Intelligent suppression method for flywheel set micro-vibration of on-orbit satellite

Abstract

The invention discloses an intelligent suppression method for micro-vibration of a flywheel set of an in-orbit satellite. Relates to the technical field of spacecraft systems, in particular to the technical field of intelligent suppression of micro-vibration of flywheel sets of on-orbit satellites. The method effectively solves the problem that quantitative indexes of the vibration isolation effect are lacked in the temperature control process when the flywheel set rotating speed working condition coverage is small, and the calculation efficiency and precision are improved. The method comprises the following steps: extracting frequency domain vibration disturbance data of a flywheel at a specified rotating speed; acquiring the displacement response of the frequency domain of the sensitive component under unit disturbance vibration; acquiring the displacement response of the sensitive component in the time domain under the disturbance vibration of the flywheel group; calculating an actual image motion value; quantitative characterization of the performance of the vibration isolator under the working condition of the specified rotating speed is calculated through the actual image motion value; constructing and training a rotation speed working condition-vibration isolator performance proxy model; and the micro-vibration of the in-orbit satellite flywheel group is intelligently inhibited.

Description

Technical Field

[0001] The present invention relates to the technical field of spacecraft systems, and in particular to the technical field of intelligent suppression of micro-vibration of a flywheel assembly for an on-orbit satellite. Background Art

[0002] With the rapid development of space remote sensing technology, the resolution, pointing accuracy, attitude stability, etc. of remote sensing satellites are constantly improving, and the requirements for satellite micro-vibration suppression are becoming higher and higher. During the on-orbit operation phase, the flywheel (group) as the main attitude control actuator on the satellite generates small amplitude and wide bandwidth micro-vibration, which will cause the optical axis target pointing of sensitive components such as optical payloads to deviate, thus seriously affecting the payload imaging quality. Therefore, it is necessary to effectively suppress the micro-vibration disturbance generated by the flywheel to ensure the control accuracy and imaging quality of the optical remote sensing camera.

[0003] At present, vibration isolation devices for satellites during the rocket launch phase and the on-orbit working phase are mainly divided into two categories. The first category is a passive vibration isolation device with materials as the core, such as silicone rubber flexible legs, wire rope flexible legs, etc. Its advantages are simple structure and high reliability, and its disadvantages are low vibration isolation efficiency, and usually only one fixed vibration isolation frequency (for second-order systems). The second category is semi-active and active vibration isolation devices based on various driving modes, such as electromagnetic mechanisms, Stewart mechanisms, etc. Its advantages are high vibration isolation efficiency, but its disadvantages are obvious: complex, expensive, low reliability, limited use in the rocket launch phase, and as the driving process is continuously executed, there is a risk of increased cumulative errors in the system and reduced calculation accuracy.

[0004] The prior art has the following problems:

[0005] The Chinese invention patent application "An Intelligent Micro-Vibration Suppression System for Satellites on Orbit" (CN116119037A) proposes a micro-vibration suppression system suitable for the real on-orbit satellite operating environment, which consists of a central control unit, a power distribution thermal control unit and a variable parameter satellite isolator. It can adjust the temperature control strategy of the isolator according to the speed information of the satellite combined flywheel, and then change the vibration isolation performance by changing the temperature of the polymer filled in the isolator. However, in this method, the "flywheel group speed-frequency-disturbance amplitude" data table made by the ground test needs to be called in the calculation process of the control target temperature value. This method has two shortcomings: first, due to the huge workload of the ground test, it is inevitable that all speed conditions of the satellite combined flywheel cannot be covered; second, the use of the disturbance amplitude as thermal control guidance information ignores the correlation between the flywheel disturbance and the satellite imaging quality. Summary of the invention

[0006] In view of the above problems, the present invention discloses an intelligent suppression method for micro-vibration of a flywheel group of an on-orbit satellite, which relates to the technical field of spacecraft systems. It effectively solves the problems of small modeling data scale, small coverage of flywheel group speed conditions, and lack of quantitative evaluation indicators for vibration isolation effect in the temperature control process of variable parameter satellite vibration isolators, thereby improving calculation efficiency and accuracy.

