Aircraft elastic vibration suppression method and system based on strong tracking Kalman filtering

Abstract

The invention discloses an aircraft elastic vibration suppression method and system based on strong tracking Kalman filtering, and the method comprises the steps: obtaining an angular acceleration estimation value according to preset gyro data; obtaining a system model of the angular velocity and the elastic frequency according to the angular acceleration estimated value; obtaining a rigid body angular velocity estimation result according to the angular velocity and elastic frequency system model; and obtaining a rudder deflection instruction according to the rigid body angular velocity estimation result. According to the method, the high-frequency signal suppression capability is improved, and the robustness of a stable control system is enhanced.

Description

Technical Field

[0001] The invention belongs to the technical field of aircraft stability control, and in particular relates to an aircraft elastic vibration suppression method and system based on strong tracking Kalman filtering. Background Art

[0002] During actual flight, the fiber optic gyroscope usually deviates from the center of mass of the missile body, and the angular velocity measurement value it senses consists of two parts: the rigid body angular velocity and the elastic interference angular velocity. If the elastic mode of the missile body is excited without filtering, the high-frequency signal caused by the elastic deformation will be solved through the control loop and directly reflected in the rudder command. For electric servos, after the rudder system responds to high-frequency signals for a long time, the current increases significantly, which may cause the power supply on the missile to be exhausted / irreversible damage to the servo. Therefore, an elastic suppression control strategy needs to be adopted to smooth the angular velocity input into the control loop solution.

[0003] Under the existing technology, two or three broadband notch filters are usually connected in series in the control loop to accommodate the elastic frequency differences between ground modal tests and actual flight tests, which will sacrifice a certain amplitude and phase margin and reduce the robustness of the stability control system. In addition, when unknown modes appear, the elastic suppression ability of the notch filter is often limited, which worsens the working condition of the rudder system and may cause instability in severe cases. Summary of the invention

[0004] The technical problem solved by the present invention is: to overcome the deficiencies of the prior art, to provide a method and system for suppressing elastic vibration of an aircraft based on a strong tracking Kalman filter, to improve the ability to suppress high-frequency signals, and to enhance the robustness of a stable control system.

[0005] The purpose of the present invention is achieved through the following technical scheme: a method for suppressing elastic vibration of an aircraft based on strong tracking Kalman filtering, comprising: obtaining an angular acceleration estimate according to preset gyroscope data; obtaining a system model of angular velocity and elastic frequency according to the angular acceleration estimate; obtaining a rigid body angular velocity estimate result according to the system model of angular velocity and elastic frequency; and obtaining a rudder deviation instruction according to the rigid body angular velocity estimate result.

[0006] In the above-mentioned aircraft elastic vibration suppression method based on strong tracking Kalman filtering, the angular acceleration estimation value is obtained by the following formula:

[0007]

[0008] in, is the estimated value of angular acceleration at the current sampling moment, T is the time step, k is the current sampling number, ω m (kT-T) is the angular velocity measurement value at the previous sampling moment, ω m (kT-2T) is the angular velocity measurement value at the first two sampling moments.

[0009] In the above-mentioned aircraft elastic vibration suppression method based on strong tracking Kalman filtering, the system model of angular velocity and elastic frequency is obtained by the following formula:

[0010]

[0011] Among them, X(kT) is the state quantity at the current sampling time, X(kT-T) is the state quantity at the previous sampling time, T is the time step, k is the current sampling number, Φ(kT) is the state transfer matrix at the current sampling time, Ψ(kT) is the control transfer matrix at the current sampling time, is the estimated value of angular acceleration at the current sampling moment, ω m (kT) is the angular velocity measurement value at the current sampling moment, ζ(kT) is the observation noise at the current sampling moment, B is the manipulation matrix, C is the observation matrix, ω1 is the first-order elastic deformation modal frequency, ω2 is the second-order elastic deformation modal frequency, I represents the unit diagonal matrix, the superscript “T” represents the transpose, the superscript “i” represents the i-th power, “!” represents the factorial, and “Σ” represents the sum.

