to inverse transform of the outcome of said collation, and to adjust the or each secondary signal using the inverse transform of the outcome of the collation to reduce said at least one residual signal. Preferably said processing means comprises adaptive response filter means having filter coefficients and adapted to adjust the or each secondary signal using said filter coefficients to reduce the or each residual signals, and to modify the filter coefficients using said inverse transform of the outcome of the collation. Also preferably said processing means is adapted to collate said transformed signals by forming at least one cross spectral estimate, to inverse transform said at least one cross spectral estimate, to form at least one cross correlation estimate and to modify the filter coefficients of said adaptive response filter using said at least one cross correlation estimate. Preferably the processing means is adapted to digitally sample said at least one first signal and said at least one residual signal, and to store a plurality of digits for each said signal to form first signal data blocks and residual signal data blocks respectively, said first signal data blocks and said residual signal data blocks being time aligned; said processing means being further adapted to set a number of said digits at the end of each first signal data block to zero to form a modified first signal data block, and to transform the modified first signal data block and the associated residual signal data block to use in the collation. Preferably the number of digits at the end of each modified first signal data block which is set to zero depend on the delay between the first signal and the contribution from the first signal in the residual signal. The number of digits set to zero are preferably selected such that the time taken to sample said number is greater than the delay experienced by a signal passing through said adaptive response filter. Preferably the cross spectral estimate is formed by multiplying the complex conjugate of the transform of the first signal with the transform of the residual signal. Preferably the transform performed on the first signal and the residual signal is the Fourier transform although any transform could be used in which the cross talk between frequencies is minimal or non-existent. In order to control the stability of the adaptive control, preferably the cross spectral estimate is multiplied with a convergence coefficient which is sufficiently small to smooth out the effect of random errors in the cross spectral estimate on the adaption. Alternatively the cross correlation estimate is multiplied with a convergence coefficient sufficiently small to smooth out the effect of random errors in the cross correlation estimate on the adaption. In one embodiment of the present invention the processing means includes system response filter means to model the response of the signals from said residual means to at least one secondary signal. In this embodiment said system response filter means preferably comprises complex filter coefficients which are an estimate of the frequency response of said residual signals to at least one said secondary signals, and said processing means is adapted to filter the transform of said at least one first signal using said complex filter coefficients. In an alternative embodiment of the present invention the processing means includes system response filter means which comprises complex filter coefficients which are an estimate of the amplitude and an estimate of the inverse of the phase of the frequency response of said residual signals to at least one secondary signal, and said processing means is adapted to filter the transform of said at least one residual signal using said complex filter coefficients. In another embodiment of the present invention the processing means is adapted to modify said filter coefficients to reduce the amplitude of portions of the or each drive signal by a predetermined amount. This action on the filter coefficients can be termed "effort weighting" and is used to control the stability of the adaptive response filter. Preferably said residual means provides a plurality of residual signals and said processing means is adapted to modify said filter coefficients of said adaptive response filter to reduce the sum of the mean of the square of the residual signals. In one embodiment wherein said undesired signals comprise undesired acoustic vibrations, said adaptive control system comprises at least one secondary vibration source responsive to said at least one secondary signal to provide secondary vibrations to interfere with said undesired acoustic vibrations; said residual means comprising at least one sensor means to sense the residual vibrations resulting from the interference between said undesired acoustic vibrations and said secondary vibrations and to provide said at least one residual signal. The present invention also provides a method of actively reducing undesired signals, comprising the steps of providing at least one signal indicative of at least selected undesired signals using said at least one first signal to provide at least one secondary signal to interfere with said undesired signals; providing at least one residual signal indicative of the interference between said undesired and secondary signals; transforming said at least one first signal and said at least one residual signal to provide the amplitude and phase of spectral components of said signals, collating the transformed signals; inverse transforming the outcome of the collation and using the inverse transform of the output of the collation to adapt the or each secondary signal to reduce the residual signals. In another aspect the present invention provides an adaptive control system for reducing undesired signals, comprising signal means to provide at least one first signal indicative of at least selected undesired signals; processing means adapted to use said at least one first signal to provide at least one secondary signal to interfere with the undesired signals; and residual means to provide for said processing means at least