A secondary channel online identification active control method

Abstract

The present invention relates to a secondary channel online identification active control method, which belongs to the field of active vibration and noise control technology. In order to solve the problem in the prior art that redundant vibration is introduced into active vibration, which makes the control effect worse, a secondary channel online identification active control method is provided. The method includes five steps: obtaining a reference signal and random noise; obtaining a first residual vibration signal; obtaining a second residual vibration signal; determining the active controller weight coefficient and the iterative step size of the modeling FIR low-pass filter; and decoupling the active control convergence of the vibration and noise active control system from the secondary channel identification. Through a random noise generator, the identification signal required by the secondary channel is output, and the FIR low-pass filter is used to perform real-time modeling of the secondary channel. A power adjustment factor is introduced to adjust the iterative step size of the active control link and the random noise power size, thereby effectively decoupling the coupling interference between the active control and the channel modeling, and achieving good control effect.

Description

Technical Field

[0001] The invention relates to a secondary channel online identification active control method, belonging to the technical field of active vibration noise control. Background Art

[0002] Active vibration and noise control typically utilizes adaptive filtering algorithms. However, the introduction of secondary channels necessitates identification to enhance algorithm convergence. For complex operating conditions and harsh application environments, online identification is employed—active control is performed while the secondary channels are identified. While this algorithm structure ensures real-time modeling of the secondary channels, it introduces unwanted vibration into the active vibration, compromising control effectiveness. Summary of the Invention

[0003] The purpose of the present invention is to solve the problem in the prior art that redundant vibration is introduced into active vibration, which makes the control effect worse, and to provide a secondary channel online identification active control method, which can ensure the implementation modeling of the secondary channel without introducing redundant vibration and has a good control effect.

[0004] In order to solve the above problems, this application is implemented through the following technical solutions:

[0005] A secondary channel online identification active control method is characterized in that it includes the following steps:

[0006] Step 1: Obtain reference signal and random noise: Add an acceleration sensor to the vibration noise source to collect acceleration signals, which are called reference signals. , reference signal The sampling frequency is , reference signal Through the primary channel Generate vibration noise signal ;

[0007] Adding a power-adjustable random noise generator and an M-order modeling FIR low-pass filter to the active vibration noise control system , wherein the random noise generator is used to generate random noise, and output the identification secondary channel when the active control system is turned on The required white noise signal , the modeled FIR low-pass filter Used to simulate the secondary channel in real time;

[0008] Step 2: Obtain the first residual vibration signal :The reference signal obtained in step 1 Input the active controller of the vibration noise active control system, adjust the weight coefficient of the active controller, and output the vibration noise suppression signal , cancel out the noise signal ,

[0009] After cancellation, the first residual vibration signal is output ;

[0010] Step 3: Obtain the second residual vibration signal :The first residual vibration signal output in step 2 With the random noise signal in step 1 By estimating the secondary channel Output Superposition to obtain the second residual vibration signal ;

[0011] ,

[0012] Where: is the first residual vibration signal, is the noise signal, is the vibration noise suppression signal, To identify white noise signals, It is represented by a transverse filter of length M, that is, ,

[0013] Represents a random noise signal After estimating the secondary channel The vibration signal generated;

[0014] Step 4: Determine the active controller weight coefficient and modeling FIR low-pass filters Iteration step: The second residual vibration signal obtained in step 3 is used Acts as the true residual signal to participate in the active controller control coefficient and modeling FIR low-pass filters The iterative process is carried out, and the iterative step length is determined according to the control effect of the vibration and noise active control system;

[0015] Step 5: decoupling the active control convergence of the vibration noise active control system from the secondary channel identification;

[0016] Step 6: When the secondary channel transfer characteristics change, the residual vibration Increase, and repeat steps 1-5 to re-identify the secondary channel.

