Carrier rocket ultralow-frequency elastic self-adaptive fault-tolerant attitude stability control method and system

Abstract

The invention relates to a carrier rocket ultralow-frequency elastic self-adaptive fault-tolerant attitude stability control method and system, and belongs to the field of carrier rocket overall design and carrier rocket dynamics and control. The frequency crossing problem is thoroughly avoided, and the method has a good application effect on processing the second-order and third-order elastic mode self-adaptive suppression problem of the whole rocket; besides, different from the conventional series notch filtering thought, the invention provides an elastic suppression strategy based on an additive compensation thought, a path of compensation signal is added on the basis of an original control loop, the influence of the loop on an original control system scheme can be controlled by means of amplitude limiting and the like, and a relatively good application basis is provided for safety and stability; the method has a good engineering application prospect, and has a good fault-tolerant adaptive capacity to heaven and earth design differences and rocket body dynamic characteristic changes under faults.

Description

Technical Field

[0001] The invention relates to a carrier rocket ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method and system, and belongs to the field of carrier rocket overall design, carrier rocket dynamics and control. Background Art

[0002] Large liquid-bundled launch vehicles are affected by propellant sloshing, elastic vibration of the rocket body, and other strong external interferences including aerodynamic forces during flight. Their attitude control faces difficulties such as multi-modal coupling and serious channel cross-linking. As the size of launch vehicles increases and the slenderness ratio increases, the first-order elastic modal frequency of the rocket body decreases sharply. Taking the series of rocket bundled configurations as an example, the elastic frequency has reached the lowest level of large launch vehicles in the world, and the stability control of medium and low frequency rigid sloshing modes has become a design problem.

[0003] Due to the differences between heaven and earth and the limitations of ground test conditions, model parameters are often unable to be accurately predicted, and the ground design deviation values ​​are conservative. Using a set of controllers to adapt to various deviation combinations in the entire flight segment often increases the complexity of the controller and also loses the controller performance. In addition, with the advancement of technology, major engineering launch missions have put forward higher requirements on the reliability and safety of the carrier, and the rocket system needs to have the ability to adapt to failures and work normally. In summary, the attitude control system design of the fourth-generation carrier rocket needs to have stronger deviation adaptability and fault tolerance on the basis of meeting the performance requirements of the traditional system, and leave enough stability margin to adapt to uncertainties such as nonlinearity, channel cross-linking, and unmodeled interference.

[0004] The adaptive notch filter currently used in engineering can only process the adaptive filtering of the first-order center frequency. The suppression of multi-order elastic signals is achieved by connecting filters in series, but this processing method inevitably causes the cross-hopping of the center frequency. The instability of the center frequency of the notch filter during the cross-hopping period of the frequency may affect the filtering effect and even the system stability. Therefore, the present invention proposes an adaptive attitude control method suitable for ultra-low frequency elastic control of launch vehicles, which has stronger adaptability to dynamics-ground inconsistency and typical power system failures than traditional control methods. Summary of the invention

[0005] The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art and provide a method and system for ultra-low frequency elastic adaptive fault-tolerant attitude stability control of a launch vehicle to solve the problem that ultra-low frequency elastic control of a launch vehicle cannot be designed. The method uses a dual-center frequency notch filter to realize online identification of the elastic frequency, and real-time compensation of the elastic information in the control loop to realize adaptive adjustment of the controller, thereby improving the robustness of the system.