one residual signal indicative of the interference between said undesired and secondary signals; wherein said processing means is adapted to digitally sample said at least one first signal and said at least one residual signal; to store a plurality of digits for each said signal to form first signal and residual signal data blocks respectively, said first signal data blocks and said residual signal data blocks being time aligned; to set a number of said digits at the end of each first signal data block to zero to form a modified first signal data block, to transform the modified first signal data block and the residual signal data block to provide the amplitude and phase of spectral components of said signals, and to adjust the amplitude and phase of spectral components of said at least one secondary signal using said transformed signals to reduce said at least one residual signal. In a further aspect the present invention provides a method of actively reducing undesired signals comprising the steps of providing at least one first signal indicative of at least selected undesired signals; using said at least one first signal to provide at least one secondary signal to interfere with said undesired signals; providing at least one residual signal indicative of the interference between said undesired and secondary signals; digitally sampling said at least one first signal and said at least one residual signal; storing a plurality of digits for each said signal to form first signal and residual signal data blocks, said first signal and residual signal data blocks being time aligned; setting a number of said digits at the end of each first signal data block to zero to form a modified first signal data block, transforming the modified first signal data block to provide the amplitude and phase of spectral components of said signals, and adjusting the amplitude and phase of spectral components of said at least one secondary signal using said transformed signals to reduce said at least one residual signal. Examples of the present invention will now be described with reference to the drawings, in which:- Figures la and lb illustrate schematically alternative adaptive control systems according to embodiments of the present invention; Figure lc illustrates an expansion of the arrangement shown in Figure la for two reference signals; Figure Id illustrates an expansion of the arrangement shown in Figure la for two error sensors; and Figure le illustrates an expansion of the arrangement shown in Figure la for two secondary vibration sources; Figure 2 illustrates the blocks of reference and error signal data used for the transform to form the cross spectral estimate; Figure 3 is a schematic drawing of an active vibration control system for practical implementation; and Figures 4a and 4b illustrate schematically frequency domain adaptive control systems in accordance with embodiments of the present invention. Referring now to the drawings, la and lb illustrate alternative adaptive control systems which can be used in accordance with the present invention. Both Figures la and lb illustrate a single channel system having a single reference signal x(n) which represents the signal from a sensor, and a single output y(n) from the w filter which represents the drive signal to a secondary vibration source. e(n) represents the error signal indicative of the residual vibrations after interference between the primary and secondary vibrations. The single channel system is shown for simplicity although the present invention is equally applicable to the multichannel system where the Fourier transform of each reference signal x(n) must be taken as well as the Fourier transform of each error signal e(n) . In Figures la and lb, A represents the acoustic response of the pathway from the primary source of vibrations (represented by the reference signal x(n)) and the location of interference with the drive signal (y(n) from the adaptive filter w) . The reference signal x(n) is input into the adaptive response filter w and this signal is modified by filter coefficients of the w filter to provide the drive signal y(n) . In order to compensate for the acoustic response of the sensor to the output of the secondary vibration source (termed C) in the conventional time domain adaptive control system an estimate of C is used to modify the reference signal x(n) before it is input into the LMS algorithm. The C coefficients provide a model of the delay and reverberant response of the system. For a multichannel system with m secondary vibration sources and £ sensors, the coefficients of the adaptive response filter w should be adjusted at every sample in the time domain according to the following equation: w mi (n+l) = Hmi(n) + μ ∑ e (n)r . (n-i) £=1 x~~ where μ is a convergence coefficient e^(n) is the sampled output from the £ sensor r m (n) is a sequence formed by filtering the reference signal x(n) by C which models the response of the £ sensor to the output of the m secondary vibration source. This requires each reference signal to be filtered by a filter which has coefficients for all the paths between the secondary vibrations sources and the sensors. In the single channel embodiment shown in Figures la and lb, the update required for the w coefficients is determined in the frequency domain and implemented in the time domain. This is achieved by taking the Fourier transform of the reference signal x(n) and the error signal e(n). The Fourier transform of the error signal E v is then convolved with the complex conjugate of the Fourier transform of the reference signal X. to form a cross spectral estimate. The inverse Fourier transform of this cross spectral estimate is then taken to form a cross correlation estimate. The causal part of the cross correlation estimate is then used to update the coefficients of the adaptive response filter w. In the above no consideration has been given to compensating for the response of the sensors to the secondary vibration sources. Figures la and lb show alternative methods for doing this. In Figure la the Fourier transform E. of the error signal e(n) is multiplied by the complex