[0017] Furthermore, the weight coefficient adjustment of the active controller in step 2 is based on the filtered least mean square algorithm, which specifically includes the following steps:

[0018] Step 2.1: Reference signal Perform convolution calculation, namely: ,

[0019] Where: is the reference signal, is the active controller coefficient vector, is the output signal;

[0020] Step 2.2: The output signal obtained in step 2.1 and identification of white noise signals Superposition and common entry into the secondary channel , output vibration noise suppression signal , ,

[0021] Where: for Through the secondary channel The vibration signal generated;

[0022] Indicates the secondary channel, which is the output signal To the first residual vibration signal The transfer characteristics of the whole process are expressed by a transverse filter with a length of M, that is, ;

[0023] Furthermore, the control coefficient of the active controller in step 4 is The iterative process is as follows:

[0024] Control coefficient The iterative formula is: ,

[0025] Where: is the iteration step length, is the reference signal Perform filtering and correction of the secondary channel signal, ,

[0026] Where: is the reference signal, It is represented by a transverse filter of length M, that is, ;

[0027] is the residual vibration, To identify the error; According to the control effect that the initial stage of the vibration noise active control system is dominated by residual vibration and the convergence speed is accelerated, and the convergence stage is dominated by identification error and the precise search of the system is achieved, the control effect is determined

[0028] Iteration step for: ;

[0029] Where: is the correction coefficient to adjust the iteration step size, usually in the range of 0.9-1.2; is the exponential calculation of e; is the power regulation factor;

[0030] Furthermore, the FIR low-pass filter modeling in step 4 is The iterative process is as follows:

[0031] Modeling FIR low-pass filters The iterative formula is: ,

[0032] Where: is the magnification factor;

[0033] According to the power of white noise required in the active control system of vibration noise, the amplification factor The settings are as follows: ,

[0034] Where: It is a coefficient related to the convergence value of the first vibration residual error e(n), usually in the range of 0.0001-0.01;

[0035] The setting can adjust the white noise power in sections;

[0036] The control process of the active vibration and noise control system is as follows: Initial stage: In order to ensure the accelerated convergence of residual vibration, white noise does not participate in the initial stage, and white noise with relatively small power should be added;

[0037] Convergence stage: As the active control residual vibration becomes smaller, the noise power should be gradually increased so that the modeling coefficients approach the true value;

[0038] Convergence end: When active control and channel identification are stable, the noise power should be gradually reduced to zero, thereby reducing the impact of secondary channel identification on active control, so that Further reduction will achieve the best control effect;

[0039] Furthermore, the step 5 uses the power adjustment factor Decoupling the active control convergence and secondary channel identification of the vibration noise active control system, the power adjustment factor : ,

[0040] Where: represents the power of the second residual vibration signal at time n, Expressed as the power of the random noise signal at time n, Expressed as the power of the first residual vibration signal at time n, It is expressed as the power of the reference signal at time n;

[0041] In the initial stage, the random noise power is small and the active control has not converged, which makes ; With the active control process, the algorithm converges, ,get , random noise power reduction, , ,at this time ,get , Gradually decreases, and at the same time, in order to ensure that the random noise is not submerged by the reference vibration signal, it is multiplied by the coefficient ;

[0042] Furthermore, the 、 、 、 The value of changes slowly, so it is determined by using a single exponential smoothing forecast:

[0043] , , , ,

[0044] Where: are respectively represented as the power of the second residual vibration signal at time n and time (n+1), They are respectively expressed as the power of the random noise signal at time n and time (n+1), are respectively represented as the power of the first residual vibration signal at time n and time (n+1), They are represented as the power of the reference signal at time n and time (n+1), For the forgetting factor, .

[0045] The secondary channel online identification active control method of the present application outputs the identification signal required by the secondary channel and the FIR low-pass filter by adding a random noise generator. Real-time modeling of secondary channels;

[0046] A power regulation factor is introduced to adjust the iteration step size and random noise power of the active control link, effectively eliminating the coupling interference between active control and channel modeling; the comparison of the effects before and after vibration signal suppression in the time-frequency domain is demonstrated by comparing the initial vibration signal and the residual vibration signal, demonstrating the effective suppression of vibration by this method; the residual vibration signal also achieves rapid and accurate convergence when the initial vibration signal changes, verifying the robustness of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flowchart of this application;

[0048] Figure 2 It is the amplitude diagram of each order before the transfer characteristic of the secondary channel changes;

[0049] Figure 3 It is the amplitude diagram of each order after the secondary channel transfer characteristic changes;

[0050] Figure 4 This is a time domain effect comparison chart before and after vibration signal suppression;

[0051] Figure 5 This is a comparison chart of the frequency domain effects before and after vibration signal suppression;

[0052] Figure 6 This is a flow chart of Example 2;

[0053] Figure 7 for Figure 6 Working principle diagram. DETAILED DESCRIPTION

[0054] The following provides specific embodiments of the present invention with reference to the accompanying drawings to further illustrate the structure of the present invention.