[0012] In the above-mentioned method for suppressing elastic vibration of an aircraft based on a strong tracking Kalman filter, the rigid body angular velocity estimation result is obtained according to the system model of angular velocity and elastic frequency, including: obtaining the state quantity prediction value at the current sampling moment according to the system model of angular velocity and elastic frequency and the preset state quantity update value at the previous sampling moment; obtaining the prediction error covariance matrix according to the preset fading factor and the input noise matrix at the previous sampling moment; obtaining the Kalman filter gain according to the prediction error covariance matrix; and obtaining the rigid body angular velocity estimation result according to the Kalman filter gain and the state quantity prediction value at the current sampling moment.

[0013] In the above-mentioned aircraft elastic vibration suppression method based on strong tracking Kalman filtering, the state quantity prediction value at the current sampling time is It is obtained by the following formula:

[0014]

[0015] Where T is the time step, k is the current sampling number, is the state update value of the previous sampling time, Φ(kT) is the state transfer matrix of the current sampling time, Ψ(kT) is the control transfer matrix of the current sampling time, is the estimated value of angular acceleration at the current sampling moment.

[0016] In the above-mentioned aircraft elastic vibration suppression method based on strong tracking Kalman filtering, the prediction error covariance matrix is ​​obtained by the following formula:

[0017]

[0018] in, is the prediction error covariance matrix at the current sampling time, T is the time step, k is the current sampling number, and n is , Q(kT-T) is the input noise matrix at the previous sampling moment, K(kT-T) is the Kalman filter gain at the previous sampling moment, Update the error covariance matrix for the previous sampling moment, is the prediction error covariance matrix of the previous sampling time, λ(kT-T) is the fading factor of the previous sampling time, ω m (kT-T) is the angular velocity measurement value at the previous sampling moment, C is the observation matrix, is the predicted value of the state quantity at the previous sampling time, Ψ(kT) is the control transfer matrix at the current sampling time, Ψ(kT-T) is the control transfer matrix at the previous sampling time, R is the observation noise matrix, Φ(kT) is the state transfer matrix at the current sampling time, Φ(kT-T) is the state transfer matrix at the previous sampling time, I represents the unit diagonal matrix, is the variance estimate of the new information d(kT-T) at the previous sampling moment. The superscript “T” represents transposition, the superscript “2” represents square, and “Tr(·)” represents the sum of the diagonal elements of the calculation matrix.

[0019] In the above-mentioned aircraft elastic vibration suppression method based on strong tracking Kalman filter, the Kalman filter gain is obtained by the following formula:

[0020]

[0021] Among them, K(kT) is the Kalman filter gain at the current sampling moment, T is the time step, k is the current sampling number, is the prediction error covariance matrix at the current sampling moment, C is the observation matrix, and R is the observation noise matrix.

[0022] In the above-mentioned aircraft elastic vibration suppression method based on strong tracking Kalman filtering, the rigid body angular velocity estimation result is obtained by the following formula:

[0023]

[0024] Among them, ω(kT) is the rigid body angular velocity estimation result, is the state update value, T is the time step, k is the current sampling number, K(kT) is the Kalman filter gain at the current moment, is the predicted value of the state quantity, ω m (kT) is the angular velocity measurement value at the current sampling moment, and C is the observation matrix.

[0025] An aircraft elastic vibration suppression system based on strong tracking Kalman filtering includes: a first module, used to obtain an angular acceleration estimation value according to preset gyro data; a second module, used to obtain a system model of angular velocity and elastic frequency according to the angular acceleration estimation value; a third module, used to obtain a rigid body angular velocity estimation result according to the system model of angular velocity and elastic frequency; and a fourth module, used to obtain a rudder deviation instruction according to the rigid body angular velocity estimation result.

[0026] An electronic device comprises: a memory for storing computer-readable instructions; and a processor for running the computer-readable instructions to execute an aircraft elastic vibration suppression method based on strong tracking Kalman filtering.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] (1) The present invention avoids the problem of insufficient stability margin of the stability control system caused by the notch filter by setting up a Kalman filter to extract the angular velocity information of the rigid body.

[0029] (2) The present invention introduces the idea of ​​strong tracking filtering, so that the dynamic characteristics of the built-in system model of the filter are closer to the actual situation, thereby improving the ability to suppress high-frequency signals.