[0007] The method comprises the following steps:

[0008] S1, extracting the frequency domain disturbance data of the flywheel n at a specified speed;

[0009] S2, obtaining the displacement response of the sensitive component in the frequency domain under unit disturbance;

[0010] S3, obtaining the time-domain displacement response of the sensitive component under the flywheel group disturbance according to the frequency-domain disturbance data of the flywheel n at the specified speed and the frequency-domain displacement response of the sensitive component under the unit disturbance;

[0011] S4, calculating the actual image displacement value through the displacement response of the sensitive component in the time domain under the disturbance of the flywheel group;

[0012] S5. Quantitative characterization of the performance of the vibration isolator under specified speed conditions is calculated by actual image shift value;

[0013] S6. Build and train the “speed condition-isolator performance” agent model;

[0014] S7. Intelligent control of micro-vibration of on-orbit satellite flywheel assembly.

[0015] Further, the extracting of frequency domain disturbance data of flywheel n at a specified speed is specifically as follows: extracting the frequency domain measured result of flywheel n according to the actual speed operating condition information of the satellite flywheel group, and obtaining the frequency domain disturbance data of flywheel n at the specified speed, wherein n=[1,2,…,N], and N represents the total number of flywheels in the flywheel group.

[0016] Furthermore, the method of obtaining the displacement response of the sensitive component in the frequency domain under unit disturbance is specifically as follows: obtaining the displacement response of the sensitive component in the frequency domain under unit disturbance by frequency response analysis under unit disturbance, wherein the sensitive component includes an optical payload of a remote sensing satellite.

[0017] Further, the method of obtaining the time domain displacement response of the sensitive component under the flywheel group disturbance is specifically as follows: in the time domain, the displacement response information of the sensitive component caused by the flywheel n disturbance is superimposed with the frequency domain displacement response of the sensitive component under the unit disturbance to obtain the time domain displacement response of the sensitive component under the flywheel group disturbance; the process of obtaining the displacement response information of the sensitive component caused by the flywheel n disturbance is as follows: the frequency domain disturbance data of the flywheel n at the specified speed and the frequency domain displacement response of the sensitive component under the unit disturbance are multiplied, summed and Fourier transformed in sequence to obtain the displacement response information of the sensitive component caused by the flywheel n disturbance.

[0018] Furthermore, the calculation of the actual image shift value is specifically as follows: the comprehensive image shift caused by the flywheel group disturbance in the time domain is calculated through the displacement response of the sensitive component in the time domain under the flywheel group disturbance; the comprehensive image shift caused by the flywheel group disturbance in the time domain is obtained by Fourier transforming the comprehensive image shift caused by the flywheel group disturbance in the frequency domain; the comprehensive image shift caused by the flywheel group disturbance in the frequency domain is the actual image shift.

[0019] Furthermore, the specific calculation formula for quantitatively characterizing the performance of the vibration isolator under the specified speed condition is: The v represents the quantitative characterization of the vibration isolator performance under the specified speed condition, and the Δp i represents the actual image shift value corresponding to the i-th frequency sampling point, represents the allowable image shift value corresponding to the i-th frequency sampling point, where i=[1, 2, ..., I], and I represents the total number of frequency sampling points.

[0020] Furthermore, the construction and training of the "speed condition-vibration isolator performance" proxy model is specifically as follows: using an artificial intelligence algorithm to establish a regression learner as the "speed condition-vibration isolator performance" model to be trained, using the speed vector Ω of the flywheel n as the input sample set X to be labeled, adding the label value f(X) of the input sample set to the input sample set X to obtain the input sample set after the label value is added, and dividing the input sample set after the label value is added into a training set, a test set and a validation set in a ratio of 3:1:1. When the determination coefficient is greater than 0.9, the training is terminated to obtain the "speed condition-vibration isolator performance" proxy model; the determination coefficient represents the determination coefficient between the predicted value and the true value of the test set, and the label value f(X) of the sample set is a quantitative representation of the vibration isolator performance under the specified speed condition.

[0021] Furthermore, the intelligent suppression of micro-vibration of the on-orbit satellite flywheel assembly is specifically as follows: the target parameters are calculated by the "speed condition-vibration isolator performance" proxy model, the target parameters are converted into temperature control instructions, and the vibration isolation performance is adjusted according to the temperature control instructions to achieve intelligent suppression of micro-vibration of the on-orbit satellite flywheel assembly. The beneficial effects of the present invention are:

[0022] (1) The present invention provides a complete and universal analysis method for the intelligent suppression of micro-vibration of the flywheel assembly during the on-orbit phase of the satellite. On the basis of the traditional method, it proposes a superposition method for multi-source disturbances of the flywheel assembly, a quantitative characterization and rapid prediction method for the performance of the vibration isolator, and an intelligent adjustment strategy for the variable parameter vibration isolator.