conjugate of an estimate of the complex transfer function C for the k iteration. The result of this operation is then multiplied by the complex conjugate of the Fourier transform of the reference signal X. to form the cross spectral estimate. Thus the update algorithm for the adaptive control system shown in Figure la can be given by the following equation: w(n+l) = w(n) - μ IFFT [X H (C H E k ) ] k where μ is a convergence coefficient, X. represents a vector of complex values of the Fourier transform of the reference signal x(n) at the k iteration E^ represents a matrix of complex values of the Fourier transform of the error signals e(n) at the k iteration C represents the matrix of transfer functions H denotes the complex conjugate of the matrix IFFT denotes the inverse fast Fourier transform of the term in the brackets. The convergence coefficient is provided to increase the stability of the adaptive control system and it is sufficiently small to smooth out the effect of random errors in the cross spectral estimate on the adaption. Although in the above algorithm the convergence coefficient is multiplied by the cross correlation estimate, the convergence coefficient may equally be multiplied by the cross spectral estimate and the algorithm is given by: w(n+l) = w(n) - IFFT [μ X H (C H E k ) ] k In the above equations the C matrix contains the transfer functions or a model of the amplitude and phase change applied to each drive signal as detected by each sensor, whereas the conjugate of the C matrix represents a model of the amplitude and the inverse of the phase. Thus in the active vibration control system illustrated in Figure la there are three Fourier transform operations to be undertaken for the update data and the transform E. of each error signal must be multiplied by the conjugate of the transfer functions C for each path from a secondary vibration source to an error sensor. The time taken for the calculations in the arrangement shown in Figure la are approximately proportional to (log_N x N) x (No. of error sensors x No. of secondary vibration sources) . If this is compared with the computational time of the conventional time domain algorithm which is approximately proportional to N 2 x (No. of references x No. of error sensors x No. of secondary vibration sources) , it can be seen that even for a single channel system the control system shown in Figure la is more computationally efficient for an adaptive response filter w having a number of taps of about 64 or greater. The computation of the cross correlation estimate by firstly calculating the cross spectral estimate reduces the number of calculation steps required since the formation of the cross correlation estimate in the time domain requires the convolving of the reference and error signals, whereas in the frequency domain the formation of the cross spectral estimate can be achieved merely by multiplying the functions. Where advantages of the control system of Figure 1 are fully utilised is in a multichannel system where a number of reference signals, a number of secondary vibration sources and a number of error sensors are provided. For the control system shown in Figure la, each of the reference signals does not have to be filtered by a model of the sensor responses to the secondary vibration sources. This reduction in computation is in addition to the computational saving discussed above for the single channel system. Figure lb illustrates an alternative active vibration control system according to one embodiment of the present invention. In this arrangement the only difference is in the position of the estimate of C. Instead of multiplying the Fourier transform of the error signal by the complex conjugate of C, the Fourier transform of the reference signal is multiplied by the matrix of transfer functions C. The cross spectral estimate is then formed by taking the complex conjugate of the result of passing the Fourier transform of the reference signal through the C filter and multiplying this complex conjugate with the Fourier transform of the error signal. The algorithm is given by: w(n+l) = w(n) - μ IFFT [(CX k ) H Ik] As for the arrangement shown in Figure la, the cross correlation estimate is multiplied by a convergence coefficient μ in order to compensate for random errors. In a like manner to that shown in Figure la the cross spectral estimate can alternatively be multiplied with the convergence coefficient and then the algorithm is given by: w(n+l) = w(n) - IFFT [μ (CX k ) H E k ] For the arrangement shown in Figure lb, the computational efficiency for the single channel system is the same as that of the arrangement shown in Figure la. This control system also benefits from forming the cross correlation estimate by firstly forming the cross spectral estimate. When there is a single reference signal and a number of secondary vibration sources and error sensors, the arrangement shown in Figure lb is equally as computationally efficient as the arrangement shown in Figure la. However, when more than one reference signal is used the computational efficiency of the arrangement shown in lb compared to the arrangement shown in Figure la decreases since it is approximately proportional to (log 2 N x N) x (No. of references x No. of error sensors x No. of secondary vibration sources) . The number of filtering operations that must be carried out by the transfer function C is increased by a factor which is the number of reference signals. Figures lc, Id and le illustrate three control systems with 1) two reference signals, one secondary vibration source and one error sensor, 2) one reference signal, one secondary vibration source and two error sensors, and 3) one reference signal, two secondary vibration sources and one error sensor. These three drawings illustrate how a multichannel system with a number of references, secondary vibration sources and error sensors provide a complex system with a matrix C of transfer functions, a number of Fourier transformed reference signals X. , and a number of Fourier transformed error signals E.. The arrangements shown in Figures lc, d and e are multichannel versions