A method for identifying and quantitatively characterizing micro-vibration transmission properties

Abstract

This invention provides a method for identifying and quantifying the transmission characteristics of micro-vibrations, comprising: identifying the characteristic frequencies of disturbance signals at the controlled target area; obtaining estimated values ​​of the function coefficients of the control signal transfer function through nominal identification using the LMS method, and determining the control signal transfer function; obtaining estimated values ​​of the function coefficients of the control signal transfer function through bias identification using the LMS method, and determining the control signal transfer function; selecting the control signal transfer function obtained by nominal identification or bias identification, and performing identification and quantification of the micro-vibration transmission characteristics. This invention utilizes an adaptive notch filter to provide a linear model, obtaining estimated values ​​of the unknown system transfer function under a black-box system condition. Based on the idea of ​​superposition of sine and cosine functions, a simplified parameter quantification method for transmission characteristics is designed, which is directly combined with an active control strategy to achieve accurate compensation, effectively improving the accuracy of micro-vibration characteristic identification and compensation control.

Description

Technical Field

[0001] This invention belongs to the field of on-orbit vibration control of spacecraft, and specifically relates to a method for identifying and quantifying the transmission characteristics of micro-vibrations. Background Technology

[0002] Currently, with the further improvement of the performance indicators of high-precision and high-stability spacecraft, the requirements for on-orbit micro-vibration suppression by observation payloads are becoming increasingly stringent. Vibration reduction and isolation technologies based on active micro-vibration control are gradually being applied to the micro-vibration suppression of high-precision and high-stability spacecraft. The time delay, amplification, or attenuation effects caused by the amplitude-frequency and phase-frequency characteristics of the micro-vibration signal and control signal transmission process directly affect the effectiveness of active micro-vibration control. Since the influence of the amplitude-frequency characteristics of the transfer function can be comprehensively considered through the control gain, only the influence of its phase-frequency characteristics, i.e., the phase delay caused by the transfer function, needs to be considered. Accurate identification and quantitative characterization of the micro-vibration transmission characteristics can further improve the effectiveness of active micro-vibration control and enhance its performance indicators.

[0003] Existing active micro-vibration control systems for spacecraft still face challenges such as difficulty in identifying and quantifying the transmission of micro-vibrations in unknown systems (mathematical models of spacecraft structures), hindering further improvements in the effectiveness of active micro-vibration control. The methods for identifying and quantifying the characteristics of micro-vibration transmission have the following shortcomings:

[0004] 1. Effective identification of micro-vibration transmission characteristics in the absence of unknown system models

[0005] Existing methods for identifying the micro-vibration transmission characteristics of spacecraft mostly rely on known models of the system being identified. However, spacecraft have numerous different types of disturbance sources, high disturbance frequencies, and wide spectral distributions. Furthermore, the spacecraft structure and its connections are complex, and the space-ground environment varies, making it impossible to establish an accurate system model. Therefore, it is necessary to research universally applicable methods for identifying the micro-vibration transmission characteristics in the black-box state of unknown system models, and to compensate for the impact of time delay characteristics on the effectiveness of active micro-vibration control.

[0006] 2. It is impossible to achieve simplified and quantitative characterization of the parameters of micro-vibration transmission characteristics.

[0007] Current methods for quantitatively characterizing the micro-vibration transmission characteristics of spacecraft often require multiple parameters to comprehensively represent these characteristics. Each additional parameter necessitates an additional identification and calculation, and the characterization formulas are complex. Therefore, it is necessary to design a simplified quantitative characterization method for micro-vibration transmission characteristics, enabling the identification and calculation of spacecraft micro-vibration transmission characteristics using as few key parameters as possible. Summary of the Invention

[0008] To overcome the shortcomings of existing technologies, the inventors have conducted intensive research and provided a method for identifying and quantifying the transmission characteristics of micro-vibrations. This method utilizes an adaptive notch filter to provide a linear model. Under the condition that the system is in a black box state, the estimated value of the unknown system transfer function is obtained. Based on the idea of ​​superposition of sine and cosine functions, a parameter-simplified quantitative characterization method for transmission characteristics is designed. This method can be directly combined with active control strategies to achieve accurate compensation and effectively improve the accuracy of micro-vibration characteristic identification and compensation control.

