CN101930731B - Mining multi-wave self-adaptive active noise control system - Google Patents

Abstract

The invention discloses a mining multi-wave self-adaptive active noise control system which comprises the following modules: a primary sensor array 9, an error sensor array 10, a loudspeaker array 1, a power amplification circuit module 2, a low-pass filter 3, an A/D conversion module 8, a D/A conversion module 4, an ANC controller 13, a field programmable gate array (FPGA) 7, a wireless transmission module 5, an explosion-proof enclosure 14, a spare battery circuit 11 and a main power supply circuit 12, wherein input noise signals are firstly converted to digital signals by the A/D conversion module 8, are subject to data processing, self-adaptive filtering, time delay and phase inversion by the ANC controller 13, are converted by the D/A conversion module 4, are processed by the low-pass filter 3 and the power amplification circuit module 2 and then are output by the loudspeaker array 1, the loudspeaker array 1 outputs sound waves which have the same frequency but opposite phases with main noise and are used for offsetting primary noise, residual error signals are fed back to the ANC controller 13 after being processed by the power amplification circuit module 2 and the A/D conversion module 8, the error signals are used for adjusting the weight coefficient of an self-adaptive filter, and secondary signals are changed according to the variation of the weight coefficient so as to minimize the error signals.

Description

Mining multi-wave self-adaptive active noise control system

Technical field

The invention belongs to a kind of mining active noise controlling system, specifically be applied to the colliery, adopt many ripples, self-adaptation, ACTIVE CONTROL mode to reduce the system of noise.

Background technology

Various device under the coal mine such as coalcutter, jackdrill, ventilation blower etc. have produced very big noise.The coal miner long term exposure in high intensity noise, coal mine operation narrow space, sealing in addition, sound wave is difficult to disperse.Tunnel wall acoustical absorptivity is poor simultaneously, is easy to form acoustic reflection, has further aggravated the harm of noise to the workman.High-intensity noise not only influences the physical and mental health of coal miner, and often covers the downhole safety alarm signal and cause the accident, so the Research of Noise Reduction under the coal mine is of great significance.

Traditional passive noise control technique generally adopts sound absorption, sound insulating material.But this mode is comparatively effective for the inhibition of high-frequency noises, and is undesirable for the noise reduction of low-frequency noise.In order to obtain good noise reduction, have only by increasing the thickness or the quality of material, cause volume too huge, be unfavorable for borehole operation, and increased the cost of enterprise.

The ultimate principle of noise ACTIVE CONTROL is that artificial one of generation and noise source frequency, amplitude equates, the secondary noise that phase place is opposite, and after secondary noise and the stack of former noise, sound wave interferes, thereby reaches the purpose of eliminating noise.Compare with traditional passive noise control technique, the active noise control technique tool has an enormous advantage and development prospect.

There is better wideband noise reduction in many ripples active noise controlling system than single ripple active noise controlling system.Simultaneously, because the position of noise source is not fixed in the mine, noise circumstance is constantly changing in the time of many, and the design of many ripples can solve the error that the counter productives such as mistake coupling of single ripple microphone are brought.

Noise mainly concentrates on low-frequency range in the mine, wavelength is longer, the coal mine operation narrow space, sealing, acoustic reflection is serious, a reference sensor, the single ripple active noise controlling system that secondary sound source and error pick-up are formed is very bad in down-hole restriceted envelope result of use, in order to enlarge the noise reduction space, the present invention adopts a plurality of reference sensors, a plurality of secondary sound sources and a plurality of error pick-up, simultaneously, because there is speed of convergence faster in many ripples active noise controlling system than single ripple active noise controlling system, mine laneway is longer, noise source is uncertain, noise source, distance is far away between secondary sound source, speed of convergence is had relatively high expectations, otherwise noise reduction is difficult to guarantee.