[0023] (2) The idea of ​​quantitatively characterizing the vibration isolation performance of the satellite flywheel assembly isolator proposed in the present invention enables the flywheel assembly speed conditions to be used for training the machine learning model in the form of feature vectors, effectively introducing artificial intelligence algorithms with high data-driven capabilities into the field of on-orbit satellite vibration suppression.

[0024] (3) The present invention can realize rapid and accurate prediction and evaluation of the performance of the vibration isolator under multi-flywheel disturbance. The calculation time for a single speed condition is shortened from 1s level of the traditional method to 1ms level, and the relative error can be controlled within 5% and the determination coefficient can be controlled above 0.95.

[0025] The method of the present invention can be applied in the fields of remote sensing satellite assembly and manufacturing, remote sensing satellite vibration suppression, and remote sensing satellite imaging system design. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of the performance analysis of the vibration isolator under multi-flywheel disturbance in an embodiment of the present invention;

[0027] Figure 2 This is a flow chart of intelligent adjustment of a satellite isolator with variable parameters under variable working conditions in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] This embodiment provides an intelligent suppression method for micro-vibration of a flywheel assembly of an on-orbit satellite. The intelligent suppression method includes two stages. The first stage is the performance analysis of the vibration isolator under multi-flywheel disturbance. The process is as follows: Figure 1 As shown, it includes steps S1-S5. The second stage is to construct an intelligent control model to suppress the micro-vibration of the on-orbit satellite flywheel assembly. The process is as follows Figure 2 As shown, steps S6-S7 are included.

[0030] S1. Extract the frequency domain disturbance data of the flywheel n at a specified speed.

[0031] The relevant operations of step S1 are introduced with a specific example:

[0032] The implementation object selected in this embodiment is a certain type of optical remote sensing satellite. First, the perturbation dynamics characteristic test is carried out on each flywheel of the satellite orthogonal flywheel group in turn.

[0033] The time domain test results of the disturbance force and disturbance torque of flywheel n at a specified speed are obtained through a force measuring platform, and are transformed into corresponding frequency domain measured results through a Fourier transform method. Then, the frequency domain measured results of flywheel n are extracted according to the actual speed operating condition information of the satellite flywheel group, and the frequency domain disturbance data of flywheel n at the specified speed is obtained, wherein n=[1,2,…,N], and N represents the total number of flywheels in the flywheel group.

[0034] S2. Obtain the displacement response of the sensitive component in the frequency domain under unit disturbance.

[0035] The relevant operations of step S2 are introduced with a specific example:

[0036] Through the frequency response analysis under unit disturbance, the displacement response of the sensitive component in the frequency domain under the unit disturbance is obtained; the sensitive component refers to the optical payload of the remote sensing satellite, and micro-vibration will cause the target pointing of its optical axis to shift, thereby seriously affecting the imaging quality of the payload; the unit disturbance represents the action of the unit force and torque at the flywheel position n obtained through the unit frequency response analysis of the satellite.

[0037] S3. Obtain the time domain displacement response of the sensitive component under the flywheel assembly disturbance according to the frequency domain disturbance data of the flywheel n at the specified speed and the frequency domain displacement response of the sensitive component under the unit disturbance.

[0038] The relevant operations of step S3 are introduced with a specific example:

[0039] In the time domain, the frequency domain disturbance data of the flywheel n at the specified speed and the displacement response of the sensitive component in the frequency domain under the unit disturbance are multiplied to obtain the displacement response under the disturbance force and torque in different directions, and then the displacement responses under the disturbance force / torque in different directions are summed, and Fourier transform is superimposed to obtain the displacement response information of the sensitive component caused by the disturbance of the flywheel n; the displacement response information of the sensitive component caused by the disturbance of the flywheel n is superimposed with the displacement responses of the sensitive component in the frequency domain under multiple unit disturbances to obtain the displacement response of the sensitive component in the time domain under the disturbance of the flywheel group, thereby completing the superposition of multi-source disturbances of the flywheel group.

[0040] S4. Calculate the actual image displacement value through the displacement response of the sensitive component in the time domain under the disturbance of the flywheel group.