[0030] (3) The present invention introduces a method for adaptively updating input noise characteristics to suppress filter divergence caused by delaying the input angular acceleration by one sampling moment, thereby enhancing the robustness of the filter itself. The method is applicable to situations where the input quantity contains a certain degree of colored noise, and can suppress the elastic vibration of the aircraft when there is a certain difference in elastic frequency between the ground modal test and the actual flight test, or when an unknown mode occurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0032] Figure 1 It is a control principle block diagram of the pitch / yaw / roll channel provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0034] A Kalman filter is set up to extract the angular velocity information of the aircraft rigid body from the angular velocity measurement value, and a control loop is introduced to replace the output of the notch filter, which can avoid the problem of insufficient stability margin of the stability control system caused by the notch filter. On the basis of the traditional extended Kalman filter, the idea of ​​strong tracking filtering is introduced to adaptively adjust the memory length of the Kalman filter according to the current observation data, that is, the efficiency of using old observation data, so that the dynamic characteristics of the system can be fully reflected by the current observation information; the method of adaptively updating the input noise characteristics is introduced to suppress the filter divergence caused by the delayed input of angular acceleration and colored noise, and the elastic suppression can be better achieved in the case of differences between the actual flight test elastic frequency and the ground modal test results and the appearance of unknown modes.

[0035] This embodiment provides a method for suppressing elastic vibration of an aircraft based on a strong tracking Kalman filter, the method comprising:

[0036] Obtaining an estimated value of angular acceleration according to preset gyro data;

[0037] The system model of angular velocity and elastic frequency is obtained according to the estimated value of angular acceleration;

[0038] The rigid body angular velocity estimation result is obtained based on the system model of angular velocity and elastic frequency;

[0039] The rudder deflection command is obtained according to the rigid body angular velocity estimation result.

[0040] This embodiment ensures that elastic vibration is suppressed when there is a difference between the actual flight test elastic frequency and the ground modal test results and when unknown modes appear. This technical solution extracts rigid body angular velocity information from the gyro output by setting up a Kalman filter, while introducing a strong tracking idea and a method for adaptively updating input noise characteristics, thereby improving the ability to suppress high-frequency signals and enhancing the robustness of the stability control system.

[0041] Specifically, obtain the real-time gyroscope data and calculate the estimated angular acceleration, specifically:

[0042]

[0043] in, is the estimated value of angular acceleration at the current sampling moment, T is the time step, k is the current sampling number, ω m (kT-T) is the angular velocity measurement value at the previous sampling moment, ω m (kT-2T) is the angular velocity measurement value at the first two sampling moments.

[0044] Model the system for angular velocity and elastic frequency:

[0045] The design of stable control systems needs to pay attention to the first-order and second-order elastic deformations. The elastic disturbance angular velocity v(kT) at the current sampling moment can be expressed as the sum of sinusoidal signals of two frequencies.

[0046] v(kT)=v1(kT)+v3(kT)=b1sin(ω1kT+φ1)+b2sin(ω2kT+φ2)

[0047] Where T is the time step, k is the current sampling number, v1(kT) and v3(kT) are the elastic disturbance angular velocities caused by the first-order and second-order elastic deformation at the current sampling moment, b1 and b2 are the amplitudes of the first-order and second-order elastic disturbance angular velocities, φ1 and φ2 are the phases of the first-order and second-order elastic disturbance angular velocities, ω1 and ω2 are the first-order and second-order modal frequencies, respectively.

[0048] Set the state quantity X(kT) to:

[0049] X(kT)=[ω(kT)v1(kT)v2(kT)v3(kT)v4(kT)] T

[0050] Where ω(kT) is the angular velocity of the rigid body at the current sampling moment, T is the time step, k is the current sampling number, v1(kT) and v3(kT) are the elastic disturbance angular velocities caused by the first-order and second-order elastic deformations at the current sampling moment, b1 and b2 are the amplitudes of the first-order and second-order elastic disturbance angular velocities, φ1 and φ2 are the phases of the first-order and second-order elastic disturbance angular velocities, ω1 and ω2 are the first-order and second-order modal frequencies, v2(kT) and v4(kT) are the first-order derivatives of v1(kT) and v3(kT) with respect to time, and the superscript “T” indicates transposition.

