CN109859733A - Engine noise control method based on FXLMS algorithm - Google Patents
Engine noise control method based on FXLMS algorithm Download PDFInfo
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Abstract
The present invention aiming at the problem that internal car noise, provides a kind of engine noise control method based on FXLMS algorithm, belongs to field of noise control caused by engine aspirating system.The present invention includes: S1, the master control system model for establishing with FXLMS algorithm engine charge noise, utilizes the reference signal x (k) of the revolving speed construction master control system model of engine；S2, off-line identification structure is established, to secondary channel transfer function H in active control system model2(z) it is recognized, and identification result is supplied to active control system model；S3, engine noise is controlled using the active noise control system model after identification.And propose improved Variable Step Algorithm, the algorithm adds parameter γ by normalization algorithm step-length and the β in sine variable step is replaced to carry out the amplitude range of adjusting step, not only have the advantages that the convergence of sinusoidal Variable Step Algorithm is fast, steady-state error is small, and when being adapted to normalization algorithm varying reference signal characteristic, parameter is easy to the advantages of choosing.
Description
Technical field
The present invention relates to a kind of internal car noise Active Control Method, in particular to a kind of engine based on FXLMS algorithm
Noise control method belongs to field of noise control.
Background technique
Automobile is while bringing convenient, the problems such as also bringing pollution from environmental noise.Internal car noise is for vapour
The influence of the riding comfort and users satisfaction degree of vehicle is very big, and people increasingly pay close attention to internal car noise is reduced.Traditional noise reduction
Method, such as insulates against sound, and vibration isolation, silencer etc. is substantially all low-frequency noise control effect unobvious.Active noise controlling method because
Its low frequency excellent noise reduction effect, and can have specific aim and Objective.Recently as the development of electronic technology, active noise control
Make the extensive concern by researcher and discussion.
Application of the nineties noise impedance technology in cabin receives significant attention, the Lockheed company in the U.S.
Active control is tested in aircraft cabin noise respectively with the P.A.Nelson of Pu Sidun university, Nanan, Britain, is achieved
Preferable drop effect, it was demonstrated that extensive prospect of the noise impedance technology in the cabins such as aircraft, steamer and automobile indoor application.English
Noise impedance technology is applied on automobile by Lotus motor corporation, state, has been significantly reduced the humorous rank of interior low frequency
Noise, the interior roar sound corresponding to the humorous rank noise of engine firing frequencies can reduce 10dB or so.Japanese Nissan Motor is in vehicle
It is equipped with Active noise control system on the automobile that type is Blue Bird, 5~6dB of internal car noise or so can be reduced.Southeast China University
Hu Xiao etc. is proposed using feedback ANC and feedforward ANC is compound realizes active noise controlling in vehicle chamber.Remaining honor equality proposes
Applied to the sinusoidal Variable Step Algorithm of active noise controlling, the defect of LMS algorithm is overcome.Shanghai University Of Engineering Science Feng Tianpei
PmLMS algorithm is proposed using human auditory system rear shelter effect, effectively reduces loudness value in car compartment, car is improved and makes an uproar
Sound quality.Liu Xue is extensively equal to construct interior multiple secondary sound source active noise controlling system based on on-line training dynamic neural network model
System obtains the noise reduction of local space 10-14dB.
The internal car noise active control research carried out in the past is mostly used acoustic sensor and acquires primary Sound source noise signal conduct
Reference signal, this method are also easy to produce acoustic feedback during implementing and controlling to influence the stability of control system.
The thought of active noise controlling is to utilize secondary sound source and noise signal width based on wave interference principle physically
The characteristics of spending equal, opposite in phase, to achieve the purpose that eliminate noise.Adaptive algorithm is the core of control system, regardless of
It is feed-forward type or feedback-type, most of active noise control system is all that (Least mean square, minimum is with LMS
Just) algorithm and its innovatory algorithm adjust controller weight coefficient.Normalized LMS algorithm makes reference signal due to the presence of secondary sound travel
It is not reciprocity in time with error signal, cause active noise control system unstable.As a result, in reference signal and error signal
Between the weights estimation of secondary sound travel transmission function is added, the stability of system can be effectively improved, that is, generate new algorithm --
FXLMS (Filtered-x Least Mean Square, X- filter lowest mean square) algorithm.FXLMS algorithm is to utilize acoustics road
Diameter model is filtered reference signal, updates filter coefficient with the output error of filtered reference signal and system to correct
Controller closely and with the acoustic path of estimation replaces the acoustic path in actual transmissions, can pass through measurement method in FXLMS algorithm
The acoustic transfer function of single-frequency periodic signal is obtained, but engine speed is consecutive variations to automobile in the process of moving, is caused
The frequency of single-frequency periodic signal also and then changes, therefore hardly results in the transmission function of secondary channel.