[0006] The above-mentioned purpose of the present invention is mainly achieved through the following technical solutions:

[0007] A launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method, comprising:

[0008] Remove the rigid body motion information from the identification signal;

[0009] Filter the identification signal after removing the rigid body motion information, filter out the low-frequency components and high-frequency components, and retain the information within the required frequency band;

[0010] For the identification signal after the filtering process, a recursive least squares algorithm with a forgetting factor is used to update the parameters of the dual center frequency adaptive notch filter to obtain the two-order modal frequencies with the highest energy in the identification signal;

[0011] The two-order modal frequencies are updated or maintained in the following manner: when the vibration magnitude is greater than the required value and the rate of change of the modal frequency is less than the required value, the notch filter adopts the identification frequency of the current beat; otherwise, the notch filter adopts the identification frequency of the previous beat;

[0012] Generate a reference signal using the updated or maintained second-order modal frequency, and generate an elastic compensation signal according to the reference signal using a minimum mean square error adaptive filtering algorithm;

[0013] The elastic compensation signal is compensated into the control swing angle instruction to realize the adaptive fault-tolerant attitude stability control of the rocket body.

[0014] In the above-mentioned ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method for a launch vehicle, the removal of rigid body motion information in the identification signal includes:

[0015] If multiple sets of rate gyros are arranged on the launch vehicle, the angular velocity information measured by any two rate gyros installed at different positions is subtracted to remove the rigid body motion signal;

[0016] If the launch vehicle is equipped with only one set of rate gyro, the angular velocity calculated by the inertial group is subtracted from the angular velocity information measured by the rate gyro to remove the rigid body motion signal;

[0017] If the launch vehicle is not equipped with a rate gyro, the angular velocity is calculated using the inertial group and the rigid body motion signal is filtered out through a high-pass filter.

[0018] In the above-mentioned ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method for a launch vehicle, a bandpass filter is used to filter the identification signal from which the rigid body motion information is removed. The form of the bandpass filter is as follows:

[0019]

[0020] in, B=ω high-ω low ,ω low The lower boundary frequency of the reserved band for bandpass filtering, ω high is the upper boundary frequency of the band reserved for bandpass filtering, and s is a complex frequency domain variable of pull-type variation.

[0021] In the above-mentioned launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method, a recursive least squares algorithm with a forgetting factor is used to update the parameters of the dual center frequency adaptive notch filter to obtain the two-order modal frequencies with the highest energy in the identification signal, including:

[0022] (1) Initialize the cross-correlation vector, set z(0) = 0; initialize the correlation matrix, set Φ(0) = δI, where δ is the regularization parameter and I is the unit diagonal matrix;

[0023] (2) According to the identification signal after filtering, the weight matrix is ​​updated and the correlation matrix Φ(n) is input. The recursive formula of the correlation matrix Φ(n) is:

[0024]

[0025] Wherein, λ is an exponentially weighted forgetting factor, and λ is a number close to 1 but less than 1; x(n), x(n-1), x(n-2), and x(n-3) are the outputs of the pole part of the filter at the nth, n-1th, n-2th, and n-3th beats, respectively;

[0026] (3) According to the identification signal after filtering, the cross-correlation vector z(n) between the weight input and the expected response is updated. The recursive update formula of the cross-correlation vector z(n) is:

[0027]

[0028] Among them, x(n-4) is the output of the filter pole part of the n-4th beat;

[0029] (4) Update the recursive least squares weight vector according to the correlation matrix Φ(n) and the cross-correlation vector z(n) The update formula is:

[0030]

[0031] (5) According to the recursive least squares weight vector Calculate the characteristic parameters of the notch filter a1(n), a2(n), the calculation formula is:

[0032]

[0033] Then the second-order modal frequencies identified by the filter are obtained, and the expressions are as follows:

[0034]

[0035] Where ΔT is the sampling time.

[0036] In the above-mentioned launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method, at least one of the following is satisfied:

[0037] The regularization parameter δ is related to the signal-to-noise ratio. It takes a small value when the signal-to-noise ratio is high and a large value when the signal-to-noise ratio is low. The value of δ is 1×10 -4 ;

[0038] The smaller the exponential weighted forgetting factor λ is, the faster the forgetting is and the smoother the recognition frequency is. The value of λ is 0.95~0.99;

[0039] The sampling time ΔT is the control period 0.02s.