of the single channel system shown in Figure la. A multichannel system of the model shown in Figure lb can be built up in a like manner to that shown in Figures lc, d and e as would be evident to a skilled person in the art. In the multichannel system with a number of error sensors the algorithm reduces the noise by reducing the sum of the mean of the square of the error signals in a similar manner to that disclosed in WO88/02912. In addition to the modification of the filter coefficients to reduce the sum of the mean of the square of the error signals, the filter coefficients can be modified to reduce the amplitude of portions of the drive signals by a predetermined amount. This is termed "effort weighting" and can increase the stability of the algorithm as well as allow for selection of the effort taken to converge for signals of different delays or different frequencies dependent upon whether the filter coefficients are weighted in the time or frequency domain. So far in considering the way in which the algorithm works, no consideration has been given to the practical considerations of taking the Fourier transform of the continuous reference signal x(n) and error signal e(n) . In order to perform a discrete fast Fourier transform a block or window of data must be stored and operated on. The number of data points which are required must at least correspond to the delay associated with the adaptive response filter w since for a reference signal x(n) the effect on it by the w filter presented in the error signal e(n) must be present. If the block of reference data has a number n of data points for operation on by the Fourier transform then the n data point will have a contribution in the error signal e(n) which is delayed by the length of the w filter. Thus if a time aligned window of error data e(n) was taken, the delayed contributions from the n data point in the reference signal would not be measured. This reduces the possibility of convergence of the algorithm. This problem is overcome by taking a block or window of data having n data points where the last few p data points are set to zero. Thus the block of data has a length of 0 to n but only the data points 0 to n-p contain actual reference signal data. The number p of data points which are set to zero is dependent on the number of tap delays of the w filter. The number p should be set to be at least the same number if not greater than the number of taps in the w filter. Using this method assures that all contributions from the reference signal data point x(n-p) are contained within the error signal data block e(n) for the two time aligned blocks of data. Figure 2 illustrates the two data blocks for the reference and error signals. These blocks of data are used for the fast Fourier transform and this method ensures that all contributions from the reference signal data points are found in the error signal data block. The data blocks or windows represent "snap shots" in time of the reference and error signals. There is no requirement for these data blocks to be taken end to end. Blocks of data can be taken at intervals of time. If the intervals between the acquisition of the data blocks is large then clearly the adaption of the coefficients of the w filter will be slow in response to rapidly changing conditions. However, for many practical applications the update of the coefficients of the w filter need not take place rapidly. Thus because the adaption of the reference signal by the w filter coefficients takes place in the time domain, the output drive signals to provide the secondary vibrations are not delayed. Only the modification of the filter coefficients of the w filter are delayed. So far only the method of operation of the algorithm has been considered. Figure 3 illustrates the construction of a practical active vibration control system for use in a motor vehicle. Figure 3 illustrates a multichannel system with four reference signal generators 31, four error sensors 42 and two secondary vibration sources 37. As mentioned hereinabove the present invention is particularly suited to a multichannel system having more than one reference signal since this provides for the greatest computational saving. In the arrangement shown in Figure 3 the reference signal generators 31 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 are amplified by the amplifiers 32 and low pass filtered by the filters 33 in order to avoid aliasing. The reference signals are then multiplexed by the multiplexer 34 and digitised using the analogue digital converter 35. This provides reference signals x.(n) to the processor 36 which is provided with memory 61. Four error sensors 42 are provided within the vehicle cabin at space locations such as around the headlining. These microphones 42. through 42. detect the noise within the cabin. The output of the microphones 42 is then amplified by the amplifiers 43 and low pass filtered by the low pass filters 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 digital converter e, (n) is then input into the processor 36. Drive signals y_(n) are output from the processor 36 and converted to an analogue signal by the digital to analogue converter 41. The output of the analogue to digital converter 41 is then demultiplexed by the demultiplexer 38. The demultiplexer 38 separates the drive signals into separate drive signals for passage through low pass filters 39 in order to remove high frequency digital sampling noise. The signal is then amplified by the amplifiers 40 and output to the secondary vibration sources 37- and 37 which comprise loudspeakers provided within the cabin of the vehicle. Conveniently, the loudspeakers can comprise the loudspeakers of the in-car entertainment system of the vehicle. In such an arrangement the drive signals are mixed with the in-car entertainment signals for output by the loudspeakers, as is disclosed in GB 2252657. Thus the processor is provided with the reference signals x^n) and the error signals e, (n) and outputs the drive signals Y m (n) and is adapted to perform the algorithm as hereinbefore described. Although in Figure 3 the analogue to digital