Summary of the invention
The present invention aiming at the problem that internal car noise, provides a kind of fast convergence rate, stable state caused by engine aspirating system
Error is small and can significantly reduce the engine noise based on FXLMS algorithm of the internal car noise as caused by engine aspirating system
Control method.
Engine noise control method based on FXLMS algorithm of the invention, which comprises
S1, the master control system model that engine charge noise is established with FXLMS algorithm are constructed using the revolving speed of engine
The reference signal x (k) of master control system model；
S2, off-line identification structure is established, to secondary channel transfer function H in active control system model2(z) it is distinguished
Know, and identification result is supplied to active control system model；
S3, engine noise is controlled using the active noise control system model after identification.
Preferably, the master control system model, comprising:
Primary channel transfer function H is passed through in the primary noise source of engine1(z), main channel output d (k) is obtained；
The revolving speed of engine carries out LMS algorithm adaptive-filtering, filtering as reference signal x (k), to reference signal x (k)
Driving signal of the reference signal afterwards as secondary sound source, by secondary channel transfer function H2(z), secondary sound source is obtained accidentally
The output signal s (k) of difference signal sensor；
D (k) and s (k) are overlapped mutually, and are obtained error signal e (k)；Wherein e (k) and reference signal x (k) is used for LMS algorithm
Adaptive-filtering；
The off-line identification structure are as follows:
Noise generator exports white noise x ' (k) to Hs(z) andHsIt (z) is secondary channel transmission function to be measured,For Hs(z) estimation；Hs(z) output d ' (k),Output y ' (k), the difference of d ' (k) and y ' (k) are identification
Error e ' (k) is right using LMS algorithm and Identification Errors e ' (k)Coefficient carry out adaptive adjustment and update；If distinguishing offline
Know structure convergence, when off-line identification structure is run enough for a long time to stable state,After fixation,It can be used as actively
H in noise control system model2(z) estimation
Preferably, the step-length of the FXLMS algorithm is fixed step size.
Preferably, the functional relation of the step size mu (k) Yu error signal e (k) of the FXLMS algorithm are as follows:
Wherein, the speed of parameter alpha control step change, β control the value range of step-length.
Preferably, the relational expression of the step size mu (k) of the FXLMS algorithm and reference signal x (k) are as follows:
Wherein, 0 < μ, 1 < 1.
Preferably, the step size mu (k) of the FXLMS algorithm and reference signal x (k) and the function of error signal e (k) close
System are as follows:
Wherein, parameter alpha is used for trend of the adjusting step amplitude with error signal variations, 0 < μ1< 1,0≤γ≤1.
The beneficial effects of the present invention are the present invention is to reduce car engine air admittance noise, is established and is sent out with FXLMS algorithm
The active control system model of motivation induction noise identifies secondary channel using offline identification method, and uses engine
Tach signal constructs sound source reference signal to avoid interference of the secondary sound source to reference signal, fast convergence rate of the invention, steady
State error is small and can significantly reduce the internal car noise as caused by engine aspirating system.Emulation compares fixed step size algorithm, just
The influence of string Variable Step Algorithm, normalization algorithm to system convergence speed and stability, and improved Variable Step Algorithm is proposed,
The algorithm adds parameter γ by normalization algorithm step-length and the β in sine variable step is replaced to carry out the amplitude range of adjusting step, no
Only have the advantages that the convergence of sinusoidal Variable Step Algorithm is fast, steady-state error is small, and there is normalization algorithm to adapt to time-varying with reference to letter
Number characteristic, parameter is easy to the advantages of choosing.Five grades of even accelerating modes of certain automobile are emulated, the results showed that improved change
Step length algorithm can effectively reduce engine charge noise, and noise reduction can reach 20dB at some frequencies, while it is steady to improve system
It is qualitative.Improved Variable Step Algorithm proposed by the present invention can effectively control car room induction noise, improve car room sound
Quality has important references meaning to practical engineering application.