[0051] Establish a system model that takes into account input noise and observation noise:

[0052]

[0053] Among them, X(kT) is the state quantity at the current sampling time, X(kT-T) is the state quantity at the previous sampling time, T is the time step, k is the current sampling number, Φ(kT) is the state transfer matrix at the current sampling time, Ψ(kT) is the control transfer matrix at the current sampling time, is the estimated value of angular acceleration at the current sampling time, which contains input noise w(kT), ω m (kT) is the angular velocity measurement value at the current sampling moment, ζ(kT) is the observation noise at the current sampling moment, B is the manipulation matrix, C is the observation matrix, ω1 is the first-order elastic deformation modal frequency, ω2 is the second-order elastic deformation modal frequency, I represents the unit diagonal matrix, the superscript “T” represents the transpose, the superscript “i” represents the i-th power, “!” represents the factorial, and “Σ” represents the sum.

[0054] On the basis of the constructed system model, the idea of ​​strong tracking filtering and the method of adaptively updating input noise characteristics are combined with the traditional extended Kalman filter to obtain the rigid body angular velocity estimation result and introduce a stable control loop.

[0055] The rigid body angular velocity estimation results obtained based on the system model of angular velocity and elastic frequency include:

[0056] The predicted value of the state quantity at the current sampling moment is obtained according to the system model of the angular velocity and the elastic frequency and the preset state quantity update value at the previous sampling moment;

[0057] The prediction error covariance matrix is ​​obtained according to the preset fading factor and the input noise matrix;

[0058] The Kalman filter gain is obtained according to the prediction error covariance matrix;

[0059] The rigid body angular velocity estimation result is obtained based on the Kalman filter gain and the predicted value of the state quantity at the current sampling moment.

[0060] The predicted value of the state quantity at the current sampling time It is obtained by the following formula:

[0061]

[0062] Where T is the time step, k is the current sampling number, is the state update value of the previous sampling time, Φ(kT) is the state transfer matrix of the current sampling time, Ψ(kT) is the control transfer matrix of the current sampling time, is the estimated value of angular acceleration at the current sampling moment.

[0063] The idea of ​​strong tracking filtering is introduced, and the prediction error covariance matrix is ​​calculated based on the fading factor and the input noise matrix. Specifically, it is:

[0064]

[0065] in, is the prediction error covariance matrix at the current sampling time, T is the time step, k is the current sampling number, and n is , Q(kT-T) is the input noise matrix at the previous sampling moment, K(kT-T) is the Kalman filter gain at the previous sampling moment, Update the error covariance matrix for the previous sampling moment, is the prediction error covariance matrix of the previous sampling time, λ(kT-T) is the fading factor of the previous sampling time, ω m (kT-T) is the angular velocity measurement value at the previous sampling moment, C is the observation matrix, is the predicted value of the state quantity at the previous sampling moment, Ψ(kT) is the control transfer matrix at the current sampling moment, Ψ(kT-T) is the control transfer matrix at the previous sampling moment, R is the observation noise matrix, Φ(kT) is the state transfer matrix at the current sampling moment, Φ(kT-T) is the state transfer matrix at the previous sampling moment, I represents the unit diagonal matrix, superscript "T" represents transpose, superscript "2" represents square, and "Tr(·)" represents the sum of the diagonal elements of the calculation matrix.

[0066] In the formula, is the variance estimate of the new information d(kT-T) at the previous sampling moment, specifically:

[0067]

[0068] Among them, T is the time step, k is the current sampling number, ω m (kT-T) is the angular velocity measurement value at the previous sampling moment, C is the observation matrix, is the predicted value of the state quantity at the previous sampling moment, is the estimated value of the variance of the new information at the previous two sampling moments, W is the length of the sliding estimation window, and “Σ” means summation.

[0069] The Kalman filter gain is obtained by the following formula:

[0070]

[0071] Among them, K(kT) is the Kalman filter gain at the current sampling moment, T is the time step, k is the current sampling number, is the prediction error covariance matrix at the current sampling moment, C is the observation matrix, and R is the observation noise matrix.

[0072] The rigid body angular velocity estimation result is obtained by the following formula:

[0073]

[0074] Among them, ω(kT) is the rigid body angular velocity estimation result, is the state update value, T is the time step, k is the current sampling number, K(kT) is the Kalman filter gain at the current moment, is the predicted value of the state quantity, ω m (kT) is the angular velocity measurement value at the current sampling moment, and C is the observation matrix.