Chinese patent application numbers 200710304314.0, open day 2008.07.30, a kind of active noise controlling system and noise control method that utilizes sound wave interference mode disclosed, loudspeaker is installed facing to the noise source engine by this system in pilothouse, microphone is placed on driver's head position, loudspeaker and microphone are connected respectively in the sef-adapting filter of controller, upgrade the feature battle array of wave filter, and the module RLS of RLS algorithm, noise control method is generation and the anti-phase control sound of noise that continues in closed-loop system,, and revise and make the error minimum reducing noise with both mutual interference effects.

Chinese patent application numbers 200810003675.6, open day 2009.01.28, a kind of active noise controlling system is disclosed, the undesirable noise signal of listening to the place, place that this system utilizes reference signal to come ACTIVE CONTROL to be sent by noise source, described reference signal is sheltered by undesirable noise signal and the desired signal of listening to the existence of place, place, so that adapt to time dependent secondary path, make that the user can not feel to be disturbed by additional man made noise source in real-time mode.

At present, existing active noise controlling equipment can not satisfy the singularity of working environment under the coal mine, and mine active noise controlling system need have following characteristics:

(1) the controller speed of convergence is very fast.The ANC controller of existing active noise controlling equipment is based on RLS algorithm and lowest mean square (LMS) algorithm, the poor-performing of these algorithms aspect speed of convergence, and in the space of the long and narrow sealing in down-hole, noise signal can not be eliminated in time.

(2) electric apparatus for explosive gas.Unfavorable factors such as mine dust, high temperature, gas are arranged under the coal mine, therefore will adopt the good explosion precaution of security performance, must there be mining product safety sign card, conformity certificate of protection in the active noise controlling system in colliery.

(3) wireless monitor.The wireless outstation that is sent to of the working condition of active noise controlling system in the mine.Be convenient to the active noise controlling system is monitored in real time.The noise situations and the de-noising situation in outstation record colliery.

In order to satisfy the controller fast convergence rate of active noise controlling equipment under the mine, the requirement that has electric apparatus for explosive gas and have wireless monitor the invention provides a kind of mining multi-wave self-adaptive active noise control system.

Summary of the invention

The present invention is directed to noise in the mine and mainly concentrate on the characteristics of low-frequency range, adopt one of active control technology design based on the multi-wave self-adaptive active noise control system that improves the FXLMS algorithm.The present invention compares with adaptive control system in the past, has adopted the design of aspects such as many ripples, the speed of convergence that improves controller, wireless monitor, flameproof enclosure, is suitable for the serious occasion of the long and narrow sealing echo of mine interference.This system can eliminate the noise pollution of mine effectively.

Below method of the present invention is discussed.

Based on the multi-wave self-adaptive active noise control system that improves the FXLMS algorithm, mainly form: primary sensor array by following module, the error pick-up array, loudspeaker array, the power amplification circuit module, low-pass filter, the A/D modular converter, the D/A modular converter, the ANC controller, field programmable gate array (FPGA), wireless transport module, flameproof enclosure, the reserve battery circuit, main feed circuit, the noise signal of input at first converts digital signal to through the A/D modular converter, carry out data processing by the ANC controller, auto adapted filtering, time-delay, paraphase, change through the D/A modular converter again, export by loudspeaker array after low-pass filter and the power amplification circuit module, loudspeaker array output is used for offsetting elementary noise with the sound wave of main noise with same frequency and reversed-phase, after the processing of residual error signal through power amplification circuit module and A/D modular converter, feed back to the ANC controller, utilize error signal to regulate the weight coefficient of sef-adapting filter, variation according to weight coefficient changes secondary signal, makes error signal reduce to minimum.

Described active noise controlling system adopts based on the many ripples ANC controller that improves the FXLMS algorithm.

Described active noise controlling system, the chip that the ANC controller is selected for use is TMS320C240.

Described active noise controlling system can monitor the working condition of system in the mine in real time by wireless transmission.

Described active noise controlling system, wireless transport module adopts and meets the MC13192 of IEEE 802.15.4 standard as rf chip.

Described active noise controlling system, sensor adopts the laying method that reduces flow disturbance.