[0041] The relevant operations of step S4 are introduced with a specific example:

[0042] The translational displacement and rotational displacement of each disturbance sensitive component under multi-flywheel disturbance are calculated through the displacement response of the sensitive component in the time domain under the flywheel group disturbance. On this basis, the comprehensive image shift caused by the flywheel group disturbance in the time domain is calculated through the transfer characteristics of the optical system. The comprehensive image shift represents the offset of the central image point. The comprehensive image shift caused by the flywheel group disturbance in the time domain is obtained by Fourier transforming the comprehensive image shift caused by the flywheel group disturbance in the frequency domain. The comprehensive image shift caused by the flywheel group disturbance in the frequency domain is the actual image shift.

[0043] S5. Quantitative characterization of the isolator performance under specified speed conditions is calculated by actual image shift value.

[0044] The relevant operations of step S5 are introduced with a specific example:

[0045] According to the design index of satellite micro-vibration pixel offset, the maximum ratio of the actual image shift of each frequency sampling point to the sinusoidal image shift tolerance is used as the evaluation index of the satellite vibration isolator performance under the specified speed condition, that is, The v represents the quantitative characterization of the vibration isolator performance under the specified speed condition, and the Δp i represents the actual image shift value corresponding to the i-th frequency sampling point, represents the allowable image shift value corresponding to the i-th frequency sampling point, where i=[1, 2, ..., I], and I represents the total number of frequency sampling points.

[0046] S6. Build and train the “speed condition-isolator performance” agent model.

[0047] The relevant operations of step S6 are introduced with a specific example:

[0048] A regression learner is established by using an artificial intelligence algorithm as a "speed condition-vibration isolator performance" model to be trained. The data sample acquisition method of the artificial intelligence algorithm in this embodiment is as follows: a certain number of speed conditions are randomly collected as sample input values, and the speed vector Ω of the flywheel n is used as the input sample set X to be labeled, and the label value f(X) of the input sample set is added to the input sample set X to obtain the input sample set after the label value is added, and the input sample set after the label value is added is divided into a training set, a test set and a validation set in a ratio of 3:1:1. According to the above-mentioned performance analysis of the vibration isolator under multi-flywheel disturbance, the corresponding vibration isolation performance characteristic value is calculated as the output sample set; a machine learning model for regression analysis is selected as the regression learner of the proxy model; and indicators such as determination coefficient and root mean square error are selected to quantitatively evaluate the prediction performance. When the determination coefficient is greater than 0.9, the training is terminated to obtain the "speed condition-vibration isolator performance" proxy model; the determination coefficient represents the determination coefficient between the predicted value and the true value of the test set, and the label value f(X) of the sample set is a quantitative representation of the performance of the vibration isolator under the specified speed condition.

[0049] The calculation results of random numerical tests show that the coefficient of determination between the predicted value and the true value of the constructed "speed condition-vibration isolation performance" proxy model is not less than 0.95, and the relative error does not exceed 5%, achieving high-precision approximation of the vibration isolation performance evaluation index under all speed conditions of the on-orbit satellite. At the same time, for a single speed condition, the proxy model can shorten the calculation time from the original 1s level to 1ms, greatly improving the calculation efficiency of the vibration isolation performance of the flywheel assembly vibration isolator.

[0050] S7. Intelligent control of micro-vibration of on-orbit satellite flywheel assembly.

[0051] The relevant operations of step S7 are introduced with a specific example:

[0052] The established proxy model library is used to calculate the optimal vibration isolator parameters under the speed conditions of the on-orbit satellite flywheel assembly according to the real-time speed conditions of the satellite in the on-orbit stage; the optimal vibration isolator parameters are used as target parameters, and according to the dynamic thermomechanical analysis test results of the vibration isolator filling material and the current temperature state of the vibration isolator, the target parameters are converted into temperature control instructions; according to the temperature control instructions, the temperature of the vibration isolator filling material is adjusted to change the vibration isolation performance, realize the intelligent suppression of the variable parameter satellite vibration isolator, and then the variable parameter satellite vibration isolator can achieve the best performance of suppressing micro-vibration under the current speed condition.

Claims

1. An intelligent method for suppressing micro-vibration of a flywheel assembly of an on-orbit satellite, characterized in that: The method comprises the following steps: S1, extracting frequency domain disturbance data of flywheel n at a specified speed; S2, obtaining the displacement response of the sensitive component in the frequency domain under unit disturbance; S3, obtaining the time-domain displacement response of the sensitive component under the flywheel group disturbance according to the frequency-domain disturbance data of the flywheel n at the specified speed and the frequency-domain displacement response of the sensitive component under the unit disturbance; S4, calculating the actual image displacement value through the displacement response of the sensitive component in the time domain under the disturbance of the flywheel group; S5. Quantitative characterization of the performance of the vibration isolator under specified speed conditions is calculated by actual image shift value; S6. Build and train the "speed condition-vibration isolator performance" agent model; S7. Intelligent control of micro-vibration of on-orbit satellite flywheel assembly.