Detailed description of the invention
Fig. 1 is the schematic illustration for the master control system model that the present invention establishes engine charge noise with FXLMS algorithm；
Fig. 2 is the schematic illustration of off-line identification structure；
The curve that Fig. 3 is convergence time, steady-state error changes with μ；
Fig. 4 (a) is the function relation curve of μ (k) and e (k), simulation parameter α=2, β=0.01,0.02,0.04；
Fig. 4 (b) is the function relation curve of μ (k) and e (k), simulation parameter β=0.01, α=0.5,1,2；
Fig. 5 (a) is error signal time changing curve simulation result, simulation parameter L=20, μ=0.01,0.02；
Fig. 5 (b) is error signal time changing curve simulation result, simulation parameter α=2, β=0.01；
Fig. 6 (a) is that reference signal amplitude doubles error signal time graph, simulation parameter: μ1=0.01, α=2；
Fig. 6 (b) is that reference signal amplitude doubles error signal time graph, simulation parameter: β=0.01, α=2；
Fig. 7 is that improved Variable Step Algorithm error signal changes over time curve；
Fig. 8 is the amplitude frequency curve figure of noise signal and error signal；
Fig. 9 (a) is the reference signal time graph of five grades of automobile even accelerating mode Imitatings；
Fig. 9 (b) is that error signal changes over time curve under five grades of even accelerating modes of automobile.
Specific embodiment
The engine noise control method based on FXLMS algorithm of present embodiment, comprising:
S1, the master control system model that engine charge noise is established with FXLMS algorithm are constructed using the revolving speed of engine
The reference signal x (k) of master control system model；
S2, off-line identification structure is established, to secondary channel transfer function H in active control system model2(z) it is distinguished
Know, and identification result is supplied to active control system model；
S3, engine noise is controlled using the active noise control system model after identification.
Engine aspirating system noise is main interior outer sound field noise source, is influenced on internal car noise especially significant.Into
Gas noise is periodically to be opened and closed by the valve that spouts and generated, and when IO Intake Valve Opens, a pressure pulse is generated in air inlet pipe, with
The movement of piston, this pressure wave be damped quickly；When IC Intake Valve Closes, a pressure pulse is equally generated, and
It is damped and rapidly disappears, in a working cycles, there are two pressure pulses altogether.Two pressure pulses are periodically sent out
It is raw, it is formed periodic noise, frequency is influenced by engine speed, expression are as follows:
In formula, m is overtone order；1,2,3......, n are engine speed；Z is number of cylinders；τ is stroke coefficient；Four
Stroke τ=2；Two-stroke τ=1.
By formula (1) it is found that engine speed not only influences noise frequency, also there is larger impact to noise size.General feelings
Under condition, for same engine, noise is often doubled with the linear regularity of distribution of rotation speed change, revolving speed, and noise level is about
Increase 10dB.When engine speed is 1000r/min, inlet noise grade is 73dB, can be obtained full between noise level and revolving speed
Foot:
According to the definition of sound pressure level SPL, have:
In formula, peFor sound pressure effective value；prefIt is 2 × 10 in air for reference sound pressure-5Pa。
Formula (2) substitutes into formula (3), there is peWith the relational expression of revolving speed n are as follows:
Since formula (1) and formula (4) characterize the quantitative relationship between engine speed and noise frequency, acoustic pressure, this
It is sound source reference signal that embodiment, which selects engine rotational speed signal, realizes system stability and noise control.
In present embodiment, to reduce car engine air admittance noise, engine charge noise is established with FXLMS algorithm
Active control system model identifies secondary channel using offline identification method, and with engine rotational speed signal construction sound
Source reference signal avoids interference of the secondary sound source to reference signal, and fast convergence rate of the invention, steady-state error are small and can show
Writing reduces the internal car noise as caused by engine aspirating system.
As shown in Figure 1, the active noise control system model of present embodiment includes:
The primary noise source P (k) of engine passes through primary channel transfer function H1(z), main channel output d (k) is obtained；
Reference signal x (k) is constructed using the revolving speed n of engine, LMS algorithm is carried out to reference signal x (k) and is adaptively filtered
Wave, driving signal y (k) of the filtered reference signal as secondary sound source, by secondary channel transfer function H2(z), it obtains
Output signal s (k) of the secondary sound source in error-sensing element；
D (k) and s (k) are overlapped mutually, and are obtained error signal e (k)；Wherein e (k) and reference signal x (k) is used for LMS algorithm
Adaptive-filtering.