[0009] The technical solution provided by this invention is as follows:

[0010] Firstly, a method for identifying and quantitatively characterizing the transmission properties of micro-vibrations includes:

[0011] Identify the characteristic frequencies of the vibration disturbance signal in the controlled target area;

[0012] Based on the identified characteristic frequencies of the disturbance signals in the controlled target area, the nominal identification of the control signal transfer function is performed using the LMS method to obtain the estimated values ​​of the function coefficients of the control signal transfer function and determine the control signal transfer function.

[0013] Based on the identified characteristic frequencies of the disturbance signals in the controlled target area, the LMS method is used to identify the bias of the control signal transfer function, obtain the estimated values ​​of the function coefficients of the control signal transfer function, and determine the control signal transfer function.

[0014] Select the control signal transfer function obtained by nominal identification or bias identification, and implement micro-vibration transmission characteristic identification and quantitative characterization.

[0015] Secondly, a device for identifying and quantifying micro-vibration transmission characteristics includes:

[0016] One or more processors;

[0017] Storage device for storing one or more programs.

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the micro-vibration transmission characteristic identification and quantitative characterization method described in the first aspect.

[0019] Thirdly, a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the micro-vibration transmission characteristic identification and quantitative characterization method described in the first aspect.

[0020] Fourthly, a computer program product comprising: a computer program (also referred to as code or instructions) that, when run, executes the micro-vibration transmission characteristic identification and quantification characterization method described in the first aspect.

[0021] The method for identifying and quantifying the micro-vibration transmission characteristics provided by the present invention has the following beneficial effects:

[0022] (1) This invention provides a method for identifying and quantifying the micro-vibration transmission characteristics, establishing a method for identifying the micro-vibration transmission characteristics of an unknown system model. Currently, most existing methods for identifying micro-vibration transmission characteristics rely on known models of the system being identified. However, spacecraft have numerous different types of disturbance sources, high disturbance frequencies, and wide spectral distributions. Furthermore, spacecraft structures and their connections are complex, and the space and ground environments differ, making it impossible to establish an accurate system model. This invention utilizes an adaptive notch filter to provide a linear model. Under black-box conditions, it obtains an estimate of the unknown system transfer function AP_es, thereby achieving the best possible fit with the unknown system.

[0023] (2) The present invention provides a method for identifying and quantifying the micro-vibration transmission characteristics, which can achieve simplified and quantitative characterization of the parameters of micro-vibration transmission characteristics. Current methods for quantifying the micro-vibration transmission characteristics of spacecraft often require multiple parameters to fully characterize the micro-vibration characteristics. Each additional parameter requires an additional identification calculation, and the characterization formula is complex. Based on the idea of ​​superposition of sine and cosine functions, the present invention designs a simplified and quantitative characterization method for micro-vibration transmission characteristics. By using iterative calculation within a limited time to quickly update the tap coefficients to approximate the expected value, the vibration signal characteristics can be characterized using only the tap coefficients Ws and Wc. Furthermore, the obtained Ws, Wc, and eff can be directly transmitted back to the active micro-vibration controller as telemetry parameters for active vibration control. Attached Figure Description

[0024] Figure 1 Flowchart for identifying the characteristic frequency dis_f of the vibration disturbance signal;

[0025] Figure 2 This is a block diagram of the nominal identification algorithm based on LMS;

[0026] Figure 3 Here is a block diagram of the LMS-based positive pull-off identification algorithm;

[0027] Figure 4 This is a block diagram of the negative bias identification algorithm based on LMS. Detailed Implementation

[0028] The features and advantages of the present invention will become clearer and more apparent from the following detailed description.