[0040] In the above-mentioned ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method for launch vehicles, the vibration magnitude is determined by the output value y of the spectrum damper. s Or use frequency identification to identify the amplitude of the input signal |y(n)|.

[0041] In the above-mentioned launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method, the reference signal is generated by using the updated or maintained second-order modal frequency using the following formula:

[0042]

[0043] Where d(n) is the reference signal at the nth time step, A is the reference signal amplitude, is the updated or maintained two-order modal frequency, t is the time, ΔT is the sampling time; δ is the sampling pulse function.

[0044] In the above-mentioned launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method, generating an elastic compensation signal according to the reference signal using a minimum mean square error adaptive filtering algorithm includes:

[0045] (1) Define the weight vector

[0046]

[0047] Where M is the filter order, let the initial weight vector

[0048] (2) Calculate the estimated error e(n) between the input signal and the output signal:

[0049] e(n)=u(n)-δ ek (n)

[0050] Where e(n) is the error signal at the nth time step, δ ek (n) is the frequency of the nth time step. The k-th order elastic compensation signal of , u(n) is the residual elastic signal of the identification signal after filtering minus the information filtered by the dual center frequency filter, and is the M×1 input vector of the n-th time step;

[0051] (3) According to the weight vector And the error signal e(n), calculate the recursive weight vector under stochastic gradient descent, the weight vector recursion formula is:

[0052] w(n+1)=w(n)+μu(n)e(n)

[0053] Among them, μ is the calculation step size;

[0054] (4) According to the reference signal d(n), calculate the filter output at the n+1th time step:

[0055] δ ek (n+1)=w T (n)d(n)

[0056] Among them, δ ek (n+1) is the frequency of the n+1th time step. The compensation signal of the k-th order elasticity; T is the transpose.

[0057] A launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control system, comprising:

[0058] An information removal module removes rigid body motion information from the identification signal;

[0059] The filtering module performs filtering processing on the identification signal after the rigid body motion information is removed, filters out the low-frequency components and high-frequency components, and retains the information within the required frequency band;

[0060] The modal frequency acquisition and adaptive filtering module updates the parameters of the dual-center frequency adaptive notch filter using a recursive least squares algorithm with a forgetting factor for the identification signal after the filtering process, so as to obtain the two-order modal frequencies with the highest energy in the identification signal;

[0061] A frequency updating or maintaining module is used to update or maintain the two-order modal frequencies, in the following way: when the vibration magnitude is greater than the required value and the rate of change of the modal frequency is less than the required value, the notch filter adopts the identification frequency of the current beat; otherwise, the notch filter adopts the identification frequency of the previous beat;

[0062] A compensation signal generating module, which generates a reference signal using the updated or maintained second-order modal frequencies, and generates an elastic compensation signal using a minimum mean square error adaptive filtering algorithm according to the reference signal;

[0063] The signal compensation module compensates the elastic compensation signal into the control swing angle instruction to realize the adaptive fault-tolerant attitude stability control of the rocket body.

[0064] A computer device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0065] Compared with the prior art, the present invention has at least the following beneficial effects:

[0066] (1) The present invention proposes for the first time a dual-center frequency adaptive filter, which can simultaneously and parallelly identify the frequencies of two-order elastic modes, completely avoiding the frequency crossing problem, and has a good application effect in dealing with the problem of adaptive suppression of the second and third order elastic modes of the whole arrow; in addition, different from the previous series notch filtering idea, the embodiment of the present invention proposes an elastic suppression strategy based on the additive compensation idea, which adds a compensation signal on the basis of the original control loop, and can control the influence of the loop on the original control system scheme by means of limiting and the like, which has a good application foundation in terms of safety and stability, and has a good prospect for engineering application.