Present embodiment is using the lateral FIR filter of LMS algorithm as modeling filter, using the method for off-line modeling,
Estimate the transfer function H in Fig. 1 in such cases2(z), as shown in Figure 2.
The off-line identification structure of present embodiment as shown in Figure 2 are as follows:
Noise generator exports white noise x ' (k) to Hs(z) andHsIt (z) is secondary channel transmission function to be measured,For Hs(z) estimation；Hs(z) output d ' (k),Output y ' (k), the difference of d ' (k) and y ' (k) are that identification misses
Poor e ' (k), it is right using LMS algorithm and Identification Errors e ' (k)Coefficient carry out adaptive adjustment and update；If off-line identification
Structure convergence, when off-line identification structure is run enough for a long time to stable state,After fixation,It can be used as and actively make an uproar
H in acoustic control system model2(z) estimation
Present embodiment selects engine rotational speed signal to construct sound source reference signal, imitates to system stability and noise control
Fruit is emulated:
In-line arrangement four-cylinder (z=4) four stroke (τ=2) automobile engine for being 3000r/min based on revolving speed n is emulated, usually
Engine charge noise is lower higher than the higher hamonic wave noise level of 3 ranks to be ignored, therefore present embodiment chooses preceding 3
Order harmonics are as reference signal.According to formula (1), fundamental wave 100Hz, second harmonic 200Hz, three order harmonics are 300Hz；And it is preceding
3 rank sound pressure levels are respectively 90dB, 76dB, 70dB, and can calculate sound pressure effective value Amplitude Ration according to formula (3) is 1:0.159:
0.1, the secondary sound source signal determined by this ratio is motivated after power amplifier based on sound wave and formula (4) of loudspeaker sending
The sound pressure level of calculating is identical, can so offset engine charge noise, and reference signal at this time can be set as:
In formula, f1=100Hz, f2=200Hz, f3=300Hz.
In simulation process, reference signal is superimposed as P (k) noise signal in Fig. 1, then with the random noise that amplitude is 0.1:
P (k)=x (k)+0.1rand (1, N) (6)
If primary and secondary acoustic path transmission function is respectively as follows: in Fig. 1
H2(z)=0.01+0.01z-1+0.9z-2-0.01z-3-0.75z-4 (8)
Influence of the step-length to system performance and noise reduction value is the most obvious in FXLMS algorithm, below using fixed step size,
Sinusoidal Variable Step Algorithm and normalization Variable Step Algorithm are to noise control；
Fixed step size algorithm is restrained as the term suggests step size mu is fixed value, and according to the property of LMS algorithm: step size mu value is big
Speed is fast, but steady-state error is larger, conversely, step size mu value is smaller, steady-state error is smaller, but convergence rate is slower, so, LMS
Convergence speed of the algorithm and steady-state error are conflicts.Influence for analysis step-length to convergence time and steady-state error, filtering
Device order L is chosen for 20, as a result as shown in Figure 3.Wherein, t is convergence time, eminRepresent stable state output error.
From the figure 3, it may be seen that system can restrain as 0 < μ < 0.02, steady-state error increases with step-length and is increased, when convergence
Between shorten therewith, so to noise reduction require it is relatively high when select lesser step-length, when more demanding to convergence rate, selection
Biggish step-length；As μ > 0.02, system no longer restrains, and noise reduction effect is not achieved.
In order to solve the defect of fixed step size algorithm, many innovatory algorithms are proposed, most is exactly Variable Step Algorithm, i.e.,
Step-length fixed in algorithm, the step size mu (k) and error signal e of the FXLMS algorithm of present embodiment are replaced with the step-length of time-varying
(k) functional relation are as follows:
Wherein, the speed of parameter alpha control step change, β control the value range of step-length.Following simulation analysis α, β take not
When with value, influence relationship of the error of observation signal to step-length.Function relation curve such as Fig. 4 of step size mu (k) and error signal e (k)
(a) and shown in Fig. 4 (b), the simulation parameter of Fig. 4 (a): α=2, β=0.01,0.02,0.04；The simulation parameter of Fig. 4 (b): β=
0.01, α=0.5,1,2；
By Fig. 4 (a) and Fig. 4 (b) it is found that when e (k) value is larger, corresponding u (k) value is also larger, algorithm the convergence speed
Comparatively fast.When algorithm enters convergence state, e (k) value reaches minimum, and corresponding u (k) value is also minimum, and the bigger step-length value of β
Range is bigger, and α can control step change speed.