Described active noise controlling system uses electric apparatus for explosive gas, satisfies the requirement of electrical equipment safety technique in the explosive gas atmospheres such as containing gas under the mine.

Described active noise controlling system is furnished with reserve battery, have a power failure or powering-off state under system can operate as normal.

Described active noise controlling system, the chip that adopts in the A/D modular converter is AD7656.

Described active noise controlling system, the chip that adopts in the D/A modular converter is DAC3282.

Compared with prior art, the invention has the advantages that:

1. by adopting many ripples designs, primary sensor array, error pick-up array, loudspeaker array are applicable to and the space of mine three-dimensional can gather and eliminate noise in the mine exactly.

2. by improving the FXLMS algorithm, reduced in the weight coefficient change procedure of sef-adapting filter the influence of system has been improved the speed of convergence of system.

3. by the design of wireless transport module, be convenient to the staff by the active noise controlling system is monitored.

4. by adopt reducing the laying method of sensor stream disturbance, the influence of the hydrodynamic noise that produces when reducing by air through sensor surface has increased correlativity, and sensor is placed on the outside and also is convenient to safeguard.

5. by the design of main feed circuit and battery module, battery is in charged state under the normal condition, and equipment carries out powered operation by main circuit.When main circuit generation abnormal electrical power supply, reserve battery is powered, and sends the abnormal electrical power supply signal to main control room simultaneously, so that maintenance in time.

Mining multi-wave self-adaptive active noise control system has been realized noise in the real-time monitoring mine, and eliminates the function of noise.Meet mining specific environment for use, satisfy explosive gas atmosphere electrical installation requirement under the coal mine.

Figure of description

Fig. 1 is the mining active noise controlling block diagram of system

Fig. 2 is mining active noise controlling system works flow process figure

Fig. 3 is many ripples FXLMS algorithmic system block diagram

Fig. 4 is that many ripples improve FXLMS algorithmic system block diagram

Fig. 5 is the comparison diagram of system's input and loudspeaker output

Fig. 6 is the phantom error curve map of system

Fig. 7 is ANC controller chip TMS320C240 figure

Fig. 8 is the Interface design block diagram of DSP and FPGA

Fig. 9 is the wireless transport module design frame chart

Figure 10 is mining linear DC power supply theory diagram

Figure 11 is the microphone laying method figure that reduces flow disturbance

Figure 12 is the design drawing of mining active noise controlling system flameproof enclosure

Among the figure, 1, loudspeaker array; 2, power amplification circuit module; 3, low-pass filter; 4, D/A modular converter; 5, wireless transport module; 6, ANC controller; 7, field programmable gate array (FPGA); 8, A/D modular converter; 9, primary sensor array; 10, error pick-up array; 11, reserve battery circuit; 12, main feed circuit; 13, ANC controller; 14, flameproof enclosure; 15, mine noise; 16, serial ports; 17, GB60; 18, SPI mouth; 19, MC13192; 20-1, antenna; 20-2, antenna; 21, AC power; 22, transformer; 23, rectification circuit; 24, filtering circuit; 25, current limiting pressure-limiting circuit; 26, explosion-proof direct supply; 27, sensor.

Embodiment

Following embodiment will further specify the present invention, and embodiment should not be regarded as limiting the scope of the invention.Below in conjunction with accompanying drawing working method of the present invention is elaborated.

As shown in Figure 1, mining multi-wave self-adaptive active noise control system of the present invention comprises primary sensor array 9, error pick-up array 10, loudspeaker array 1, power amplification circuit module 2, low-pass filter 3, A/D modular converter 8, D/A modular converter 4, ANC controller 13, field programmable gate array (FPGA) 7, wireless transport module 5, flameproof enclosure 14, reserve battery circuit 11, main feed circuit 12.

The system works flow process is as follows:

As shown in Figure 2, primary sensor array 9 is converted to electric signal with voice signal after detecting mine noise 15, and 2 pairs of these electric signal of power amplification circuit module amplify, and the simulating signal dress is changed to digital signal through A/D modular converter 8, send into ANC controller 13 and handle.