[0067] (2) The present invention relates to an adaptive attitude control method suitable for ultra-low frequency elastic control of a launch vehicle, which has good fault tolerance and adaptability to the design differences between the ground and the spacecraft and the changes in the dynamic characteristics of the rocket body under faults. Compared with traditional control methods, it has stronger adaptability to dynamic ground-ground inconsistencies and typical power system faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a control scheme architecture diagram in an embodiment of the present invention;

[0069] Figure 2 This is a schematic diagram of a spectrum damper in step four of an embodiment of the present invention;

[0070] Figure 3 This is a schematic diagram of a frequency updating and maintaining module in step 4 of an embodiment of the present invention;

[0071] Figure 4 This is an example diagram of algorithm simulation verification in an embodiment of the present invention;

[0072] Figure 5 This is an example diagram of algorithm simulation verification in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0074] like Figure 1 As shown, the ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method for a carrier rocket in an embodiment of the present invention specifically includes the following steps:

[0075] Step 1: Rigid body trend item removal;

[0076] In order to ensure the accuracy of frequency estimation, it is necessary to eliminate the rigid body motion information in the identification signal and only retain the elastic information.

[0077] If multiple sets of rate gyros are arranged on the launch vehicle, the angular velocity information measured by any two rate gyros installed at different positions is subtracted to remove the rigid body motion signal;

[0078] If the launch vehicle is equipped with only one set of rate gyro, the angular velocity calculated by the inertial group is subtracted from the angular velocity information measured by the rate gyro to remove the rigid body motion signal;

[0079] If the launch vehicle is not equipped with a rate gyro, the angular velocity is calculated using the inertial group and the rigid body motion signal is filtered out through a high-pass filter.

[0080] Step 2: Filter the identification signal after removing the rigid body motion information according to the design prior information.

[0081] Using a bandpass filter to preprocess the identification input data, filter out the low-frequency and high-frequency components and retain only the information within the required frequency band, which can effectively improve the accuracy and continuity of frequency identification. The bandpass filter form is as follows:

[0082]

[0083] In the formula, B=ω high -ω low ,ω low The lower boundary frequency of the reserved band for bandpass filtering, ω high is the upper boundary frequency of the band reserved for bandpass filtering, and s is a complex frequency domain variable of pull-type variation.

[0084] Step 3: For the identification signal after filtering, use the recursive least squares algorithm (RLS) with forgetting factor to update the parameters of the dual center frequency adaptive notch filter. Then the two modal frequencies with the highest energy in the signal are identified;

[0085] The calculation process of the recursive least squares algorithm is:

[0086] (1) Algorithm initialization: Initialize the mutual correlation vector z(0) = 0; Initialize the correlation matrix, Φ(0) = δI, where δ is a regularization parameter, which is set to be related to the signal-to-noise ratio. It takes a small value when the signal-to-noise ratio is high and a large value when the signal-to-noise ratio is low. In this embodiment, the value is 1×10 -4 , I is the unit diagonal matrix.

[0087] (2) According to the identification signal after filtering, the weight matrix is ​​updated and the input correlation matrix Φ(n) is input. The correlation matrix recursive formula is:

[0088]

[0089] Wherein, λ is an exponentially weighted forgetting factor, λ is a number close to 1 but less than 1, the smaller the value of λ is, the faster the forgetting is, and the smoother the recognition frequency is. In this embodiment, the value is 0.95 to 0.99; x(n), x(n-1), x(n-2), and x(n-3) are the outputs of the filter pole parts of the nth beat, n-1th beat, n-2th beat, and n-3th beat, respectively.

[0090] (3) Based on the identification signal after filtering, update the cross-correlation vector z(n) between the weight input and the expected response. The recursive update formula of z(n) is:

[0091]

[0092] Wherein, x(n-4) is the output of the pole part of the filter at the n-4th beat.

[0093] (4) Update the recursive least squares weight vector according to the correlation matrix Φ(n) and the cross-correlation vector z(n) The update formula is:

[0094]

[0095] (5) According to the recursive least squares weight vector The characteristic parameters of the notch filter a1(n) and a2(n) are calculated using the following formula:

[0096]

[0097] Then the second-order modal frequencies identified by the filter are obtained, and the expressions are as follows:

[0098]

[0099] Wherein, ΔT is the sampling time, and in this embodiment, the control period is 0.02s. The identification frequency results are output in ascending order.