[0055] Example 1. A secondary channel online identification active control method, the specific process is as follows Figure 1 As shown, the following steps are included:

[0056] Step 1: Obtain reference signal and random noise: Add an acceleration sensor to the vibration noise source to collect acceleration signals, which are called reference signals. , reference signal The sampling frequency is , reference signal Through the primary channel Generate vibration noise signal ;

[0057] Adding a power-adjustable random noise generator and an M-order modeling FIR low-pass filter to the active vibration noise control system , wherein the random noise generator is used to generate random noise, and output the identification secondary channel when the active control system is turned on The required white noise signal , the modeled FIR low-pass filter Used to simulate the secondary channel in real time;

[0058] Step 2: Obtain the first residual vibration signal :The reference signal obtained in step 1 Input the active controller of the vibration noise active control system, adjust the weight coefficient of the active controller, and output the vibration noise suppression signal , cancel out the noise signal ,

[0059] After cancellation, the first residual vibration signal is output ;

[0060] Step 3: Obtain the second residual vibration signal :The first residual vibration signal output in step 2 With the random noise signal in step 1 By estimating the secondary channel Output Superposition to obtain the second residual vibration signal ;

[0061] ,

[0062] Where: is the first residual vibration signal, is the noise signal, is the vibration noise suppression signal, To identify white noise signals, It is represented by a transverse filter of length M, that is, ,

[0063] Represents a random noise signal After estimating the secondary channel The vibration signal generated;

[0064] Step 4: Determine the active controller control coefficient and modeling FIR low-pass filters Iteration step: The second residual vibration signal obtained in step 3 is used Acts as the true residual signal to participate in the active controller control coefficient and modeling FIR low-pass filters The iterative process is carried out, and the iterative step length is determined according to the control effect of the vibration and noise active control system;

[0065] Step 5: decoupling the active control convergence of the vibration noise active control system from the secondary channel identification;

[0066] Introducing Power Regulation Factor , so that the active control link and the secondary channel modeling are effectively separated. At the beginning, the active control link is accelerated to converge, and the secondary channel is not modeled. As the active control gradually converges, its step size is reduced and fine-tuning is started. At the same time, the output power of the random noise signal is increased to accelerate the modeling of the secondary channel, thereby effectively decoupling the active control convergence and channel identification;

[0067] The weight coefficient adjustment of the active controller in step 2 is based on the filtered least mean square algorithm, which specifically includes the following steps:

[0068] Step 2.1: Reference signal Perform convolution calculation, namely: ,

[0069] Where: is the reference signal, For active controller, is the output signal;

[0070] Step 2.2: The output signal obtained in step 2.1 and identification of white noise signals Superposition and common entry into the secondary channel , output vibration noise suppression signal , ,

[0071] Where: for Vibration signal generated through the secondary channel; Indicates the secondary channel, which is the output signal To the first residual vibration signal The transfer characteristics of the whole process are expressed by a transverse filter with a length of M, that is, ;

[0072] Among them, the control coefficient of the active controller in step 4 is The iterative process is as follows:

[0073] Control coefficient The iterative formula is: ,

[0074] Where: is the iteration step length; is the reference signal Perform filtering and correction of the secondary channel signal, ,

[0075] Where: is the reference signal, It is represented by a transverse filter of length M, that is, ;

[0076] is the residual vibration, To identify the error; According to the control effect that the initial stage of the vibration noise active control system is dominated by residual vibration and the convergence speed is accelerated, and the convergence stage is dominated by identification error and the precise search of the system is achieved, the control effect is determined

[0077] Iteration step for: ;

[0078] Where: is the correction coefficient to adjust the iteration step size to the range of 0.9-1.2; is the exponential calculation of e; is the power regulation factor; initial stage: residual vibration It is the main part, and a larger iteration step size needs to be set to speed up the convergence.

[0079] Identification error The impact of is not taken into account;

[0080] Convergence stage: As the residual vibration signal decreases, the identification error It is the main part. The influence of residual vibration signal is ignored. The iteration step size is adjusted to a smaller value to accurately search for the optimal value of the system and complete active control.