X in the ANC controller 13 (n) is a noise signal, k ₁(n) be the feedforward control part, k ₂(n) be the FEEDBACK CONTROL part.E (n) is an error signal, and y (n) is the secondary sound source signal, and s (z) is the transport function of secondary channels.

After the processing by ANC controller 13, produce one and noise source frequency, amplitude equates, phase place is opposite secondary sound source signal y (n).The secondary sound source signal is converted to simulating signal through D/A modular converter 4, amplifies by power amplification circuit module 2 pairs of voltages, electric currents, to drive loudspeaker array 1.Noise source frequency of loudspeaker array 1 generation, the secondary noise signal that amplitude equates, phase place is opposite are by superposeing to offset noise with former noise.

The noise residue signal that error pick-up array 10 detects after offsetting, residue signal amplifies through power amplification circuit module 2, produces noise cancellation signal through sending into ANC controller 13 behind the A/D modular converter 8.k ₁(n), k ₂(n) weight coefficient is brought in constant renewal in according to the variation of noise signal x (n), error signal e (n), so just can change real-time generation and the equivalent anti-phase secondary sound source signal of noise signal according to noise, offsets noise.

As shown in Figure 3, be many ripples FXLMS algorithmic system block diagram.

Supposing the system has P reference sensor, a Q secondary sound source and R error pick-up, and sef-adapting filter length is L, and secondary sound source transport function length is M.

Elementary acoustic path transport function is P (Z), and secondary acoustic path transport function is S (Z), and it is estimated as

(Z).

Many ripples FXLMS algorithm is derived as follows:

If the reference vector matrix is X (n)=[x ⁽¹⁾(n), x ⁽²⁾(n) ..., x ^(P)(n)] ^T

Then p reference sensor in n output signal constantly is

x ^(p)(n)＝[x ^(p)(n)，x ^(p)(n-1)，…，x ^(p)(n-L+1)] ^T p＝1，2，…，P

x ^(p)′(n)＝[x ^(p)(n)，x ^(p)(n-1)，…，x ^(p)(n-M+1)] ^T

Q secondary sound source in n output signal constantly is

y ^(q)(n)＝[y ^(q)(n)，y ^(q)(n-1)，…，y ^(q)(n-M+1)] ^T

N r elementary noise signal constantly is expressed as d ^(r)(n).

N constantly r error pick-up place error signal of picking up is expressed as e ^(r)(n).

The sef-adapting filter weight vector is expressed as

w ^(p，q)(n)＝[w ^(p，q) ₀(n)，w ^(p，q) ₁(n)，…w ^(p，q) _L-1(n)] ^T

Q sub loudspeaker to the unit impact response vector of r error microphone is

$S^{(q,r)}\left(n\right)={[S_0^{(q,r)}\left(n\right),S_1^{(q,r)}\left(n\right),\·\·\·,S_{M-1}^{(q,r)}\left(n\right)]}^T$

The filtering reference signal is r ^{(p, q, r)}(n)=[r ^{(p, q, r)}(n), r ^{(p, q, r)}(n-1) ..., r ^{(p, q, r)}(n-L+1)] ^T

Then, from Fig. 3, can draw, be through the output signal behind the ANC controller:

$y^{\left(q\right)}\left(n\right)=\underset{p=1}{\overset{p}{\mathrm{\Σ}}}{\left(w^{(p,q)}\left(n\right)\right)}^T{x}^p\left(n\right)=\underset{p=1}{\overset{P}{\mathrm{\Σ}}}\underset{p=0}{\overset{L-1}{\mathrm{\Σ}}}{w}_l^{(p,q)}\left(n\right)x^{\left(p\right)}(n-l)$ l＝0，1，…L

Can draw from Fig. 3, the filtering reference signal is:

$r^{(p,q,r)}\left(n\right)={\hat{s}}^{(q,r)}\left(n\right)x^{\left(p\right)\′}\left(n\right)$

Can draw from Fig. 3, the computing formula of sef-adapting filter weight vector is:

$w^{(p,q)}(n+1)=w^{(p,q)}\left(n\right)-\mathrm{\μ}\underset{r=1}{\overset{R}{\mathrm{\Σ}}}{r}^{(p,q,r)}\left(n\right)e^{\left(r\right)}\left(n\right)$ (wherein, μ is a step factor)

Can draw from Fig. 3, error signal is the residual signal after elementary noise source and the secondary sound source stack, and the computing formula of error signal is:

$e^{\left(r\right)}\left(n\right)=d^{\left(r\right)}\left(n\right)+\underset{q=1}{\overset{Q}{\mathrm{\Σ}}}{S}^{(q,r)T}\left(n\right)y^q\left(n\right)$

On this basis, many ripples FXLMS algorithm is improved.As shown in Figure 4, be that many ripples improve FXLMS algorithmic system block diagram.

Elementary noise signal d ^(r)(n)

And error signal e ^(r)(n)

Subtract each other the correction that obtains elementary noise signal

That is: ${\hat{d}}^{\left(r\right)}\left(n\right)=d^{\left(r\right)}\left(n\right)-e^{\left(r\right)}\left(n\right).$

Utilization obtains

Produce the error signal of revising

For:

${\hat{e}}^{\left(r\right)}\left(n\right)={\hat{d}}^{\left(r\right)}\left(n\right)+\underset{p=1}{\overset{P}{\mathrm{\Σ}}}\underset{q=1}{\overset{Q}{\mathrm{\Σ}}}{S}^{(q,r)T}\left(n\right)y^q\left(n\right)$

The computing formula of sef-adapting filter weight vector is:

$w^{(p,q)}(n+1)=w^{(p,q)}\left(n\right)-\mathrm{\μ}\underset{r=1}{\overset{R}{\mathrm{\Σ}}}{r}^{(p,q,r)}\left(n\right){\hat{e}}^{\left(r\right)}\left(n\right)$

With the error signal formula

Correction formula with elementary noise signal

Substitution

In, the error signal of the correction that obtains in theory

Go to zero.Like this, the secondary acoustic path weight coefficient that just can improve sef-adapting filter upgrades pace of change, reduces the influence of the variation of weight coefficient to system.Since when step factor μ hour, speed of convergence is slower, this correction makes algorithm can choose bigger step factor μ, has improved speed of convergence.The every renewal of secondary acoustic path weight coefficient of sef-adapting filter once all is copied in the sef-adapting filter, is used for noise signal is carried out filtering.

Above-mentioned multi-wave self-adaptive active noise controlling algorithm is carried out emulation on computers by MATLAB.Because noise mainly concentrates on low-frequency range in the mine, suppose that input signal is made up of gaussian random series.If P=Q=R=10, sample frequency is 5KHZ, sampling number N=1000, and the length L of wave filter=16, secondary sound source transport function length is M=16, convergence coefficient gets 0.0001.In programming procedure, adopted the algorithm of matrix and vector, to improve the travelling speed of MATLAB.

As shown in Figure 5, be the contrast of system's input and loudspeaker output.

Can see that by contrast among the figure it is equifrequency basically that system input and loudspeaker are exported, anti-phase waveform, the design that many ripples are described can be eliminated the noise signal in the space effectively.In engineering design, note choosing of step factor μ.Step factor has determined the speed of convergence of sef-adapting filter.When step factor was big, speed of convergence was very fast, and filter effect is relatively poor; When step factor hour, speed of convergence is slower, filter effect is better.Therefore, suitable step factor be chosen, just more stable filter effect can be obtained.

As shown in Figure 6, be the phantom error curve of system.

As can be seen, the error of desired output and actual output mainly fluctuates between 0.1 to 0.01 from the result of graph of errors.Preceding 30 nodes are as training sequence, system's instability, before the node of the relative back of error of 30 nodes bigger than normal.