Influence for comparative analysis fixed step size algorithm and sinusoidal Variable Step Algorithm to system performance, is calculated using fixed step size
The change curve of method simulation analysis system output error signal and time, shown in simulation result such as Fig. 5 (a), simulation parameter: L=
20, μ=0.01,0.02；Using the change curve of sinusoidal Variable Step Algorithm simulation analysis output amplitude and time, simulation result is such as
Shown in Fig. 5 (b), simulation parameter takes α=2 in Fig. 5 (b), β=0.01.
As shown in Figure 5, when fixed step size algorithm obtains small steady-state error same as Variable Step Algorithm, convergence rate than
Sinusoidal variable step is slow, and to obtain faster convergence rate, and steady-state error again can be bigger than sinusoidal variable step, so sinusoidal become step
Long algorithm alleviates the contradiction between steady-state error and convergence rate, that is, achieves lesser steady-state error, and maintains very fast
Convergence rate.
Therefore, for sinusoidal Variable Step Algorithm compared with fixed step size algorithm, sinusoidal Variable Step Algorithm can solve fixed step size
Inherent shortcoming, be a kind of effective modified Variable Step Algorithm.
It is well known that automobile is in the process of moving, automobile speed can not be always maintained at and drive at a constant speed, and automobile engine turns
Speed is also impossible to remain unchanged, then the reference signal of active noise control system is exactly unstable, but the signal of time-varying.
Sinusoidal Variable Step Algorithm only considered the functional relation of step-length and error, not account for reference signal amplitude variation bring to being
The influence for performance of uniting.If in fact reference signal x (k) amplitude is to become in the active noise control system using sinusoidal variable step
Change, then causes error signal that may not restrain.It is therefore desirable to find it is a kind of can be by reference to signal x (k) adjusting step
Algorithm.
Normalization algorithm is one kind of Variable Step Algorithm, according to the input of filter come the step-length of adjustment algorithm.With defeated
The continuous increase entered, the steady-state error of filter are also gradually increased, so needing to reduce by adjusting step-length the stable state of filter
Error.Normalization algorithm is to carry out " normalization " to step-length by reference to the squared Euclidean norm of signal x (k).Step-length and reference
The relational expression of signal x (k) are as follows:
From the above equation, we can see that μ1After choosing suitable parameter, step size mu (k) can change with reference signal x (k) amplitude and be adjusted,
X (k) amplitude increases μ (k) and reduces, and x (k) amplitude reduces μ (k) and increases, and enables change using the system of this algorithm to x (k)
Change is made adjustment, and the stability of system is improved.
The stability that system can be enhanced for verifying normalization algorithm, by reference signal x (k) amplitude in 0.25 second Shi Zengjia
It one times, inquires into error signal and changes with time rule.Normalization algorithm simulation parameter: μ1=0.01, α=2, as a result such as Fig. 6
(a)；Sinusoidal Variable Step Algorithm simulation parameter: β=0.01, α=2, simulation result such as Fig. 6 (b), wherein component 1,2 respectively indicates
Error signal when amplitude doubles when error signal when reference signal is constant changes over time curve, 0.25 second is at any time
Change curve.
By Fig. 6 (a), Fig. 6 (b) it is found that sinusoidal variable step can restrain before 0.25 second, in 0.25 second x (k) signal width
After value doubles, error signal diverging does not restrain, if wanting to continue to restrain, it is necessary to readjust parameter beta, α.In automobile active
The reference signal constructed in noise control system changes over time, it is necessary to continuous adjusting parameter, can just make system obtain compared with
Good noise reduction effect, therefore sinusoidal Variable Step Algorithm cannot directly apply to the active noise reduction system of car room.Normalization is calculated
After 0.25 second x (k) signal amplitude doubles, error signal can restrain method again, but it does not have sinusoidal variable step with
The characteristic of error size variation.The timing of x (k) amplitude one, the value of μ (k) be it is fixed, be equivalent to fixed step size algorithm, Bu Nenghuan
The contradiction between system convergence speed and steady-state error is solved, and is worked as | | x (k) | |2It can make step-length excessive when very little and make system
It is unstable, therefore present embodiment proposes improved Variable Step Algorithm to improve the noise reduction effect of Indoor Noise of Motor Vehicle.