[0075] According to the estimated value of the rigid body angular velocity, the rudder deflection command is calculated, the rudder surface is driven to deflect, and the control force and control torque are applied to stabilize the missile attitude.

[0076] This method is explained by taking the pitch channel control of a certain aircraft as an example. The control principle diagram of the pitch channel is as follows: Figure 1 As shown, the specific steps are described below:

[0077] 1. Obtain real-time gyro data, and the gyro outputs the pitch angular velocity measurement value. Considering that the gyro output is contaminated by elastic vibration and the angular acceleration needs to be estimated based on the gyro output, the present invention regards the angular acceleration caused by elastic vibration as colored noise, and delays one sampling moment to estimate the angular acceleration to approximately meet the basic assumption of the Kalman filter on noise. The formula is as follows:

[0078]

[0079] In the formula, is the estimated value of the pitch acceleration at the current sampling moment, T is the time step, k is the current sampling number, ω m (kT-T) is the pitch angular velocity measurement value at the previous sampling moment, ω m (kT-2T) is the pitch angular velocity measurement value at the first two sampling moments.

[0080] 2. Take the estimated value of pitch angular acceleration calculated in step 1 as input and establish the system model of rigid body angular velocity and elastic frequency in the pitch channel. Set the state quantity X to:

[0081]

[0082] Where ω(kT) is the estimated value of the pitch rigid body angular velocity at the current sampling moment, v1(kT) is the elastic disturbance pitch angular velocity caused by the first-order elastic deformation at the current sampling moment, v2(kT) is the first-order derivative of v1(kT) with respect to time at the current sampling moment, v3(kT) is the elastic disturbance pitch angular velocity caused by the second-order elastic deformation at the current sampling moment, v4(kT) is the first-order derivative of v3(kT) with respect to time at the current sampling moment, b1 and b2 are the amplitudes of the first-order and second-order elastic disturbance pitch angular velocities, φ1 and φ2 are the phases of the first-order and second-order elastic disturbance pitch angular velocities, ω1 and ω2 are the first-order and second-order modal frequencies, and the superscript “T” indicates transposition.

[0083] The system model is as follows:

[0084]

[0085] Among them, X(kT) is the state quantity at the current sampling time, X(kT-T) is the state quantity at the previous sampling time, T is the time step, k is the current sampling number, Φ(kT) is the state transfer matrix at the current sampling time, Ψ(kT) is the control transfer matrix at the current sampling time, is the estimated value of angular acceleration at the current sampling time, which contains input noise w(kT), ω m (kT) is the angular velocity measurement value at the current sampling moment, ζ(kT) is the observation noise at the current sampling moment, B is the manipulation matrix, C is the observation matrix, ω1 is the first-order elastic deformation modal frequency, ω2 is the second-order elastic deformation modal frequency, I represents the unit diagonal matrix, the superscript “T” represents the transpose, the superscript “i” represents the i-th power, “!” represents the factorial, and “Σ” represents the sum.

[0086] The settings of ω1 and ω2 can refer to the ground modal test results.

[0087] 3. Based on the established system model, the idea of ​​strong tracking filtering and the method of adaptively updating the input noise characteristics are combined with the traditional extended Kalman filter to obtain the rigid body pitch angular velocity estimation result and introduce a stable control loop.

[0088] Set initial parameters:

[0089]

[0090] In the formula, Indicates the updated value of the state quantity at the initial moment, Represents the updated error covariance matrix at the initial moment, and I represents the unit diagonal matrix.

[0091] Update the value according to the state quantity at the previous sampling moment Predict the state value at the current sampling time Specifically:

[0092]

[0093] Where T is the time step, k is the current sampling number, is the state update value of the previous sampling time, Φ(kT) is the state transfer matrix of the current sampling time, Ψ(kT) is the control transfer matrix of the current sampling time, It is the estimated value of pitch angle acceleration at the current sampling moment.