As shown in Figure 7, the chip that the ANC controller is selected for use is the TMS320C240 of TI company, it has at a high speed, flexibly, characteristics such as low consumption, the hardware configuration of optimization is applicable to Adaptive Signal Processing.The TMS320C240 instruction execution cycle is 50nS, and can carry out many instructions simultaneously in the monocycle.The XC3S200-4TQG144C that field programmable gate array (FPGA) adopts Xilinx company to produce controls controller.It is the DSP external storage that the inner register space that is provided with of FPGA is used as.DATA15-DATA0 represents the data bus of DSP, and ADDRESS13-ADDRESS0 represents the address bus of DSP.CE3-CE0 represents the address at the place, visit data space that determines.The DSP external interrupt pin that INT4-INT0 represents can interrupt to the DSP application by its peripheral hardware.

As shown in Figure 8, be the Interface design of DSP and FPGA.

Data transfer procedure between them is: DSP and FPGA carry out data by the mode of external interrupt and transmit, after data enter FPGA, FPGA at first sends interrupt request singal to DSP, if DSP responds interruption, then send address signal, then by data bus DATA15-DATA0 reading of data by address bus ADDRESS13-ADDRESS0.

As shown in Figure 9, be the wireless transport module design.

What rf chip adopted is MC13192 (19).It meets IEEE 802.15.4 standard, and the frequency of operation of selection is 2.405～2.480GHz, and message transmission rate is 250kbps, adopts the O-QPSK debud mode.MC13192 (19) is the Zigbee radio frequency chip of inner integrated MAC (medium Access Layer), PHY (Physical layer) hardware logic.MC13192 radiofrequency signal in the circuit design adopts the method for difference input and output.Processor is selected the GB60 (17) that meets the Zigbee technology for use, and it is 8 MCU of HCS08 series.Whole protocol stack resides on the main control chip GB60 (17).

The serial data that receives is sent to the MC13192 (19) by SPI mouth 18 from GB60 (17), launches from antenna 20-1 by transtation mission circuit after process spread spectrum O-QPSK is modulated to carrier wave again.The radiofrequency signal that receives from antenna 20-2 is sent to the MC13192 (19), obtains original data through demodulation, despreading, is sent to GB60 (17) through SPI mouth 18 again, is converted into serial data and sends.

As shown in figure 10, be mining linear DC power supply.

Mining linear DC power supply generally is made up of transformer 22, rectification circuit 23, filtering circuit 24, dual current limiting pressure-limiting circuit 25.Transformer 22 has step-down and electromagnetic isolation function, to guarantee the performance of intrinsic safe explosion-proof.Rectification circuit 23 is a direct current with exchange conversion.Alternating component in the output of filtering circuit 24 filtering rectification circuits.Dual current limiting pressure-limiting circuit 25 can be used as voltage stabilizing or constant-current circuit, guarantees explosion-proof direct supply 26 output voltage stabilizing or constant-current supplies.Produce 1.5V and VCC (3.3v) voltage supply FPGA by the linear DC power supply, 1.6V and VDD (3.3v) voltage is supplied with DSP.

As shown in figure 11, be the microphone laying method that reduces flow disturbance.

Owing to all hydrodynamic noise can be arranged in the noise that primary sensor and error pick-up are accepted, oneself produce when being air process sensor 27 surfaces.Therefore, the sound pressure variations of the hydrodynamic noise at sensor 27 places and disturbance has limited the effect of noise cancellation.Sensor 27 is placed in the external disturbance small container that links to each other with passage by little otch, can increases correlativity, and sensor 27 also can be protected corresponding device and be convenient to maintenance when being placed in the external container.

As shown in figure 12, be to satisfy to have the mine explosion-suppression shell that damp uses under the coal mine.

The principle of flameproof enclosure is: the live part of electrical equipment is placed in the special shell, and this shell has the effect that explosive mixture outside the spark of electric component generation in the shell and electric arc and the shell is kept apart.The shape of flameproof enclosure adopts the strong rectangle structure of anti-quick-fried ability.Therefore the volume of flameproof enclosure and shell implode pressure independent, satisfying under the service condition, reduce the volume of flameproof enclosure as far as possible.The flame proof protected mode that explosion suppresion surface adopts flange to connect, plane-shaped structure is adopted in flame proof faying face gap.After FL flange length was determined, the design alternative of flange thickness will guarantee that under the effect of explosion pressure, the deformation extent of flange can not influence the size in flame proof gap.