[0029] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0030] To address the issues of unknown system models and parameter simplification in the identification and characterization of micro-vibration characteristics in spacecraft on-orbit, this invention provides a method for identifying and quantifying the transmission characteristics of micro-vibration. It assumes that the vibration signal from a disturbance source at point A on the spacecraft, located at point B of a sensitive component, is a sinusoidal signal of a specific frequency. Active suppression of micro-vibration on-orbit requires that an actuator installed at point C generate a sinusoidal signal at point B with the same amplitude and frequency but opposite phase. Due to the complexity of the spacecraft structure, if the transmission model of the micro-vibration response between points C and B is unknown, a least mean square adaptive notch filter (LMS) can be used to provide a linear model. With the system model in a black box state, an estimate of the unknown system transfer function can be obtained. When calculating the estimated transfer function, the vibration signal expected to be output by the actuator can be considered to be a superposition of a sine function and a cosine function containing specific amplitude coefficients Ws and Wc and a frequency. The characteristics of the vibration signal can be characterized using only Ws and Wc.

[0031] The method for identifying and quantitatively characterizing the micro-vibration transmission properties includes the following steps:

[0032] The characteristic frequency dis_f of the disturbance signal at point B in the controlled target area is identified by the following steps:

[0033] Before applying control, under the influence of the disturbance source, the original disturbance signal dis_ori is transmitted to the controlled target area via the disturbance source signal transfer function DP. An FFT analysis is then performed on the actual disturbance signal dis_des to obtain the measured characteristic frequency of the disturbance signal. After correction, the estimated value dis_f_es of the characteristic frequency dis_f of the disturbance signal that actually needs to be actively suppressed at the controlled target area is obtained. The purpose of the correction is to mathematically correct the actual frequency of the disturbance signal transmitted from the disturbance source to the controlled target area based on prior knowledge of the error.

[0034] Where: dis_f is the actual value of the characteristic frequency of the disturbance signal at the controlled target area, which is determined by the characteristics of the disturbance source; dis_ori is the original disturbance signal of the disturbance source itself (without considering the influence of installation boundary and transmission link factors); DP is the actual transfer function of the disturbance source signal transmitted to the controlled target area; dis_f_es is the estimated value of the characteristic frequency of the disturbance signal at the controlled target area, the initial value is the ground identification result, and it can be updated through autonomous identification in orbit.

[0035] (2) Nominal identification of the control signal transfer function AP based on the LMS algorithm is performed. The control signal transfer function AP characterizes the vibration effect generated after the actuator at point C generates vibration (to suppress disturbance) and transmits it to the controlled target area (point B). The steps are as follows:

[0036] Before the system disturbance source is activated, and with all onboard structures and their connections fully deployed, the actual frequency of the control signal in the controlled target area during active identification is identical to the pre-acquired estimated characteristic frequency (dis_f_es) of the disturbance source in the on-orbit controlled target area. The estimated function coefficients AP_es are obtained through nominal identification of the control signal transfer function AP, characterized in the frequency domain by sine tap coefficients Ws, cosine tap coefficients Wc, and the propagation phase delay. The estimated value of the transfer function AP is a linear model.

[0037] Where: AP is the actual value of the transfer function from the active control signal to the controlled target area; AP_es is the estimated value of the function coefficients of the control signal transfer function, with the initial value being the ground identification result and the value being updated autonomously in orbit.

[0038] (3) Perform pull-off identification on the control signal transfer function AP based on the LMS algorithm. The steps are as follows:

[0039] When the system disturbance source has been activated and all onboard structures and their connections are fully deployed, the actual frequency of the control signal in the controlled target area during active identification is obtained by positively and negatively biasing the estimated value (dis_f_es) of the disturbance source characteristic frequency in the controlled target area on the orbit. The positive bias estimate of the function coefficient AP_esp is obtained by positive bias identification of the control signal transfer function AP, and the negative bias estimate of the function coefficient AP_esn is obtained by negative bias identification of the control signal transfer function AP. The function coefficient estimate AP_es is obtained by weighted averaging the positive bias estimate AP_esp and the negative bias estimate AP_esn. In the frequency domain, it is characterized by the sine tap coefficient Ws, the cosine tap coefficient Wc, and the propagation phase delay.