converters 35 and 46 and the digital to analogue converter 41 are shown separately, such can be provided by a single chip. Figure 3 also 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 only 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. Although the arrangement shown in Figure 3 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 although the full potential computational saving of the algorithm is not utilised, the computational requirement is still reduced compared to the conventional time domain algorithm. Further, although the secondary vibration sources illustrated in Figure 3 are loudspeakers they could alternatively be vibrators or a mix of both. Figures 4a and 4b illustrate other embodiments of the present invention. In these embodiments adaption is performed in the frequency domain. Figures 4a and 4b differ from Figures la and lb in that the w filter coefficients are complex and require the input to the w filter to be the transform of the reference signal. Also, there is no need to inverse transform the cross spectral estimate to modify the complex filter coefficients. The output of the w filter must be inverse transformed to generate the drive signal y(n) since the w filter acts on the amplitude and phase of spectral components. For the arrangement in Figure 4a the algorithm can be given by w k + l = k - μ XH (£ H lk ) k whereas for Figure 4b the algorithm can be given by w k+1 = w k - μ(CX k )H E k As for the arrangements shown in Figures la and lb in order to avoid the problem of the window of error data not containing the contribution from the reference data in a time aligned window, the latter part of the error data block is zeroed in the manner described with respect to Figure 2 with all the associated advantages. Although the embodiments of the invention described hereinabove have been described with reference to an active vibration control system the present invention is not limited thereto. The present invention applies to the reduction of any undesired signals. A signal indicative of at least selected undesired vibrations from a vibration source is used to provide a drive signal to cancel the undesired vibrations at a location. The degree of success in reducing the undesired vibrations is measured to provide a residual signal and this is used to adjust the drive signal to provide better cancellation. Thus the undesired signals being cancelled could be electrical or acoustic.

Claims: CLAIMS 1. An adaptive control system for reducing undesired signals, comprising signal means to provide at least one first signal indicative of at least selected undesired signals; processing means adapted to use said at least one first signal to provide at least one secondary signal to interfere with the undesired signals; and residual means to provide for said processing means at least one residual signal indicative of the interference between said undesired and secondary signals; wherein said processing means is adapted to transform said at least one first signal and said at least one residual signal to provide the amplitude and phase of spectral components of said signals, to collate the transformed signals, to inverse transform of the outcome of said collation, and to adjust the or each secondary signal using the inverse transform of the outcome of the collation to reduce said at least one residual signal. 2. An adaptive control system as claimed in Claim 1, wherein said processing means comprises adaptive response filter means having filter coefficients and adapted to adjust the or each secondary signal using said filter coefficients to reduce the or each residual signal, and to modify the filter coefficients using said inverse transform of the outcome of the collation. 3. An adaptive control system as claimed in Claim 2, wherein said processing means is adapted to collate said transformed signals by forming at least one cross spectral estimate, to inverse transform said at least one cross spectral estimate to form at least one cross correlation estimate and to modify the filter coefficients of said adaptive response filter means using said at least one cross correlation estimate. 4. An adaptive control system as claimed in Claim 2 or Claim 3, wherein said processing means is adapted to digitally sample said at least one first signal and said at least one residual signal, and to store a plurality of digits for each signal to form first signal and residual signal data blocks respectively, said first signal data blocks and said residual signal data blocks being time aligned; said processing means being further adapted to set a number of said digits at the end of each first signal data block to zero to form a modified first signal data block, and to transform the modified first signal data block and the time associated residual signal data block to use in the collation. 5. An adaptive control system as claimed in Claim 4, wherein said processing means is adapted to set the number of said digits at the end of each modified first signal data block to zero in depencence upon the delay between the first signal and the contribution from the first signal in the residual signal. 6. An adaptive control system as claimed in Claim 5, wherein said processing means is adapted to select the number of digits to set to zero such that the time taken to sample said number is greater than the delay experienced by a signal passing through said adaptive response filter. 7. An adaptive control system as claimed in any one of Claims 3 to 6, wherein said processing means is adapted to form the cross spectral estimate by multiplying the complex conjugate of the transform of the first signal with the transform of the residual signal. 8. An adaptive control system as claimed in any one of Claims 3 to 7, wherein said processing means is adapted to multiply said at least one cross spectral estimate with a convergence coefficient sufficiently small to smooth out the effect of random errors in the cross spectral estimate on the adjustment of the or each secondary signal. 