Elastic frequency identification and elastic vibration suppression method for aircraft with large length-diameter ratio

Abstract

The invention discloses an elastic frequency identification and elastic vibration suppression method for an aircraft with a large length-diameter ratio, and the method comprises the steps: carrying out the real-time online elastic frequency identification of angular velocity information collected by an inertial navigation device, and obtaining a current identification value omega 1 of an elastic vibration frequency; correcting the identification result based on the omega1 to obtain an iteration correction value omegab of frequency identification; then combining prior information of a ground modal test to obtain a prior value omega a of the elastic vibration frequency, weighting the omega a and the omega a to obtain an acquisition value omega of the elastic vibration frequency, designing a notch filter structure based on the omega, and enabling filter parameters to be automatically adjusted along with the change of the elastic frequency; according to the invention, the elastic frequency can be identified with high precision, and the elastic vibration can be well suppressed.

Description

Technical Field

[0001] The present invention belongs to the technical field of aircraft control, and in particular relates to a method for elastic frequency identification and elastic vibration suppression of an aircraft with a large aspect ratio. Background Art

[0002] To reduce drag, increase maximum speed, and extend range, aircraft designs tend to feature large aspect ratios. Aircraft with an aspect ratio greater than 20 are generally referred to as high-aspect-ratio aircraft. Furthermore, to increase engine charge and payload capacity, aircraft designs often incorporate lightweight composite materials, resulting in more pronounced elastic characteristics. During flight, the aircraft's inertial navigation system (INS) is sensitive to additional signals caused by elastic vibrations. These additional signals enter the control loop, causing high-frequency oscillations in the servo, exacerbating wear.

[0003] At the same time, due to engine operation, changes in the flight environment, and the discrete nature of the aircraft manufacturing process, the aircraft's elastic vibration modes exhibit time-varying and uncertain characteristics. When these modes are close to the frequency of rigid body motion, they pose difficulties in notch filter design. If the filter notch width is insufficient, the aircraft's elastic vibration frequency may exceed the notch range, causing filter failure. If the filter notch width is too large, the resulting phase delay is large, affecting the stability of rigid body motion control. Summary of the Invention

[0004] In view of this, the present invention provides a method for elastic frequency identification and elastic vibration suppression of a large aspect ratio aircraft, which can identify the elastic frequency with high precision and effectively suppress the elastic vibration.

[0005] The technical solutions for implementing the present invention are as follows:

[0006] A method for elastic frequency identification and elastic vibration suppression of a large aspect ratio aircraft is proposed. The angular velocity information collected by the inertial navigation device is subjected to real-time online elastic frequency identification to obtain the current identification value ω1 of the elastic vibration frequency. The identification result is corrected based on ω1 to obtain the iterative correction value ω of the frequency identification. b ; Then, combined with the prior information of the ground modal test, the prior value of the elastic vibration frequency ω is obtained a , for ω b and ω a The weighted confidence value ω of the elastic vibration frequency is obtained, and the notch filter structure is designed based on ω, so that the filter parameters are automatically adjusted as the elastic frequency changes.

[0007] Furthermore, a fast Fourier transform algorithm based on rolling time series is used to perform real-time online elastic frequency identification on the angular velocity information collected by the inertial navigation device.

[0008] Furthermore, the iterative correction value ω of frequency identification b for:

[0009]

[0010] ω2 is the average value of the first 10 frequency identification results, Δ t is the sampling step of the inertial navigation device, and k is the maximum change rate of the elastic frequency.

[0011] Furthermore, the prior value of the elastic vibration frequency ω a for:

[0012]

[0013] Among them, ω min is the elastic frequency value at full load, ω max is the elastic frequency value when no-load, M(t) is the theoretical value of the aircraft mass changing with the flight time t, interp1(.) is the one-dimensional linear interpolation function, M max is the mass of the aircraft when fully loaded, M min is the mass of the aircraft when it is unloaded, and t1 is the end time of the engine active phase.

[0014] Furthermore, the confidence value ω is specifically:

[0015] ω=(1-a)·ω a +a·ω b

[0016] a is the weight coefficient (0≤a≤1), and the value of a is as follows:

[0017]

[0018] Furthermore, the transfer function G of the notch filter is F (s) is:

[0019]

[0020] Where: s is the complex variable in Laplace transform, ω j is the center frequency of the notch filter, ξ j is the damping ratio of the notch filter.