[0040] Where: AP_esp is the positive bias estimate of the coefficients of the control signal transfer function, and AP_esn is the negative bias estimate of the coefficients of the control signal transfer function.

[0041] When identifying the nominal value of the control signal transfer function AP in step (2), or when performing pull-off identification on the control signal transfer function AP in step (3), the identification method based on the LMS algorithm is as follows:

[0042] The first step involves performing a self-checking iterative calculation using the LMS algorithm, with a self-check period of x seconds, to identify and quantify the representation:

[0043]

[0044] In the formula: DA represents the control signal output to the actuator, i.e., the desired output voltage; n represents the nth iteration calculation; v represents the actuator drive voltage amplitude; f represents the estimated characteristic frequency of the disturbance signal at the nominal identification of the controlled target area, dis_f_es, dis_f_es+δf at positive bias identification, and dis_f_es-δf at negative bias identification, where δf represents the signal frequency bias; e represents the error signal; AD represents the actual acquired signal at the controlled target area; y represents the estimated value of the acquired signal at the controlled target area after the actuator is actuated; μ represents the gain constant used to control adaptive speed and stability; Ws represents the sine tap coefficient, and Wc represents the cosine tap coefficient, both of which together characterize the transmission amplitude amplification / attenuation coefficient.

[0045] The second step is to calculate the root mean square of the error signal e by taking a period of data m seconds before the self-test begins, and record it as rms0.

[0046] The third step is to calculate the root mean square of the error signal e over a period of time after the self-test is completed in x seconds. The result is denoted as rms1. The identification accuracy is calculated as eff = rms1 / rms0. Finally, Ws and Wc are saved.

[0047] The present invention also provides a device for identifying and quantifying micro-vibration transmission characteristics, comprising:

[0048] One or more processors;

[0049] Storage device for storing one or more programs.

[0050] When the one or more programs are executed by the one or more processors, the one or more processors implement the micro-vibration transmission characteristic identification and quantitative characterization method described in the first aspect.

[0051] The present invention also provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the micro-vibration transmission characteristic identification and quantitative characterization method described in the first aspect.

[0052] The readable storage media include, but are not limited to, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0053] The present invention also provides a computer program product, the computer program product comprising: a computer program (also referred to as code or instructions), which, when the computer program is run, executes the micro-vibration transmission characteristic identification and quantification characterization method described in the first aspect.

[0054] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, microwave, etc.) means.

[0055] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0056] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the products and devices described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0057] The present invention has been described in detail above with reference to specific embodiments and exemplary examples; however, these descriptions should not be construed as limiting the present invention. Those skilled in the art will understand that various equivalent substitutions, modifications, or improvements can be made to the technical solutions and embodiments of the present invention without departing from the spirit and scope of the invention, and all such modifications and improvements fall within the scope of the present invention. The scope of protection of the present invention is defined by the appended claims.

[0058] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A method for identifying and quantitatively characterizing the transmission properties of micro-vibrations, characterized in that, include: Identify the characteristic frequencies of the vibration disturbance signal in the controlled target area; Based on the identified characteristic frequencies of the disturbance signals in the controlled target area, the nominal identification of the control signal transfer function is performed using the LMS method to obtain the estimated values ​​of the function coefficients of the control signal transfer function and determine the control signal transfer function. Based on the identified characteristic frequencies of the disturbance signals in the controlled target area, the LMS method is used to identify the bias of the control signal transfer function, obtain the estimated values ​​of the function coefficients of the control signal transfer function, and determine the control signal transfer function. Select the control signal transfer function obtained by nominal identification or bias identification, and implement micro-vibration transmission characteristic identification and quantitative characterization. The step of obtaining the function coefficient estimate of the control signal transfer function by identifying the pull-off of the control signal transfer function based on the characteristic frequency of the disturbance signal at the identified controlled target area using the LMS method includes: when the system disturbance source has been activated and all on-board structures and their connecting links are fully deployed, using the characteristic frequencies of the disturbance signal at the controlled target area with positive and negative pull-off as input, obtaining the positive pull-off estimate of the function coefficient of the control signal transfer function by identifying the positive pull-off, obtaining the negative pull-off estimate of the function coefficient by identifying the negative pull-off, and taking a weighted average of the positive and negative pull-off estimates to obtain the function coefficient estimate, thus obtaining the control signal transfer function.