9. An adaptive control system as claimed in any one of Claims 3 to 7, wherein said processing means is adapted to multiply said at least one cross correlation estimate with a convergence coefficient sufficiently small to smooth out the effect of random errors, in the cross correlation estimate on the adjustment of the or each secondary signal. 10. An adaptive control system as claimed in any one of Claims 2 to 9, wherein said processing means further includes system response filter means to model the signals from said residual means in response to at least one secondary signal. 11. An adaptive control system as claimed in Claim 10, wherein said system response filter means comprises complex filter coefficients which are an estimate of the frequency response of the signals from said residual means to at least one said secondaary signal, and said processing means is adapted to filter the transform of said at least one first signal using said complex filter coefficients. 12. An adaptive control system as claimed in any one of Claims 2 to 9, wherein said processing means further includes system response filter means comprising complex filter coefficients which are an estimate of the amplitude and an estimate of the inverse of the phase of the frequency response of the signals from said residual means to at least one said secondary signal, and said processing means is adapted to filter the transform of said at least one residual signal using said complex filter coefficients 13. An adaptive control system as claimed in any one of Claims 3 to 12, wherein said processing means is adapted to modify said filter coefficients to reduce the amplitude of portions of the or each secondary signal by a predetermined amount. 14. An adaptive control system as claimed in any one of Claims 3 to 13, wherein said residual means provides a plurality of residual signals, and said processing means is adapted to modify said filter coefficients of said adaptive response filter to reduce the sum of the mean of the square of the residual signals. 15. An adaptive control system as claimed in any preceding claim, wherein said undesired signals comprise undesired acoustic vibrations, said adaptive control system comprising at least one secondary vibration source responsive to said at least one secondary signal to provide secondary vibrations to interfere with said undesired acoustic vibrations; said residual means comprising at least one sensor means to sense the residual vibrations resulting from the interference between said undesired acoustic vibrations and said secondary vibrations and to provide said at least one residual signal. 16. A method of actively reducing undesired signals comprising the steps of providing at least one first signal indicative of at least selected undesired signals using said at least one first signal to provide at least one secondary signal to interfere with paid undesired signals; providing at least one residual signal indicative of the interference between said undesired and secondary signals; transforming said at least one first signal and said at least one residual signal to provide the amplitude and phase of spectral components of said signals, collating the transformed signals; inverse transforming the outcome of the collation, and using the inverse transform of the output of the collation to adapt the or each secondary signal to reduce the sensed residual signals. 17. A method as claimed in Claim 16, wherein said at lest one secondary signal is adjusted using filter coefficients in an adaptive response filter means to reduce the residual signals, and the filter coefficients are modified using said inverse transform of the outcome of the collation. 18. A method as claimed in Claim 16, wherein the transformed signals are compared by forming at least one cross spectral estimate, and said at least one cross spectral estimate is inverse transformed to form at least one cross correlation estimate, said filter coefficients being modified using said at least one cross correlation estimate. 19. A method as claimed in any of Claims 16 to 18, wherein said at least one first signal and said at least one residual signal are digitally sampled, including the steps of storing a plurality of digits for each said signal to form first signal data blocks and residual signal data blocks respectively, said first signal data blocks and residual signal data blocks being time aligned; setting a number of said digits at the end of each first signal data block to zero to form a modified first signal data block; and transforming the modified first signal block and the time associated residual signal data block for use in the collation. 20. A method as claimed in Claim 19, wherein the number of digits at the end of each modified first signal data block which is set to zero is dependent upon the delay between the first signal and the contribution from the first signal in the residual signal. 21. A method as claimed in Claim 20, wherein the number of digits set to zero is determined such that the time taken to sample said number is greater than the delay experienced by a signal during adjustment of the or each secondary signal. 22. A method as claimed in any one of Claims 18 to 21, wherein the cross spectral estimate is formed by multiplying the complex conjugate of the transform of the first signal with the transform of the residual signal. 23. A method as claimed in any one of Claims 18 to 22, wherein the cross spectral estimate is multiplied with a convergence coefficient sufficiently small to smooth out the effect of random errors in the cross spectral estimate on the adjustment of the or each secondary signal. 24. A method as claimed in any one of Claims 18 to 22, wherein the cross correlation estimate is multiplied with a convergence coefficient sufficiently small to smooth out the effects of random errors in the cross correlation estimate on the adjustment of the or each secondary signal. 25. A method as claimed in any one of Claims 18 to 24, wherein the response of said at least one residual signal to said at least one secondary signal is modelled by system response filter means. 