[0094] The idea of ​​strong tracking filtering and the method of adaptively updating the input noise characteristics are introduced to adjust the prediction of the error covariance matrix. The formula is as follows:

[0095]

[0096] Among them, λ(kT-T) is a fading factor, and its initial value can be selected as 1; the initial value of Q and the setting of R can refer to the historical flight test data, and Q will be updated adaptively in the future. is the variance estimate of the new information d(kT-T) at the previous sampling moment, specifically:

[0097]

[0098] Among them, T is the time step, k is the current sampling number, ω m (kT-T) is the pitch angular velocity measurement value at the previous sampling moment, C is the observation matrix, is the predicted value of the state quantity at the previous sampling moment, is the estimated value of the variance of the new information at the first two sampling moments, and "Σ" represents the sum. W is the length of the sliding estimation window, which needs to be determined by a compromise between sensitivity and accuracy based on the actual dynamic range. When W is small, the filter is more sensitive to changes in the dynamic characteristics of the system, but the estimation accuracy of the input noise characteristics is low, which can easily lead to filter divergence; when W is large, the filter sensitivity is low and the accuracy is high.

[0099] Calculate the Kalman filter gain and estimate the rigid body pitch angular velocity, specifically:

[0100]

[0101] Among them, ω(kT) is the rigid body pitch angular velocity estimation result, is the state update value, T is the time step, k is the current sampling number, K(kT) is the Kalman filter gain at the current moment, is the predicted value of the state quantity, ω m (kT) is the pitch angular velocity measurement value at the current sampling moment, and C is the observation matrix.

[0102] 4. According to the estimated value of the rigid body pitch angular velocity obtained in step 3, the pitch channel rudder deflection instruction is calculated, the pitch channel rudder surface is driven to deflect, and the control force and control torque are applied to stabilize the missile pitch attitude.

[0103] This embodiment also provides an aircraft elastic vibration suppression system based on strong tracking Kalman filtering, including: a first module, used to obtain an angular acceleration estimate based on preset gyroscope data; a second module, used to obtain a system model of angular velocity and elastic frequency based on the angular acceleration estimate; a third module, used to obtain a rigid body angular velocity estimate result based on the system model of angular velocity and elastic frequency; and a fourth module, used to obtain a rudder deviation instruction based on the rigid body angular velocity estimate result.

[0104] This embodiment also provides an electronic device, including: a memory: used to store computer-readable instructions; and a processor: used to run the computer-readable instructions to execute an aircraft elastic vibration suppression method based on strong tracking Kalman filtering.

[0105] This embodiment avoids the problem of insufficient stability margin of the stability control system caused by the notch filter by setting up a Kalman filter to extract the rigid body angular velocity information. This embodiment introduces the idea of ​​strong tracking filtering, so that the dynamic characteristics of the built-in system model of the filter are closer to the actual situation, and the ability to suppress high-frequency signals is improved. This embodiment introduces a method for adaptively updating the input noise characteristics to suppress the filter divergence caused by delaying the input angular acceleration by one sampling moment, and enhance the robustness of the filter itself; it is suitable for situations where the input contains a certain degree of colored noise, and can suppress the elastic vibration of the aircraft when there is a certain difference in the elastic frequency between the ground modal test and the actual flight test or when an unknown mode appears.

[0106] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for suppressing aircraft elastic vibration based on strong tracking Kalman filtering, characterized in that include: Obtaining an estimated value of angular acceleration according to preset gyro data; The system model of angular velocity and elastic frequency is obtained according to the estimated value of angular acceleration; The rigid body angular velocity estimation result is obtained based on the system model of angular velocity and elastic frequency; The rudder deflection command is obtained according to the rigid body angular velocity estimation result.

2. The method for suppressing aircraft elastic vibration based on strong tracking Kalman filtering according to claim 1, characterized in that: The estimated angular acceleration is obtained by the following formula: in, is the estimated value of angular acceleration at the current sampling moment, T is the time step, k is the current sampling number, ω m (kT-T) is the angular velocity measurement value at the previous sampling moment, ω m (kT-2T) is the angular velocity measurement value at the first two sampling moments.

3. The method for suppressing aircraft elastic vibration based on strong tracking Kalman filtering according to claim 1, characterized in that: The system model of angular velocity and elastic frequency is obtained by the following formula: Among them, X(kT) is the state quantity at the current sampling time, X(kT-T) is the state quantity at the previous sampling time, T is the time step, k is the current sampling number, Φ(kT) is the state transfer matrix at the current sampling time, Ψ(kT) is the control transfer matrix at the current sampling time, is the estimated value of angular acceleration at the current sampling moment, ω m (kT) is the angular velocity measurement value at the current sampling moment, ζ(kT) is the observation noise at the current sampling moment, B is the manipulation matrix, C is the observation matrix, ω1 is the first-order elastic deformation modal frequency, ω2 is the second-order elastic deformation modal frequency, I represents the unit diagonal matrix, the superscript "T" represents the transpose, the superscript "i" represents the i-th power, "!" represents the factorial, and "Σ" represents the sum.