The flameproof joint structural parameters should meet the requirement of following table.L represents the composition surface width, and L1 represents the width of bolt to the edge, composition surface, and W represents the maximal clearance corresponding with shell volume V.

Claims (9)

1. mining multi-wave self-adaptive active noise control system, it is characterized in that described system is made up of following module: primary sensor array, the error pick-up array, loudspeaker array, the power amplification circuit module, low-pass filter, the A/D modular converter, the D/A modular converter, based on the ANC controller that improves many ripples FXLMS algorithm, field programmable gate array (FPGA), wireless transport module, flameproof enclosure, the reserve battery circuit, main feed circuit, the noise signal of input at first converts digital signal to through the A/D modular converter, by carrying out data processing based on the ANC controller that improves many ripples FXLMS algorithm, auto adapted filtering, time-delay, paraphase, change through the D/A modular converter again, export by loudspeaker array after low-pass filter and the power amplification circuit module, loudspeaker array output is used for offsetting elementary noise with the sound wave of main noise with same frequency and reversed-phase, after the processing of residual error signal through power amplification circuit module and A/D modular converter, feed back to based on the ANC controller that improves many ripples FXLMS algorithm, utilize error signal to regulate the weight coefficient of sef-adapting filter, variation according to weight coefficient changes secondary signal, make error signal reduce to minimum, many ripples of the improvement FXLMS algorithm concrete grammar that improves the ANC controller employing of many ripples FXLMS algorithm is:

(1) many ripples FXLMS algorithm:

Supposing the system has P reference sensor, a Q secondary sound source and R error pick-up, and sef-adapting filter length is L, and secondary sound source transport function length is M, and elementary acoustic path transport function is P (Z), and secondary acoustic path transport function is S (Z), and it is estimated as

If the reference vector matrix is X (n)=[x ⁽¹⁾(n), x ⁽²⁾(n) ..., x ^(P)(n)] ^T,

Then p reference sensor in n output signal constantly is,

x ^(p)(n)＝[x ^(p)(n)，x ^(p)(n-1)，…，x ^(p)(n-L+1)] ^T，p＝1，2，…，P，

x ^(p)′(n)＝[x ^(p)(n)，x ^(p)(n-1)，…，x ^(p)(n-M+1)] ^T，

Q secondary sound source in n output signal constantly be,

y ^(q)(n)＝[y ^(q)(n)，y ^(q)(n-1)，…，y ^(q)(n-M+1)] ^T，

N r elementary noise signal constantly is expressed as d ^(r)(n),

N constantly r error pick-up place error signal of picking up is expressed as e ^(r)(n),

The sef-adapting filter weight vector is expressed as w ^{(p, q)}(n)=[w ^{(p, q)} ₀(n), w ^{(p, q)} ₁(n) ... w ^{(p, q)} _L-1(n)] ^T,

Q sub loudspeaker to the unit impact response vector of r error microphone be,

$s^{(q,r)}\left(n\right)={[s_0^{(q,r)}\left(n\right),s_1^{(q,r)}\left(n\right),...,s_{M-1}^{(q,r)}\left(n\right)]}^T,$

The filtering reference signal is r ^{(p, q, r)}(n)=[r ^{(p, q, r)}(n), r ^{(p, q, r)}(n-1) ..., r ^{(p, q, r)}(n-L+1)] ^T, then, be through the output signal behind the ANC controller,

$y^{\left(q\right)}\left(n\right)=\underset{p=1}{\overset{p}{\mathrm{\Σ}}}{\left(w^{(p,q)}\left(n\right)\right)}^T{x}^p\left(n\right)=\underset{p=1}{\overset{P}{\mathrm{\Σ}}}\underset{p=0}{\overset{L-1}{\mathrm{\Σ}}}{w}_l^{(p,q)}\left(n\right)x^{\left(p\right)}(n-l),l=\mathrm{0,1},...L,$