2. The method for identifying and quantitatively characterizing micro-vibration transmission characteristics according to claim 1, characterized in that, The step of identifying the characteristic frequencies of the disturbance signal at the controlled target area includes: Before applying control, the original disturbance signal dis_ori is transmitted to the controlled target area through the disturbance source signal transfer function only under the action of the disturbance source. The actual disturbance signal dis_des is subjected to FFT analysis to obtain the characteristic frequency measurement value of the disturbance signal. After correction, the estimated value of the characteristic frequency of the disturbance signal that actually needs to be actively suppressed in the controlled target area is obtained as dis_f_es.

3. The method for identifying and quantitatively characterizing micro-vibration transmission characteristics according to claim 1, characterized in that, The step of obtaining the estimated function coefficients of the control signal transfer function by nominal identification of the control signal transfer function using the LMS method based on the identified characteristic frequencies of the disturbance signal at the controlled target area includes: Before the system disturbance source is activated and all onboard structures and their connections are fully deployed, the characteristic frequency of the disturbance signal at the identified controlled target area is used as input. The nominal identification of the control signal transfer function is performed using the LMS method to obtain the estimated value of its function coefficients, thus obtaining the control signal transfer function.

4. The method for identifying and quantitatively characterizing micro-vibration transmission characteristics according to claim 3, characterized in that, When identifying the nominal value of the control signal transfer function, or when performing bias identification on the control signal transfer function, the identification method based on the LMS algorithm is as follows: The first step is to perform self-checking iterative calculations using the LMS algorithm. x Seconds, identifying quantitative representations: In the formula: DA This represents the control signal output to the actuator, i.e., the desired output voltage; n Indicates the first n The calculation is performed in several iterations; v Indicates the amplitude of the actuator drive voltage; f This represents the estimated characteristic frequency of the disturbance signal during nominal identification at the controlled target area, dis_f_es, and the value of dis_f_es+ during positive bias identification. δf dis_f_es during negative bias identification δf , δf Indicates the signal frequency deviation; e Indicates the error signal; AD This indicates the actual signal collected at the controlled target area; y This represents the estimated value of the signal collected at the controlled target area after the actuator is activated. μ Ws represents the gain constant used to control adaptive speed and stability; Wc represents the sine tap coefficient and Ws represents the cosine tap coefficient. Together, they characterize the transmission amplitude amplification / attenuation coefficient. Step 2, before self-check m Before the second begins, the error signal is checked. e Calculate the root mean square of data over a period of time, denoted as . rms 0; Step 3: Self-check x After the seconds are completed, the error signal is processed. e Calculate the root mean square of data over a period of time, denoted as . rms 1. Calculate the recognition accuracy. eff = rms 1 / rms 0, will be the final Ws and Wc save.

5. A device for identifying and quantifying the characteristics of micro-vibration transmission, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the micro-vibration transmission characteristic identification and quantitative characterization method according to any one of claims 1 to 4.

6. A readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the micro-vibration transmission characteristic identification and quantitative characterization method as described in any one of claims 1 to 4.

7. A computer program product, characterized in that, The computer program product includes: a computer program that, when the computer program is run, executes the micro-vibration transmission characteristic identification and quantitative characterization method as described in any one of claims 1 to 4.

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