The improved Variable Step Algorithm of present embodiment: respective excellent scarce according to sinusoidal Variable Step Algorithm and normalization algorithm
Point adds parameter γ by normalization algorithm step-length and the β in sinusoidal variable step is replaced to carry out the amplitude range of adjusting step, can obtain
The relational expression of step-length and reference signal and error signal out are as follows:
In formula (11), γ is to avoid | | x (k) | |2It is too small that step-length is caused to be arranged greatly very much, work as parameter μ1, γ is selected
Afterwards, the amplitude of step-length can adjust in real time according to x (k), and when e (k) is larger, u (k) is increased with it, when e (k) is smaller, μ (k)
Reduce therewith, and the adjustable step-length amplitude of α is with the trend of error signal variations, therefore improved Variable Step Algorithm is provided simultaneously with
Sinusoidal variable step and the advantages of normalization algorithm.
In order to analyze convergence and stability using improved Variable Step Algorithm system, simulation analysis error signal is at any time
Between change curve.Simulation parameter: γ=1, μ1=0.01, α=2, simulation result are as shown in Figure 7.
As shown in Figure 7, improved Variable Step Algorithm not only has normalization algorithm after the variation of x (k) signal amplitude, error
Signal can convergent characteristic again, and have sinusoidal Variable Step Algorithm convergence fast, the small advantage of steady-state error can be good at
Applied in automobile active noise control system.
For the noise reduction effect for assessing improved Variable Step Algorithm, the amplitude-frequency of simulation analysis noise signal and error signal is bent
Line, as a result as shown in Figure 8.
Amplitude in Fig. 8 at noise signal 100Hz is denoted as P1, the amplitude at error signal 100Hz is denoted as P2.Root as a result,
According to the calculation formula of noise knots modification:
By the simulation result of Fig. 8 it is found that noise signal is respectively in the amplitude in 100Hz, 200Hz and 300Hz
1.499,0.2379 and 0.1539, amplitude of the error signal at 100Hz, 200Hz and 300Hz is respectively 0.1325,0.0334
And 0.0251, can be calculated according to formula (12): the noise reduction value at 100Hz, at 200Hz and at 300Hz be divided into 21.07dB,
17.06dB and 15.75dB.It can be seen that this method has good noise reduction effect to engine noise.
The different working condition of automobile engine has different noise characteristics, can answer to verify improved Variable Step Algorithm
For automobile active noise control system, need to analyze the control effect under engine different conditions.For certain domestic automobile
Research, relationship at five grades of this automobile between engine speed and speed are as shown in table 1.
The corresponding relationship of table 1 revolving speed and speed
| n/(r/min) | 1800 | 2400 | 3000 | 3600 | 4200 |
| Speed/(km/h) | 59.5 | 79.9 | 100.3 | 120.7 | 141.1 |
Ideal noise reduction effect can be obtained under each engine speed to verify improved Variable Step Algorithm, take table 1
In several revolving speeds construct reference signal, simulation parameter is constant, still takes γ=1, μ1=0.01, α=2, according to simulation result,
It is as shown in table 2 to can be calculated noise reduction under each revolving speed using formula (12).
Noise reduction under each revolving speed of 2 engine of table
As shown in Table 2, improved Variable Step Algorithm is applied in active noise control system, under the various revolving speeds of engine
Ideal noise reduction can be obtained.It can be seen that maximum noise reduction is at the order of amplitude maximum under each revolving speed, width
It is bigger to be worth bigger noise reduction.If first three rank noise amplitude is close under certain revolving speed, the Variable Step Algorithm of application enhancements is to every single order
Noise can obtain biggish noise reduction.