The filtering reference signal is $r^{(p,q,r)}\left(n\right)={\hat{s}}^{(q,r)}\left(n\right)x^{{\left(p\right)}^{\′}}\left(n\right),$

The computing formula of sef-adapting filter weight vector is,

$w^{(p,q)}(n+1)=w^{(p,q)}\left(n\right)-\mathrm{\μ}\underset{r=1}{\overset{R}{\mathrm{\Σ}}}{r}^{(p,q,r)}\left(n\right)e^{\left(r\right)}\left(n\right),$ μ is a step factor,

Error signal is the residual signal after elementary noise source and the secondary sound source stack, and the computing formula of error signal is $e^{\left(r\right)}\left(n\right)=d^{\left(r\right)}\left(n\right)+\underset{q=1}{\overset{Q}{\mathrm{\Σ}}}{s}^{(q,r)T}\left(n\right)y^q\left(n\right);$

(2) on this basis, many ripples FXLMS algorithm is improved, many ripples FXLMS algorithm is improved:

Elementary noise signal d ^(r)(n) and error signal e ^(r)(n) subtract each other the correction that obtains elementary noise signal

For ${\hat{d}}^{\left(r\right)}\left(n\right)=d^{\left(r\right)}\left(n\right)-e^{\left(r\right)}\left(n\right),$

Utilization obtains

Produce the error signal of revising

For,

${\hat{e}}^{\left(r\right)}\left(n\right)={\hat{d}}^{\left(r\right)}\left(n\right)+\underset{q=1}{\overset{Q}{\mathrm{\Σ}}}{s}^{(q,r)T}\left(n\right)y^q\left(n\right),$

The computing formula of sef-adapting filter weight vector is,

$w^{(p,q)}(n+1)=w^{(p,q)}\left(n\right)-\mathrm{\μ}\underset{r=1}{\overset{R}{\mathrm{\Σ}}}{r}^{(p,q,r)}\left(n\right){\hat{e}}^{\left(r\right)}\left(n\right),$

With the error signal formula

Correction formula with elementary noise signal ${\hat{d}}^{\left(r\right)}\left(n\right)=d^{\left(r\right)}\left(n\right)-e^{\left(r\right)}\left(n\right),$ Substitution ${\hat{e}}^{\left(r\right)}\left(n\right)={\hat{d}}^{\left(r\right)}\left(n\right)+\underset{q=1}{\overset{Q}{\mathrm{\Σ}}}{s}^{(q,r)T}\left(n\right)y^q\left(n\right)$ In, obtain, ${\hat{e}}^{\left(r\right)}\left(n\right)=0$ 。

2. multi-wave self-adaptive active noise control system according to claim 1 is characterized in that the chip of selecting for use based on the ANC controller that improves many ripples FXLMS algorithm is TMS320C240.

3. multi-wave self-adaptive active noise control system according to claim 1 is characterized in that the working condition of the system in the mine that can monitor in real time by wireless transmission.

4. multi-wave self-adaptive active noise control system according to claim 3 is characterized in that the wireless transport module employing meets the MC13192 of IEEE 802.15.4 standard as rf chip.

5. multi-wave self-adaptive active noise control system according to claim 1 is characterized in that sensor adopts the laying method that reduces flow disturbance.

6. multi-wave self-adaptive active noise control system according to claim 1 is characterized in that using electric apparatus for explosive gas, satisfies the requirement of electrical equipment safety technique in the explosive gas atmospheres such as containing gas under the mine.

7. multi-wave self-adaptive active noise control system according to claim 1 is characterized in that being furnished with reserve battery, and system can operate as normal under power failure or powering-off state.

8. multi-wave self-adaptive active noise control system according to claim 1 is characterized in that the chip that adopts in the A/D modular converter is AD7656.

9. multi-wave self-adaptive active noise control system according to claim 1 is characterized in that the chip that adopts in the D/A modular converter is DAC3282.

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