26. A method as claimed in Claim 25, wherein said system response filter means has complex filter coefficients which are an estimate of the frequency response of said at least one residual signal to at least one said secondary signal, said method including the steps of multiplying the said transform of said at least one first signal with said complex filter coefficients. 27. A method as claimed in any one of Claims 18 to 25 wherein system response filter means comprises complex filter coefficients which are an estimate of the amplitude and an estimate of the inverse of the phase of the frequency response of said sensed residual vibration to said at least one secondary signal, said method including the step of multiplying the transform of said at least one residual signal with said complex filter coefficients. 28. A method as claimed in any one of Claims 18 to 27 including the step of modifying said filter coefficients to reduce the amplitude of portions of the or each secondary signal by a predetermined amount. 29. A method as claimed in any of Claims 18 to 28 including the steps of sensing residual signals in a plurality of locations to provide a plurality of residual signals and modifying said filter coefficients to reduce the sum of the square of the residual signals. 30. A method as claimed in any of Claims 16 to 29, wherein said undesired signals comprise undesired acoustic vibrations, the method comprising the steps of providing at least one secondary vibration from said at least one secondary signal for interference with said undesired vibration and sensing the residual vibrations resulting from the interference between said undesired and secondary vibrations to provide said residual signal. 31. An adaptive control system for reducing undesired signals, comprising signal means to provide at least one first signal indicative of at least selected undesired signals; processing means adapted to use said at least one first signal to provide at least one secondary signal to interfere with the undesired signals; and residual means to provide for said processing means at least one residual signal indicative of the interference between said undesired and secondary signals; wherein said processing means is adapted to digitally sample said at least one first signal and said at least one residual signal, to store a plurality of digits for each said signal to form first signal and residual signal data blocks respectively, said first signal data blocks and said residual signal data blocks being time aligned, to set a number of said digits at the end of each first signal data block to zero to form a modified first signal data block, to transform the modified first signal data block and the residual signal data block to provide the amplitude and phase of spectral components of said signals, and to adjust the amplitude and phase of spectral components of said at least one secondary signal using said transformed signals to reduce said at least one residual signal. 32. An adaptive control system as claimed in Claim 31, wherein said processing means comprises adaptive response filter means having complex filter coefficients and adapted to adjust the or each secondary signal using said filter coefficients to reduce the or each residual signals, and to modify the filter coefficients using said transformed signals, said processing means being further operative to inverse transform the adjusted amplitude and phase of the spectral components of said at least one secondary signal to provide said at least one secondary signal. 33. An adaptive control system as claimed in Claim 32, wherein said processing means is adapted to form at least one cross spectral estimate using the transform of said at least one modified first signal data block and said at least one residual signal data block, and to modify the filter coefficients using said at least one cross spectral estimate. 34. An adaptive control system as claimed in any one of Claims 31 to 33, wherein said processing means is adapted to set the number of said digits at the end of each modified first signal data block to zero in dependence upon the delay between the first signal and the contribution from the first signal in the residual signal. 35. An adaptive control system as claimed in Claim 34, wherein said processing means is adapted to select the number of digits to set to zero such that the time taken to sample said number is greater than the delay experienced by a signal passing through said adaptive response filter. 36. An adaptive control system as claimed in Claim 33, wherein said processing means is adapted to form the cross spectral estimate by multiplying the complex conjugate of the transform of the first signal with the transform of the residual signal. 37. An adaptive control system as claimed in Claim 33 or Claim 36, wherein said processing means is adapted to multiply said at least one cross spectral estimate with a convergence coefficient sufficiently small to smooth out the effect of random errors in the cross spectral estimate on the adjustment of the or each secondary signal. 38. An adaptive control system as claimed in Claim 32, wherein said processing means further includes system response filter means to model the signals from said residual means in response to at least one secondary signal. 39. An adaptive control system as claimed in Claim 38, wherein said system response filter means comprises complex filter coefficients which are an estimate of the frequency response of the signals from said residual means to at least one said secondary signal, and said processing means is adapted to filter the transform of said at least one first signal using said complex filter coefficients. 40. An adaptive control system as claimed in Claim 32, wherein said processing means further includes system response filter means comprising complex filter coefficients which are an estimate of the amplitude and an estimate of the inverse of the phase of the frequency response of the signals from said residual means to at least one said secondary signal, and said processing means is adapted to filter the transform of said at least one residual signal using said complex filter coefficients 41. An adaptive control system as claimed in any one of Claims 32 to 40, wherein said processing means is adapted to modify said filter coefficients to reduce the amplitude of portions of the or each secondary signal by a predetermined amount. 