Further to verify improved Variable Step Algorithm suitable for engine charge noise control system, using above-mentioned improvement
Variable Step Algorithm to being emulated under the even acceleration mode of this automobile, under five grades of operating conditions, utilize 1 data of table, simulated automotive
The reference signal constructed during speed accelerates to 141.1km/h from 59.5km/h is even in 8 seconds, as shown in Fig. 9 (a).It utilizes
Control information in Fig. 9 (a) is emulated, simulation parameter using convergence effect of the improved Variable Step Algorithm to error signal:
γ=1, μ1=0.01, α=2, as a result as shown in Fig. 9 (a) and Fig. 9 (b).
By Fig. 9 (a) and Fig. 9 (b) it is found that under the even accelerating mode of automobile, the amplitude and frequency of reference signal are at any time
Smooth variation, improved Variable Step Algorithm has preferable control effect for this signal.Error signal once restrains, even if
Reference signal is also changing, and error signal can also maintain stable state, does not need the time and is adjusted and can restrain.It is improved
The parameter μ of Variable Step Algorithm1, after γ, α determine, controller can join according to the x (k) of input, e (k) adjust automatically step-length
Number, does not need to make again and changes, and the case where system cannot restrain will not occurs because of the variation of speed, and can obtain preferable
Noise reduction effect.Therefore, the improved Variable Step Algorithm of present embodiment design is suitably applied the active noise control of car room
In system processed.
The improved Variable Step Algorithm of present embodiment combines the excellent of both sinusoidal Variable Step Algorithm and normalization algorithm
Point has been provided simultaneously with fast convergence rate, and steady-state error is small, and the reference signal and parameter suitable for time-varying are easy to choose；It will improve
Variable Step Algorithm be applied to active noise control system in, noise reduction can reach 10dB or more at each frequency, and make an uproar
Acoustic control system has apparent noise reduction effect to the automobile of five grades of even accelerating modes.
Claims (6)
1. the engine noise control method based on FXLMS algorithm, which is characterized in that the described method includes:
S1, the master control system model that engine charge noise is established with FXLMS algorithm construct master control using the revolving speed of engine
The reference signal x (k) of system model processed；
S2, off-line identification structure is established, to secondary channel transfer function H in active control system model2(z) it is recognized, and will
Identification result is supplied to active control system model；
S3, engine noise is controlled using the active noise control system model after identification.
2. engine noise control method according to claim 1, which is characterized in that the active control system model,
Include:
Primary channel transfer function H is passed through in the primary noise source of engine1(z), main channel output d (k) is obtained；
Reference signal x (k) is constructed using the revolving speed of engine, LMS algorithm adaptive-filtering, filtering are carried out to reference signal x (k)
Driving signal of the reference signal afterwards as secondary sound source, by secondary channel transfer function H2(z), secondary sound source is obtained accidentally
The output signal s (k) of difference signal sensor；
D (k) and s (k) are overlapped mutually, and are obtained error signal e (k)；Wherein e (k) and reference signal x (k) is adaptive for LMS algorithm
It should filter；
The off-line identification structure are as follows:
Noise generator exports white noise x ' (k) to Hs(z) andHsIt (z) is secondary channel transmission function to be measured,
For Hs(z) estimation；Hs(z) output d ' (k),Output y ' (k), the difference of d ' (k) and y ' (k) are Identification Errors e '
(k), right using LMS algorithm and Identification Errors e ' (k)Coefficient carry out adaptive adjustment and update；If off-line identification structure
Convergence, when off-line identification structure is run enough for a long time to stable state,After fixation,It can be used as active noise control
H in system model processed2(z) estimation
3. engine noise control method according to claim 1 or 2, which is characterized in that the step-length of the FXLMS algorithm
For fixed step size.
4. engine noise control method according to claim 1 or 2, which is characterized in that the step-length of the FXLMS algorithm
The functional relation of μ (k) and error signal e (k) are as follows:
Wherein, the speed of parameter alpha control step change, β control the value range of step-length.
5. engine noise control method according to claim 1 or 2, which is characterized in that the step-length of the FXLMS algorithm
The relational expression of μ (k) and reference signal x (k) are as follows:
Wherein, 0 < μ1< 1.
6. engine noise control method according to claim 1 or 2, which is characterized in that the step-length of the FXLMS algorithm
The functional relation of μ (k) and reference signal x (k) and error signal e (k) are as follows:
Wherein, parameter alpha is used for trend of the adjusting step amplitude with error signal variations, 0 < μ1< 1,0≤γ≤1.
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