Active control method for high-frequency vibration in variable-weight adaptive robust compensation type structure

Abstract

The invention relates to the technical field of medium-high frequency vibration control, and discloses a structure medium-high frequency vibration variable weight adaptive robust compensation type active control method, which comprises the following steps of: respectively inputting error signals into a minimum mean square controller, a robust controller and a weight controller for operation; a weight controller compares the squared error signal with a reconstructed reference signal mean value, and adaptively adjusts the weights of a minimum mean square controller and a robust controller according to the signal mean value; the first output control quantity and the second output control quantity are superposed and then act on a structural vibration control point through an electromagnetic actuator to obtain an output signal, the output signal is superposed with an original reference signal, and a new error signal is formed to serve as an input signal of a next round of control operation. According to the method, a reference signal reconstruction technology is adopted, a robust compensation mechanism is additionally arranged, an adaptive variable weight strategy and a convergence time and error robustness statistical method are configured, and the medium-high frequency vibration control effect of structures such as a new energy automobile electric drive assembly speed reducer shell is effectively improved.

Description

A variable-weight adaptive robust compensation structure for active control of high-frequency vibration Technical Field

[0001] This invention relates to the field of high-frequency vibration control technology in structures such as the reducer housing of electric drive assemblies for new energy vehicles, and particularly to a variable-weight adaptive robust compensation structure active control method for high-frequency vibration. Background Technology

[0002] Since new energy vehicles lack the masking effect of low-frequency engine noise, gear whine in their electric drive assembly reducers is more pronounced. Furthermore, by analyzing the structural and radiative contributions of in-vehicle noise based on the research object, for gear orders: in the 200Hz~1500Hz range, structural contribution is absolutely dominant. Traditional vibration control methods targeting gear profiles have reached a research bottleneck. Active vibration control technology can not only overcome the limitations of traditional control methods but also achieve more precise and flexible vibration and noise management.

[0003] Currently, most studies on active vibration control place actuators on bearings and gears. However, in reality, these locations are difficult to accommodate actuators. Furthermore, due to the lack of consideration for the nonlinear characteristics of the actuators and the fluctuation of the output force under a given frequency and amplitude input, the highest frequency achieved in domestic and international research is around 400Hz. In addition, there is no unified statistical method for evaluating the algorithm performance (including convergence time and error) of active vibration control technology. Summary of the Invention

[0004] This invention provides a variable-weight adaptive robust compensation structure active control method for high-frequency vibration. By optimizing the installation layout of the actuator, adopting reference signal reconstruction technology, adding a robust compensation mechanism, configuring an adaptive variable-weight strategy and a standardized performance statistical method, the method effectively improves the high-frequency vibration control effect of structures such as the reducer housing of the electric drive assembly of new energy vehicles.

[0005] This invention provides a method for active control of high-frequency vibration in a variable-weight adaptive robust compensation structure, comprising: S1, acquiring the error signal e(n) of the response point of the controlled structure, and obtaining y'(n) of the controller output signal u(n) after passing through a secondary channel estimation model, so as to obtain a reconstructed reference signal d'(n) through reference signal reconstruction technology; S2, inputting the reconstructed reference signal d'(n) into a secondary channel estimation model that considers the electromechanical coupling effect of the system, and obtaining the signal x'(n) after model calculation; S3, inputting the error signal e(n) into an adaptive filter and a robust controller respectively for operation. S4. Calculate and obtain the first output control quantity uf(n) and the second output control quantity ur(n); S5. Superimpose the first output control quantity uf(n) and the second output control quantity ur(n) and apply them to the control point of the controlled structure to generate the final output signal of the controller and transmit it to the secondary channel. After signal amplification, electromagnetic actuator force output and structural transmission, the output signal y(n) is obtained; S6. Superimpose the output signal y(n) with the original reference signal d(n) to form a new error signal e(n), and use the new error signal e(n) as the input signal for the next round of control calculation.

[0006] Further, S3 specifically includes: S301, taking the error signal e(n) and signal x'(n) as input signals of the normalized least mean square algorithm, inputting them into an adaptive filter for processing to output a first preliminary control quantity, multiplying the first preliminary control quantity by the weight β of the normalized least mean square algorithm to obtain the first output control quantity uf(n) of the normalized least mean square algorithm; S302, inputting the error signal e(n) into a robust controller for processing to output a second preliminary control quantity, multiplying the second preliminary control quantity by the weight (1-β) of the robust controller to obtain the second output control quantity ur(n) of the robust controller.

[0007] Further, in S301 and S302, the method for determining the weights of the normalized minimum mean square algorithm and the robust controller is as follows: The weight of the normalized minimum mean square algorithm is set to β, and the weight of the robust controller is (1-β). Simultaneously, the maximum value βmax, minimum value βmin, error threshold Δ, and weight adjustment step sizes δ1 and δ2 are preset. A signal acquisition window of a set length is defined, and the error signal e(n) within the window is acquired and squared to obtain e²(n). The mean of e²(n) within the window is calculated, and the mean of the square A² of the reconstructed reference signal d'(n) is obtained. Finally... The ratio of the mean of e²(n) to the mean of A² within the window is obtained. If the ratio is greater than the error threshold Δ, it is determined that the error is large, and the control strategy aims to quickly converge the error. That is, if β has not reached the minimum value βmin, then β is reduced by a step size δ2; if β has reached the minimum value βmin, then β is kept unchanged. If the ratio is less than the error threshold Δ, it is determined that the error is small, and the control strategy aims to pursue a smaller steady-state error. That is, if β has not reached the maximum value βmax, then β is increased by a step size δ1; if β has reached the maximum value βmax, then β is kept unchanged.