42. An adaptive control system as claimed in any one of Claims 32 to 41, wherein said residual means provides a plurality of residual signals, and said processing means is adapted to modify said filter coefficients of said adaptive response filter to reduce the sum of the mean of the square of the residual signals. 43. An adaptive control system as claimed in any one of Claims 31 to 42, wherein said undesired signals comprise undesired acoustic vibrations, said adaptive control system comprising at least one secondary vibration source responsive to said at least one secondary signal to provide secondary vibrations to interfere with said undesired acoustic vibrations; said residual means comprising at least one sensor means to sense the residual vibrations resulting from the interference between said undesired acoustic vibrations and said secondary vibrations and to provide said at least one residual signal. 44. A method of actively reducing undesired signals comprising the steps of providing at least one first signal indicative of at least selected undesired signals; using said at least one first signal to provide at least one secondary signal to interfere with said undesired signals; providing at least one residual signal indicative of the interference between said undesired and secondary signals; digitally sampling said at least one first signal and said at least one residual signal, storing a plurality of digits for each said signal to form first signal and residual signal data blocks, said first signal and residual signal data blocks being time aligned; setting a number of said digits at the end of each first signal data block to zero to form a modified first signal data block, transforming the modified first signal data block to provide the amplitude and phase of spectral components of said signals, and adjusting the amplitude and phase of spectral components of said at least one secondary signal using said transformed signals to reduce said at least one residual signal. 45. A method as claimed in Claim 44, wherein the amplitude and phase of spectral components of said at least one secondary signal is adjusted using complex filter coefficients in an adaptive response filter means; said complex filter coefficients are modified said said transformed signals; and the inverse transform of the adjusted amplitude and phase of spectral components of said at least one secondary signal is taken to provide said at least one secondary signal. 46. A method as claimed in Claim 45, wherein at least one cross spectral estimate is formed using the transform of said at least one modified first signal data block and said at least one residual signal data block; and the filter coefficients are modified using said at least one cross spectral estimate. 47. A method as claimed in any one of Claims 44 to 46, wherein the number of digits at the end of each modified first signal data block which is set to zero is dependent upon the delay between the first signal and the contribution from the first signal in the residual signal. 48. A method as claimed in Claim 47, wherein the number of digits set to zero is determined such that the time taken to sample said number is greater than the delay experienced by a signal during adjustment of the or each secondary signal. 49. A method as claimed in Claim 46, wherein the cross spectral estimate is formed by multiplying the complex conjugate of the transform of the first signal with the transform of the residual signal. 50. A method as claimed in Claim 46, wherein the cross spectral estimate is multiplied with a convergence coefficient sufficiently small to smooth out the effect of random errors in the cross spectral estimate on the adjustment of the or each secondary signal. 51. A method as claimed in Claim 45, wherein the response of said at least one residual signal to said at least one secondary signal is modelled by system response filter means. 52. A method as claimed in Claim 51, wherein said system response filter means has complex filter coefficients which are an estimate of the frequency response of said at least one residual signal to at least one said secondary signal, said method including the steps of multiplying the said transform of said at least one first signal with said complex filter coefficients. 53. A method as claimed in Claim 45, wherein system response filter means comprises complex filter coefficients which are an estimate of the amplitude and an estimate of the inverse of the phase of the frequency response of said sensed residual vibration to said at least one secondary signal, said method including the step of multiplying the transform of said at least one residual signal with said complex filter coefficients. 54. A method as claimed in any one of Claims 44 to 53, including the step of modifying said filter coefficients to reduce the amplitude of portions of the or each secondary signal by a predetermined amount. 55. A method as claimed in Claim 45, including the steps of sensing residual signals in a plurality of locations to provide a plurality of residual signals and modifying said filter coefficients to reduce the sum of the square of the residual signals. 56. A method as claimed in any one of Claims 44 to 55, wherein said undesired signals comprise undesired acoustic vibrations, the method comprising the steps of providing at least one secondayr vibration from said at least one secondayr signal for interference with said undesired vibration and sensing the residual vibrations resulting from the interference between said undesired and secondary vibrations to provide said residual signal. 57. A method substantially as hereinbefore described with reference to the drawings. 58. An adaptive control system substantially as hereinbefore described and as illustrated with reference to the drawings.

Inventors: STOTHERS IAN, STOTHERS, IAN
Applicants: LOTUS CAR, STOTHERS IAN, LOTUS CARS LIMITED
Application year: 1993
Main classification: G10K11
IPC codes: G10K11