[0100] Step 4: Update or maintain the two-order modal frequencies.

[0101] When the vibration level of the rocket is small, the frequency identification module will identify the inelastic signal; the signal components in the rocket attitude control loop are complex, and the vibration frequency identification value is unstable. Therefore, the following improvement strategy is adopted for the adaptive notch filter control:

[0102] When the vibration magnitude is greater than the required value and the rate of change of the modal frequency is less than the required value, the notch frequency of the notch filter adopts the current modal frequency; otherwise, the notch frequency of the notch filter adopts the modal frequency of the previous beat; the rate of change of the modal frequency is the difference between the modal frequency of the current beat and the modal frequency of the previous beat divided by the time period; the schematic diagram of the frequency update and hold module is shown in Figure 3 .

[0103] The vibration level can be determined by the output value y of the spectrum damper s , the spectrum damper is in the form of Figure 2 As shown, the amplitude |y(n)| of the input signal can also be identified by frequency. The criterion for judging whether the frequency estimation value is stable is to judge the size of the frequency increment.

[0104] Step 5: Generate a reference signal using the updated or maintained second-order modal frequencies. The formula is as follows:

[0105]

[0106] Where d(n) is the reference signal at the nth time step, A is the reference signal amplitude, is the updated or maintained two-order modal frequency, t is the time, ΔT is the sampling time; δ is the sampling pulse function.

[0107] The frequency is generated by the least mean square error (LMS) adaptive filtering algorithm. The compensation signal δ of the kth order elasticity ek ;

[0108] The calculation process of the LMS algorithm is:

[0109] (1) Define the weight vector

[0110]

[0111] Wherein, M represents the filter order, and in this embodiment, the value M=5; the weight vector is initialized

[0112] (2) Calculate the estimated error e(n) between the input signal and the output signal:

[0113] e(n)=u(n)-δ ek (n)

[0114] Where e(n) is the error signal at the nth time step, δ ek (n) is the frequency of the nth time step. u(n) is the k-th order elastic compensation signal of the identification signal after filtering, which is the residual elastic signal after the dual center frequency filter is deducted, and is the M×1 input vector of the n-th time step;

[0115] (3) According to the weight vector And the error signal e(n), calculate the recursive weight vector under stochastic gradient descent, the weight vector recursion formula is:

[0116] w(n+1)=w(n)+μu(n)e(n)

[0117] Where μ is the calculation step size;

[0118] (4) According to the reference signal d(n), calculate the filter output at the n+1th time step:

[0119] δ ek (n+1)=w T (n)d(n)

[0120] Among them, δ ek (n+1) is the frequency of the n+1th time step. The compensation signal of the k-th order elasticity; T is the transpose.

[0121] Step 6: The two-order characteristic frequencies identified in Step 3 and Step 4 are calculated using the algorithm in Step 5 to calculate the swing angle compensation instruction δ e1 and δ e2 . The elastic compensation signal δ e1 and δ e2 The compensation is added to the control swing angle command, thereby realizing the adaptive fault-tolerant attitude stability control of the elastic vibration of the rocket body. Figure 4 , Figure 5 , the adaptive control strategy can effectively improve the system robustness.