[0081] Among them, the FIR low-pass filter modeled in step 4 The iterative process is as follows:

[0082] Modeling FIR low-pass filters The iterative formula is: ,

[0083] Where: is the magnification factor;

[0084] According to the power of white noise required in the active control system of vibration noise, the amplification factor The settings are as follows: ,

[0085] Where: It is a coefficient related to the convergence value of the first vibration residual error e(n), usually in the range of 0.0001-0.01;

[0086] The setting can adjust the white noise power in sections;

[0087] The control process of the active vibration and noise control system is as follows:

[0088] Initial stage: To ensure the accelerated convergence of residual vibration, white noise is not involved in the initial stage, and white noise with relatively low power should be added;

[0089] Convergence stage: As the active control residual vibration becomes smaller, the noise power should be gradually increased so that the modeling coefficients approach the true value;

[0090] Convergence end: When active control and channel identification are stable, the noise power should be gradually reduced to zero, thereby reducing the impact of secondary channel identification on active control, so that Further reduction will achieve the best control effect;

[0091] Wherein, the power adjustment factor is used in step 5 Decoupling the active control convergence and secondary channel identification of the vibration noise active control system, the power adjustment factor : ,

[0092] Where: represents the power of the second residual vibration signal at time n, Expressed as the power of the random noise signal at time n, Expressed as the power of the first residual vibration signal at time n, It is expressed as the power of the reference signal at time n;

[0093] In the initial stage, the random noise power is small and the active control has not converged, which makes ; With the active control process, the algorithm converges, ,get , random noise power reduction, , ,at this time ,get , Gradually decreases, and at the same time, in order to ensure that the random noise is not submerged by the reference vibration signal, it is multiplied by the coefficient ;

[0094] Among them, the 、 、 、 The value of changes slowly, so it is determined by using a single exponential smoothing forecast:

[0095] , , , ,

[0096] Where: are respectively represented as the power of the second residual vibration signal at time n and time (n+1), They are respectively expressed as the power of the random noise signal at time n and time (n+1), are respectively represented as the power of the first residual vibration signal at time n and time (n+1), They are represented as the power of the reference signal at time n and time (n+1), For the forgetting factor, .

[0097] The above method was experimentally verified and vibration noise control simulation was performed on the MATLAB / Simulink platform. The secondary channel is identified and actively controlled online using a sinusoidal signal synthesis with frequencies of 25Hz and 30Hz, amplitudes of 1, and phases of zero. The random noise generator generates random Gaussian white noise with a mean of zero and a variance of 0.001. The real secondary channel is simulated using a 200-order transverse FIR filter, and its amplitude characteristics are as follows: Figure 2 shown.

[0098] The parameters are set as sampling rate 10000Hz, identification filter length 300 steps, is 1, is 0.001. At the same time, in order to verify the robustness of the algorithm, the secondary channel transfer characteristics are set to change at the 5th second, and its amplitude characteristics are as follows: Figure 3 The parameter settings are listed in Table 1.

[0099] Table 1 Simulation parameter setting table

[0100] ,

[0101] After the setting is completed, start the simulation and obtain the comparison chart of the effect of this method on vibration signal suppression before and after, as shown in the figure below: Figure 4-Figure 5 As shown, Figure 4 It is the time domain effect diagram. Figure 5 The frequency domain renderings are shown. Comparing the initial vibration signal and the residual vibration signal shows rapid convergence in the time domain, with an amplitude reduction of 95%. In the frequency domain, the 25Hz and 30Hz vibration line spectra were reduced by 30dB and 32dB, respectively, demonstrating the algorithm's effective vibration suppression. At the 5th second, the secondary channel transfer characteristics changed, and the residual vibration signal's amplitude quickly converged to 90% by the 6th second and to 95% by the 7th second. This demonstrates that identification can be quickly achieved again when the secondary channel changes, validating the algorithm's robustness.

[0102] Example 2. A secondary channel online identification active control method, such as Figure 6As shown, the steps are basically the same as those in Example 1, except that: Step 6 is also included, when the secondary channel transfer characteristic changes, the residual vibration Increase, and repeat steps 1-5 to re-identify the secondary channel.

[0103] The working principle of this embodiment is as follows Figure 7 As shown, a random noise generator is added to output the identification signal required by the secondary channel and the FIR low-pass filter Real-time modeling of secondary channels;

[0104] At the 5th second, the reference signal remains unchanged, and the secondary channel transfer characteristics change, and its amplitude characteristics are as follows: Figure 3 shown.

[0105] The power regulation factor is introduced to adjust the iteration step size and random noise power of the active control link, effectively eliminating the coupling interference between active control and channel modeling.

[0106] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Persons skilled in the art may make various modifications, additions, or substitutions to the described specific embodiments without departing from the spirit of the present invention or exceeding the scope defined by the appended claims.