4. The method for suppressing aircraft elastic vibration based on strong tracking Kalman filtering according to claim 1, characterized in that: The rigid body angular velocity estimation results obtained based on the system model of angular velocity and elastic frequency include: The predicted value of the state quantity at the current sampling moment is obtained according to the system model of the angular velocity and the elastic frequency and the preset state quantity update value at the previous sampling moment; The prediction error covariance matrix is ​​obtained according to the preset fading factor and the input noise matrix at the previous sampling moment; The Kalman filter gain is obtained according to the prediction error covariance matrix; The rigid body angular velocity estimation result is obtained based on the Kalman filter gain and the predicted value of the state quantity at the current sampling moment.

5. The method for suppressing aircraft elastic vibration based on strong tracking Kalman filtering according to claim 4, characterized in that: The predicted value of the state quantity at the current sampling time It is obtained by the following formula: Where T is the time step, k is the current sampling number, is the state update value of the previous sampling time, Φ(kT) is the state transfer matrix of the current sampling time, Ψ(kT) is the control transfer matrix of the current sampling time, is the estimated value of angular acceleration at the current sampling moment.

6. The method for suppressing aircraft elastic vibration based on strong tracking Kalman filtering according to claim 4, characterized in that: The prediction error covariance matrix is ​​obtained by the following formula: in, is the prediction error covariance matrix at the current sampling time, T is the time step, k is the current sampling number, and n is , Q(kT-T) is the input noise matrix at the previous sampling moment, K(kT-T) is the Kalman filter gain at the previous sampling moment, Update the error covariance matrix for the previous sampling moment, is the prediction error covariance matrix of the previous sampling time, λ(kT-T) is the fading factor of the previous sampling time, ω m (kT-T) is the angular velocity measurement value at the previous sampling moment, C is the observation matrix, is the predicted value of the state quantity at the previous sampling time, Ψ(kT) is the control transfer matrix at the current sampling time, Ψ(kT-T) is the control transfer matrix at the previous sampling time, R is the observation noise matrix, Φ(kT) is the state transfer matrix at the current sampling time, Φ(kT-T) is the state transfer matrix at the previous sampling time, I represents the unit diagonal matrix, is the variance estimate of the innovation d(kT-T) at the previous sampling moment. The superscript "T" indicates transposition, the superscript "2" indicates square, and "Tr(·)" indicates the sum of the diagonal elements of the calculation matrix.

7. The method for suppressing aircraft elastic vibration based on strong tracking Kalman filtering according to claim 4, characterized in that: The Kalman filter gain is obtained by the following formula: Among them, K(kT) is the Kalman filter gain at the current sampling moment, T is the time step, k is the current sampling number, is the prediction error covariance matrix at the current sampling moment, C is the observation matrix, and R is the observation noise matrix.

8. The method for suppressing aircraft elastic vibration based on strong tracking Kalman filtering according to claim 4, characterized in that: The rigid body angular velocity estimation result is obtained by the following formula: Among them, ω(kT) is the rigid body angular velocity estimation result, is the state update value, T is the time step, k is the current sampling number, K(kT) is the Kalman filter gain at the current moment, is the predicted value of the state quantity, ω m (kT) is the angular velocity measurement value at the current sampling moment, and C is the observation matrix.

9. An aircraft elastic vibration suppression system based on strong tracking Kalman filtering, characterized in that include: The first module is used to obtain an angular acceleration estimation value according to preset gyro data; The second module is used to obtain a system model of angular velocity and elastic frequency according to the estimated value of angular acceleration; The third module is used to obtain the rigid body angular velocity estimation result according to the system model of angular velocity and elastic frequency; The fourth module is used to obtain the rudder deviation instruction according to the rigid body angular velocity estimation result.

10. An electronic device, characterized in that: include: Memory: used to store computer-readable instructions; and Processor: used to run the computer-readable instructions to perform the method according to any one of claims 1 to 8.

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