[0122] The present invention also provides a launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control system, comprising:

[0123] An information removal module removes rigid body motion information from the identification signal;

[0124] The filtering module performs filtering processing on the identification signal after the rigid body motion information is removed, filters out the low-frequency components and high-frequency components, and retains the information within the required frequency band;

[0125] The modal frequency acquisition and adaptive filtering module updates the parameters of the dual-center frequency adaptive notch filter using a recursive least squares algorithm with a forgetting factor for the identification signal after the filtering process, and obtains the two-order modal frequencies with the highest energy in the identification signal; and performs adaptive notch filtering on the signal to be identified after the filtering process to extract the elastic signal;

[0126] A frequency updating or maintaining module is used to update or maintain the two-order modal frequencies, in the following way: when the vibration magnitude is greater than the required value and the rate of change of the modal frequency is less than the required value, the notch filter adopts the identification frequency of the current beat; otherwise, the notch filter adopts the identification frequency of the previous beat;

[0127] A compensation signal generating module, which generates a reference signal using the updated or maintained second-order modal frequencies, and generates an elastic compensation signal using a minimum mean square error adaptive filtering algorithm according to the reference signal;

[0128] The signal compensation module compensates the elastic compensation signal into the control swing angle instruction to realize the adaptive fault-tolerant attitude stability control of the rocket body.

[0129] The present invention also provides a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0130] The above description is only the best specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

[0131] The contents not described in detail in the specification of the present invention belong to the common knowledge of the professionals in this field.

Claims

1. A launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method, characterized in that: include: Remove the rigid body motion information from the identification signal; Filter the identification signal after removing the rigid body motion information, filter out the low-frequency components and high-frequency components, and retain the information within the required frequency band; For the identification signal after the filtering process, a recursive least squares algorithm with a forgetting factor is used to update the parameters of the dual center frequency adaptive notch filter to obtain the two-order modal frequencies with the highest energy in the identification signal; The two-order modal frequencies are updated or maintained in the following manner: when the vibration magnitude is greater than the required value and the rate of change of the modal frequency is less than the required value, the notch filter adopts the identification frequency of the current beat; otherwise, the notch filter adopts the identification frequency of the previous beat; Generate a reference signal using the updated or maintained second-order modal frequency, and generate an elastic compensation signal according to the reference signal using a minimum mean square error adaptive filtering algorithm; The elastic compensation signal is compensated into the control swing angle instruction to realize the adaptive fault-tolerant attitude stability control of the rocket body.

2. The launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method according to claim 1 is characterized in that: The removing of rigid body motion information in the identification signal comprises: If multiple sets of rate gyros are arranged on the launch vehicle, the angular velocity information measured by any two rate gyros installed at different positions is subtracted to remove the rigid body motion signal; If the launch vehicle is equipped with only one set of rate gyro, the angular velocity calculated by the inertial group is subtracted from the angular velocity information measured by the rate gyro to remove the rigid body motion signal; If the launch vehicle is not equipped with a rate gyro, the angular velocity is calculated using the inertial group and the rigid body motion signal is filtered out through a high-pass filter.

3. The launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method according to claim 1 is characterized in that: A bandpass filter is used to filter the identification signal to remove the rigid body motion information. The form of the bandpass filter is as follows: in, B=ω high -ω low ,ω low The lower boundary frequency of the reserved band for bandpass filtering, ω high is the upper boundary frequency of the band reserved for bandpass filtering, and s is a complex frequency domain variable of pull-type variation.

4. The launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method according to claim 1, characterized in that: The recursive least squares algorithm with forgetting factor is used to update the parameters of the dual center frequency adaptive notch filter to obtain the two-order modal frequencies with the highest energy in the identification signal, including: (1) Initialize the cross-correlation vector, set z(0) = 0; initialize the correlation matrix, set Φ(0) = δI, where δ is the regularization parameter and I is the unit diagonal matrix; (2) According to the identification signal after filtering, the weight matrix is ​​updated and the correlation matrix Φ(n) is input. The recursive formula of the correlation matrix Φ(n) is: Wherein, λ is an exponentially weighted forgetting factor, and λ is a number close to 1 but less than 1; x(n), x(n-1), x(n-2), and x(n-3) are the outputs of the pole part of the filter at the nth, n-1th, n-2th, and n-3th beats, respectively; (3) According to the identification signal after filtering, the cross-correlation vector z(n) between the weight input and the expected response is updated. The recursive update formula of the cross-correlation vector z(n) is: Among them, x(n-4) is the output of the filter pole part of the n-4th beat; (4) Update the recursive least squares weight vector according to the correlation matrix Φ(n) and the cross-correlation vector z(n) The update formula is: (5) According to the recursive least squares weight vector Calculate the characteristic parameters of the notch filter a1(n), a2(n), the calculation formula is: Then the second-order modal frequencies identified by the filter are obtained, and the expressions are as follows: Where ΔT is the sampling time.