2. The intelligent suppression method for micro-vibration of a flywheel assembly for an on-orbit satellite according to claim 1, characterized in that: The method of extracting the frequency domain disturbance data of flywheel n at a specified speed is specifically as follows: extracting the frequency domain measured result of flywheel n according to the actual speed operating condition information of the satellite flywheel group, and obtaining the frequency domain disturbance data of flywheel n at the specified speed, wherein n=[1,2,…,N], and N represents the total number of flywheels in the flywheel group.

3. The intelligent suppression method for micro-vibration of a flywheel assembly for an on-orbit satellite according to claim 1, characterized in that: The method of obtaining the displacement response of the sensitive component in the frequency domain under unit disturbance is specifically: obtaining the displacement response of the sensitive component in the frequency domain under unit disturbance through frequency response analysis under unit disturbance, wherein the sensitive component includes an optical payload of a remote sensing satellite.

4. The intelligent suppression method for micro-vibration of a flywheel assembly for an on-orbit satellite according to claim 3, characterized in that: The method of obtaining the time domain displacement response of the sensitive component under the flywheel group disturbance is specifically as follows: in the time domain, the displacement response information of the sensitive component caused by the flywheel n disturbance is superimposed with the frequency domain displacement response of the sensitive component under the unit disturbance to obtain the time domain displacement response of the sensitive component under the flywheel group disturbance; the process of obtaining the displacement response information of the sensitive component caused by the flywheel n disturbance is as follows: the frequency domain disturbance data of the flywheel n at the specified speed and the frequency domain displacement response of the sensitive component under the unit disturbance are multiplied, summed and Fourier transformed in sequence to obtain the displacement response information of the sensitive component caused by the flywheel n disturbance.

5. The intelligent suppression method for micro-vibration of a flywheel assembly for an on-orbit satellite according to claim 4, characterized in that: The calculation of the actual image shift value is specifically as follows: the comprehensive image shift caused by the flywheel group disturbance in the time domain is calculated through the time domain displacement response of the sensitive component under the flywheel group disturbance; the comprehensive image shift caused by the flywheel group disturbance in the time domain is obtained by Fourier transforming the comprehensive image shift caused by the flywheel group disturbance in the frequency domain; the comprehensive image shift caused by the flywheel group disturbance in the frequency domain is the actual image shift.

6. The intelligent suppression method for micro-vibration of a flywheel assembly for an on-orbit satellite according to claim 5, characterized in that: The specific calculation formula for quantitative characterization of the vibration isolator performance under the specified speed condition is: The v represents the quantitative characterization of the vibration isolator performance under the specified speed condition, and the ΔP i represents the actual image shift value corresponding to the i-th frequency sampling point, represents the allowable image shift value corresponding to the i-th frequency sampling point, where i=[1, 2, ..., I], and I represents the total number of frequency sampling points.

7. The intelligent suppression method for micro-vibration of a flywheel assembly for an on-orbit satellite according to claim 6, characterized in that: The construction and training of the "speed condition-vibration isolator performance" proxy model is specifically as follows: using an artificial intelligence algorithm to establish a regression learner as the "speed condition-vibration isolator performance" model to be trained, using the speed vector Ω of the flywheel n as the input sample set X to be labeled, adding the label value f(X) of the input sample set to the input sample set X to obtain the input sample set after the label value is added, and dividing the input sample set after the label value is added into a training set, a test set and a validation set in a ratio of 3:1:

1. When the determination coefficient is greater than 0.9, the training is terminated to obtain the "speed condition-vibration isolator performance" proxy model; the determination coefficient represents the determination coefficient between the predicted value and the true value of the test set, and the label value f(X) of the sample set is a quantitative representation of the vibration isolator performance under the specified speed condition.

8. The intelligent suppression method for micro-vibration of a flywheel assembly for an on-orbit satellite according to claim 7, characterized in that: The intelligent suppression of micro-vibration of the on-orbit satellite flywheel assembly is specifically: through the "speed The "operating condition-isolator performance" proxy model calculates the target parameters and converts the target parameters into temperature control instructions. The vibration isolation performance is then adjusted according to the temperature control instruction to achieve intelligent suppression of micro-vibration of the flywheel assembly of the on-orbit satellite.

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