Claims

1. A secondary channel online identification active control method, characterized by: The following steps are involved: Step 1: Obtain a reference signal and random noise; Step 2: Obtain the first residual vibration signal :The reference signal obtained in step 1 Input the active controller of the vibration noise active control system, adjust the weight coefficient of the active controller, and output the vibration noise suppression signal , cancel out the noise signal , After cancellation, the first residual vibration signal is output ; Step 3: Obtain the second residual vibration signal :The first residual vibration signal output in step 2 With the random noise signal in step 1 By estimating the secondary channel Output Superposition to obtain the second residual vibration signal ; ， Where: is the first residual vibration signal, is the noise signal, is the vibration noise suppression signal, To identify white noise signals, It is also represented by a transverse filter of length M, that is, , Represents a random noise signal After estimating the secondary channel The vibration signal generated; Step 4: Determine the active controller weight coefficient and modeling FIR low-pass filters Iteration step: The second residual vibration signal obtained in step 3 is used Acts as the true residual signal to participate in the active controller control coefficient and modeling FIR low-pass filters The iterative process is carried out, and the iterative step length is determined according to the control effect of the vibration and noise active control system; The active controller control coefficient The iterative process is as follows: Control coefficient The iterative formula is: , Where: is the iteration step length; is the reference signal Perform filtering and correction of the secondary channel signal, , Where: is the reference signal, The transverse filter of length M is represented as ; is the residual vibration, To identify the error; According to the control effect of the active vibration and noise control system, the initial stage is mainly based on residual vibration to accelerate the convergence speed, and the convergence stage is mainly based on identification error to achieve the precise search of the system. Iteration step for: ; Where: is the correction coefficient to adjust the iteration step size, usually in the range of 0.9-1.2; is the exponential calculation of e; is the power regulation factor; The modeled FIR low-pass filter The iterative process is as follows: Modeling FIR low-pass filters The iterative formula is: , Where: is the magnification factor; According to the power of white noise required in the active control system of vibration noise, the amplification factor The settings are as follows: ， Where: is the coefficient related to the convergence value of the first vibration residual error e(n), which is in the range of 0.0001-0.01; Step 5: decoupling the active control convergence of the vibration noise active control system from the secondary channel identification; Power Regulation Factor Decoupling the active control convergence and secondary channel identification of the vibration noise active control system, the power adjustment factor : , Where: represents the power of the second residual vibration signal at time n, Expressed as the power of the random noise signal at time n, Expressed as the power of the first residual vibration signal at time n, It is expressed as the power of the reference signal at time n.

2. The method for online identification and active control of a secondary channel according to claim 1, characterized in that: The method further includes step 6: when the transfer characteristic of the secondary channel changes, repeating steps 1-5 to re-identify the secondary channel.

3. A secondary channel online identification active control method according to claim 1 or 2, characterized in that: The step 1 is as follows: add an acceleration sensor to the vibration noise source to collect the acceleration signal, which is called the reference signal , reference signal The sampling frequency is , reference signal Through the primary channel Generate vibration noise signal ; Adding a power-adjustable random noise generator and an M-order modeling FIR low-pass filter to the active vibration noise control system , wherein the random noise generator is used to generate random noise, and output the secondary channel when the active control system is turned on Need to identify white noise signal , the modeled FIR low-pass filter Used to perform real-time simulation of secondary channels.

4. The method for online identification and active control of a secondary channel according to claim 3, characterized in that: The weight coefficient adjustment of the active controller in step 2 is based on the filtered least mean square algorithm, which specifically includes the following steps: Step 2.1, the reference signal Perform convolution calculation, namely: , Where: is the reference signal, For active controller, is the output signal; Step 2.2: The output signal obtained in step 2.1 and identification of white noise signals Superposition and common entry into the secondary channel , output vibration noise suppression signal , , Where: for Vibration signal generated through the secondary channel; Indicates the secondary channel, which is the output signal To the first residual vibration signal The transfer characteristics of the whole process are expressed by a transverse filter with a length of M, that is, .

5. The method for online identification and active control of a secondary channel according to claim 4, characterized in that: As described in step 5 、 、 、 The value of is determined by using a single exponential smoothing forecast: ， ， ， ， Where: are respectively represented as the power of the second residual vibration signal at time n and time (n+1), They are respectively expressed as the power of the random noise signal at time n and time (n+1), are respectively represented as the power of the first residual vibration signal at time n and time (n+1), They are represented as the power of the reference signal at time n and time (n+1), For the forgetting factor, .

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