[0021] Furthermore, the notch filter parameter adjustment strategy is: take ω j =ω, that is, the center frequency of the notch filter is selected as the final value of the elastic vibration frequency of the aircraft; in the active section of the flight process, ξ j It can be taken as 0.5~0.7 to increase the filter width; in the passive section, ξ j It can be set to 0.3-0.4 to reduce system latency.

[0022] The method of the present invention includes the following beneficial technical effects:

[0023] (1) The present invention uses fast Fourier transform to perform elastic frequency identification on the aircraft angular velocity information, and designs an iterative correction strategy to iteratively correct the current identification result.

[0024] (2) To improve the reliability of elastic frequency identification, the present invention combines data from static engine tests and ground modal tests to calculate a priori values ​​for the aircraft's elastic frequency. A weighting strategy for the priori values ​​and correction values ​​is proposed, improving the accuracy of elastic frequency identification.

[0025] (3) The present invention designs a notch filter structure and parameter adjustment scheme, and adaptively adjusts the notch filter parameters according to the flight status of the aircraft and the elastic frequency identification results to suppress the elastic vibration of the aircraft.

[0026] (4) The simulation experiments have shown that the method of the present invention has high frequency identification accuracy, little influence on the rigid motion of the aircraft, and good elastic vibration suppression effect. It solves the problem of adaptive suppression of elastic vibration under the ultra-wide elastic frequency variation range of large aspect ratio aircraft and ensures the stability of servo elasticity. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a control system block diagram of the elastic vibration frequency autonomous identification and adaptive suppression method of the present invention.

[0028] Figure 2 This is the flow chart of elastic frequency identification based on the fast Fourier algorithm of rolling time series.

[0029] Figure 3 This is a graph showing the actual frequency and identified frequency of the aircraft changing with time in the simulation test.

[0030] Figure 4 This is a graph showing the variation of identification error over time in the simulation experiment.

[0031] Figure 5 This is a comparison chart of the pitch angular velocity curves in the simulation test.

[0032] Figure 6 This is a comparison chart of the pitch rudder angle curve in the simulation experiment. DETAILED DESCRIPTION

[0033] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0034] During flight, aircraft with large aspect ratios are subject to structural elastic vibrations and unsteady aerodynamic forces. If improperly managed, these vibrations can induce phenomena such as elastic vibration or structural flutter. A traditional approach to addressing this problem involves designing a notch filter with fixed parameters based on predicted elastic information. However, to suppress all possible elastic frequencies, the filter width is relatively wide, resulting in significant phase lag. Therefore, the present invention designs a method for elastic frequency identification and elastic vibration suppression for aircraft with large aspect ratios. This method can quickly and accurately identify elastic vibration frequencies and update the filter parameters online in real time, achieving optimal elastic vibration suppression.

[0035] Figure 1 This is a control system block diagram for the present invention's autonomous identification and adaptive suppression of elastic vibration frequency. The present invention first utilizes a rolling time series-based fast Fourier transform algorithm to perform real-time online elastic frequency identification on the angular velocity information collected by the inertial navigation device. Then, incorporating prior information from ground modal tests, an iterative correction strategy based on this prior information is designed. Finally, a notch filter structure and parameter adjustment scheme are designed to automatically adjust the filter parameters as the elastic frequency changes.

[0036] like Figure 2 As shown, the specific steps of the method of the present invention are as follows:

[0037] Step 1: Elastic Vibration Frequency Identification

[0038] Step 1-1: Obtain the angular velocity data of the aircraft through the inertial navigation unit as the input signal for frequency identification;

[0039] Step 1-2: Store the input signal into the data buffer according to the first-in-first-out principle;

[0040] Step 1-3: Use Fast Fourier Transform to perform frequency identification on the input signal.

[0041] The formula for the fast Fourier transform is:

[0042]

[0043] Where N is the number of samples of the fast Fourier transform, n is the serial number of the discrete signal in the time domain (0≤n≤N-1), m is the serial number of the discrete signal in the frequency domain (0≤m≤N-1), x(n) is the time series of length N, and X(m) is the frequency domain signal in complex form obtained by fast Fourier transform.

[0044] Find the signal with the largest amplitude in X(m) and record its corresponding serial number as X p , then X p The corresponding frequency is the current identification value ω1 of the elastic vibration frequency.