5. The launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method according to claim 4 is characterized in that: Satisfy at least one of the following: The regularization parameter δ is related to the signal-to-noise ratio. It takes a small value when the signal-to-noise ratio is high and a large value when the signal-to-noise ratio is low. The value of δ is 1×10 -4 ; The smaller the exponential weighted forgetting factor λ is, the faster the forgetting is and the smoother the recognition frequency is. The value of λ is 0.95~0.99; The sampling time ΔT is the control period 0.02s.

6. The launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method according to claim 1, characterized in that: The vibration level is determined by using the output value y of the spectrum damper s Or use frequency identification to identify the amplitude of the input signal |y(n)|.

7. The launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method according to claim 1, characterized in that: The reference signal is generated by using the updated or maintained two-order modal frequencies using the following formula: Where d(n) is the reference signal at the nth time step, A is the reference signal amplitude, is the updated or maintained two-order modal frequency, t is the time, ΔT is the sampling time; δ is the sampling pulse function.

8. The launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stabilization control method according to claim 7 is characterized in that: Generating an elastic compensation signal according to the reference signal using a minimum mean square error adaptive filtering algorithm includes: (1) Define the weight vector Where M is the filter order, let the initial weight vector (2) Calculate the estimated error e(n) between the input signal and the output signal: e(n)=u(n)-δ ek (n) Where e(n) is the error signal at the nth time step, δ ek (n) is the frequency of the nth time step. The k-th order elastic compensation signal of , u(n) is the residual elastic signal of the identification signal after filtering minus the information filtered by the dual center frequency filter, and is the M×1 input vector of the n-th time step; (3) According to the weight vector And the error signal e(n), calculate the recursive weight vector under stochastic gradient descent, the weight vector recursion formula is: w(n+1)=w(n)+μu(n)e(n) Among them, μ is the calculation step size; (4) According to the reference signal d(n), calculate the filter output at the n+1th time step: δ ek (n+1)=w T (n)d(n) Among them, δ ek (n+1) is the frequency of the n+1th time step. The compensation signal of the k-th order elasticity; T is the transpose.

9. A launch vehicle ultra-low frequency elastic adaptive fault-tolerant attitude stability control system, characterized in that: include: An information removal module removes rigid body motion information from the identification signal; The filtering module performs filtering processing on the identification signal after the rigid body motion information is removed, filters out the low-frequency components and high-frequency components, and retains the information within the required frequency band; The modal frequency acquisition and adaptive filtering module updates the parameters of the dual-center frequency adaptive notch filter using a recursive least squares algorithm with a forgetting factor for the identification signal after the filtering process, so as to obtain the two-order modal frequencies with the highest energy in the identification signal; A frequency updating or maintaining module is used to update or maintain the two-order modal frequencies, in the following way: when the vibration magnitude is greater than the required value and the rate of change of the modal frequency is less than the required value, the notch filter adopts the identification frequency of the current beat; otherwise, the notch filter adopts the identification frequency of the previous beat; A compensation signal generating module, which generates a reference signal using the updated or maintained second-order modal frequencies, and generates an elastic compensation signal using a minimum mean square error adaptive filtering algorithm according to the reference signal; The signal compensation module compensates the elastic compensation signal into the control swing angle instruction to realize the adaptive fault-tolerant attitude stability control of the rocket body.

10. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method of claim 1.

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