[0045] The larger the number of samples N, the greater the amount of calculation of the onboard computer, the higher the resolution of frequency identification, and the greater the delay error caused by sampling; the smaller N is, the smaller the amount of calculation of the onboard computer, the lower the resolution of frequency identification, and the smaller the delay error caused by sampling.

[0046] Table 1 Theoretical identification resolution and sampling delay corresponding to different sampling numbers N

[0047] N 128 256 512 1024 Identification resolution / Hz 0.7813 0.3906 0.1953 0.0977 Sampling delay / s 0.32 0.64 1.28 2.56

[0048] During the active phase of the engine, the aircraft's elastic vibration frequency changes rapidly. To improve the speed of frequency identification, N can be set to 256. During the passive phase of the engine, the aircraft's mass remains unchanged, and the elastic vibration frequency changes less. Therefore, N can be set to 512 to improve identification accuracy.

[0049] Step 2: Design an iterative correction strategy based on prior information

[0050] Step 2-1: Combine previous experiments to obtain prior information on elastic vibration frequency;

[0051] During the operation of the engine, due to the continuous consumption of fuel, the mass of the aircraft continues to decrease, and the elastic frequency of the aircraft continues to increase. Assume that the mass of the aircraft when fully loaded is M max , the mass of the aircraft when empty is M min Then, combined with the engine thrust test data, the mass-second consumption is set to The theoretical value M(t) of the aircraft mass changing with flight time can be obtained as follows:

[0052]

[0053] Where t is the current flight time, and t1 is the end time of the engine active phase.

[0054] Then, according to the ground modal test, the first-order elastic frequency range of the aircraft is obtained. Assume that the elastic frequency value at full load is ω min , the elastic frequency value when no load is ω max The change trend of the aircraft's elastic vibration frequency is related to the change trend of its mass, and the prior value of the elastic vibration frequency ω can be obtained. a for:

[0055]

[0056] Where, interp1() function is a one-dimensional linear interpolation function.

[0057] Step 2-2: Based on the frequency identification value obtained in steps 1-3, the identification result is corrected to obtain an iterative correction value of the frequency identification;

[0058] The current identification value obtained through steps 1-3 is ω1, and the average value of the frequency identification results before steps 1-3 is ω2. The iterative correction formula is as follows:

[0059]

[0060] Where ω b is the elastic vibration frequency identification value after processing, where Δ t is the sampling step of the inertial navigation device, and k is the maximum change rate of the elastic frequency.

[0061] Step 2-3: Combine the prior values ​​and correction values ​​of steps 2-1 and 2-2, set the weight coefficient, and obtain the accepted value of the elastic vibration frequency.

[0062] The accepted value of elastic vibration frequency is:

[0063] ω=(1-a)·ω a +a·ω b

[0064] Where ω is the adopted value of elastic vibration frequency, a is the weight coefficient (0≤a≤1), and the value of a is as follows:

[0065]

[0066] Step 3: Notch filter structure design and parameter adjustment strategy

[0067] Step 3-1: Notch filter structure design

[0068] The transfer function G of the notch filter F (s) is:

[0069]

[0070] Where: s is the complex variable in Laplace transform, ω is the adopted value of elastic vibration in step 2-3, ω j is the center frequency of the notch filter, ζ j is the damping ratio of the notch filter.

[0071] Step 3-2: Notch filter parameter adjustment strategy

[0072] Take ω j =ω, that is, the center frequency of the notch filter is selected as the final value of the elastic vibration frequency of the aircraft. j The increase of ξ will increase the filter width and depth, but it will also increase the phase delay of rigid motion and slow down the response speed of the system. j It can be taken as 0.5~0.7 to increase the filter width; in the passive section, ξj It can be set to 0.3-0.4 to reduce system latency.

[0073] Example:

[0074] In order to verify the effect of the method of the present invention on elastic frequency identification and elastic vibration suppression, a six-degree-of-freedom model of a large aspect ratio aircraft is used as the basis, and the simulation step length of the system is set to 5ms and the sampling step length of the inertial navigation device is Δ t The maximum change rate of elastic frequency k is 5ms, and the maximum change rate of elastic frequency k is 1Hz / s. A noise signal with an amplitude of 10deg / s is applied to the pitch angular velocity, and the system is verified in combination with the theoretical elastic vibration frequency of the aircraft.

[0075] The a priori parameter values ​​of the aircraft can be obtained from the ground modal test, as shown in Table 2.

[0076] Table 2 Priori parameter values ​​of aircraft

[0077]

[0078] Combined with the engine thrust test data, the theoretical mass change of the aircraft in the active section is shown in Table 3.

[0079] Table 3 Changes of theoretical values ​​of aircraft mass over time

[0080]

[0081] Then use the formula in step 2-1:

[0082]

[0083] The relationship between the theoretical value of elastic vibration frequency and time can be calculated, as shown in Table 4.

[0084] Table 4 Changes of theoretical values ​​of elastic vibration frequency over time

[0085]

[0086] In the active section, the sampling value N is 256, and the notch filter damping ratio ξ j Take 0.6; in the passive section, the sampling value N is 512, and the notch filter damping ratio ξ j Take 0.3. N and ξ j The value can be selected by the designer according to the specific situation.

[0087] The simulation results show that the frequency identification method of the present invention has high accuracy and good elastic vibration suppression effect. The comparison curve of the theoretical elastic frequency and the identified elastic frequency over time is as follows: Figure 3 As shown in the figure. The elastic frequency identification error is as follows Figure 4The comparison curve of pitch angular velocity before and after elastic vibration suppression is shown in Figure 5 The comparison curve of the elastic vibration suppression before and after the pitch rudder angle is shown as follows. Figure 6 shown.

[0088] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for elastic frequency identification and elastic vibration suppression of a large aspect ratio aircraft, characterized in that: Perform real-time online elastic frequency identification on the angular velocity information collected by the inertial navigation device to obtain the current identification value ω1 of the elastic vibration frequency; based on ω1, correct the identification result to obtain the iterative correction value ω of the frequency identification b ; Then, combined with the prior information of the ground modal test, the prior value of the elastic vibration frequency ω is obtained a , for ω b and ω a The weighted confidence value ω of the elastic vibration frequency is obtained, and the notch filter structure is designed based on ω, so that the filter parameters are automatically adjusted as the elastic frequency changes.

2. The elastic frequency identification and elastic vibration suppression method for a large aspect ratio aircraft according to claim 1, characterized in that: The fast Fourier transform algorithm based on rolling time series is used to perform real-time online elastic frequency identification on the angular velocity information collected by the inertial navigation device.

3. The elastic frequency identification and elastic vibration suppression method for a large aspect ratio aircraft according to claim 1, characterized in that: Iterative correction value ω of frequency identification b for: ω2 is the average value of the first 10 frequency identification results, Δ t is the sampling step of the inertial navigation device, and k is the maximum change rate of the elastic frequency.

4. The elastic frequency identification and elastic vibration suppression method for a large aspect ratio aircraft according to claim 1 or 3, characterized in that: Prior value of elastic vibration frequency ω a for: Among them, ω min is the elastic frequency value at full load, ω max is the elastic frequency value when no-load, M(t) is the theoretical value of the aircraft mass changing with the flight time t, interp1(.) is the one-dimensional linear interpolation function, M max is the mass of the aircraft when fully loaded, M min is the mass of the aircraft when it is unloaded, and t1 is the end time of the engine active phase.

5. The elastic frequency identification and elastic vibration suppression method for a large aspect ratio aircraft according to claim 4, characterized in that: The specific acceptance value ω is: ω=(1-a)·ω a +a·ω b a is the weight coefficient (0≤a≤1), and the value of a is as follows:

6. The elastic frequency identification and elastic vibration suppression method for a large aspect ratio aircraft according to claim 5, characterized in that: The transfer function G of the notch filter F (s) is: Where: s is the complex variable in Laplace transform, ω j is the center frequency of the notch filter, ξ j is the damping ratio of the notch filter.

7. The elastic frequency identification and elastic vibration suppression method for a large aspect ratio aircraft according to claim 6, characterized in that: The notch filter parameter adjustment strategy is: take ω j =ω, that is, the center frequency of the notch filter is selected as the final value of the elastic vibration frequency of the aircraft; in the active section of the flight process, ξ j It can be taken as 0.5~0.7 to increase the filter width; in the passive section, ξ j It can be set to 0.3-0.4 to reduce system latency.

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