[0008] Furthermore, in S302, the robust controller is an H∞ hybrid sensitivity robust controller, used to compensate for the impact of the reconstructed reference signal on the robustness of the control system.

[0009] Furthermore, after S5, the method further includes: when the error signal e(n) reaches the control target, acquiring the response point signal without control and the response point signal after control, performing bandpass filtering on the response point signal without control and the response point signal after control to remove irrelevant noise, extracting the envelope of the filtered signal and removing singular values, fitting the preprocessed envelope, taking the mean value of the fitted envelope entering the stable stage, defining the time corresponding to the intersection of the mean value and the envelope fitting curve as the convergence time, defining the ratio of the mean value to the mean value of the response point signal envelope without control as the control error, and determining the quantitative evaluation of the closed-loop control performance of S1-S5 based on the convergence time and control error; wherein, the control target is that the superimposed acceleration signal approaches 0.

[0010] Furthermore, the control points and response points set on the controlled structure are physically linked through the structural transmission path. The control points are set in the region of the controlled structure where the stiffness is greater than a set value, so as to reduce the vibration response.

[0011] Furthermore, in S4, the secondary channel consists of a power amplifier, an electromagnetic actuator, and a structural transmission path.

[0012] This invention also provides a variable-weight adaptive robust compensation structure high-frequency vibration active control system. Based on the variable-weight adaptive robust compensation structure high-frequency vibration active control method described above, the control system includes an accelerometer, a voltage acquisition module, a controller, a host computer, a voltage output module, a power amplifier, an electromagnetic actuator, and a controlled structure. The accelerometer is connected to the response point of the controlled structure and the voltage acquisition module. The voltage acquisition module is connected to the controller. The controller is connected to the host computer and the voltage output module. The voltage output module is connected to the power amplifier. The power amplifier is connected to the electromagnetic actuator. The electromagnetic actuator is connected to the control point of the controlled structure. The accelerometer acquires the acceleration error signal e(n) of the response point, i.e., the acceleration error of the controlled structure. The acceleration signal d(n) generated by the disturbance force at the structural disturbance point and the acceleration signal y(n) converted from the output force of the electromagnetic actuator are superimposed and transmitted to the voltage acquisition module via an adaptive amplifier. This signal is converted into an analog signal and transmitted to the controller. The controller calls the control strategy preset by the FPGA module in LabVIEW on the host computer to perform calculations and generate a control signal synthesized by the normalized least mean square algorithm output uf(n) and the robust controller output ur(n). This control signal is converted into a digital signal by the voltage output module and transmitted to the power amplifier for amplification. It is then output to the electromagnetic actuator at the control point to drive the actuator to output a physical control force. This control force is converted into the acceleration signal y(n) at the response point through the structural transmission path and superimposed with d(n) at the response point to achieve the control objective of making the superimposed acceleration signal approach 0.

[0013] Furthermore, the control strategy integrates a nonlinear model of the power amplifier-electromagnetic actuator mechanism, a secondary channel estimation model considering electromechanical coupling effects, an H∞ hybrid sensitivity robust controller, and an adaptive variable weight control strategy; wherein, the adaptive variable weight control strategy is a normalized least mean square algorithm and a weight determination method for the robust controller.

[0014] The beneficial effects of this invention are as follows: By installing the actuator in structures such as the reducer housing, the installation space is optimized and the engineering implementation difficulty is reduced. Electromechanical coupling effect is incorporated into the secondary channel estimation model to improve the accuracy of signal description and algorithm performance. Reference signal reconstruction technology is used to solve the problem of pure reference signal acquisition. The robustness loss of the system is compensated by the H∞ hybrid sensitivity robust controller. The adaptive variable weight control strategy dynamically adjusts the weights of the two controllers according to the error to ensure the rapid and stable convergence of the system. At the same time, the control effect is accurately quantified based on the controller performance statistical method with strong consistency under single-frequency excitation. This invention effectively overcomes the shortcomings of traditional active vibration control in terms of installation feasibility, model accuracy, robustness, convergence efficiency and performance evaluation consistency. It significantly improves the reliability, accuracy and engineering applicability of high-frequency vibration control in structures such as reducers of electric drive assemblies for new energy vehicles. Attached Figure Description

[0015] Figure 1 is a schematic flowchart of the active control method for high-frequency vibration in a variable-weight adaptive robust compensation structure according to the present invention.

[0016] Figure 2 is a control block diagram of the active control method for high-frequency vibration in the variable-weight adaptive robust compensation structure of the present invention.

[0017] Figure 3 is a schematic diagram of the adaptive weight adjustment process in this invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] As shown in Figures 1 and 2, this invention provides a high-frequency vibration active control method for a variable-weight adaptive robust compensation structure. It is based on a control system composed of an accelerometer, a voltage acquisition module, a controller, a host computer, a voltage output module, a power amplifier, an electromagnetic actuator, and a controlled structure. The controlled structure has disturbance points, control points, and response points, which are physically linked through a structural transmission path. The control points are located in areas of the controlled structure where the stiffness is greater than a set value (i.e., areas with higher stiffness have greater effective force) to reduce vibration response. The disturbance point is the vibration source, the control point is the location where the control force is applied, and the response point is the target monitoring point for vibration transmission. The core of the control is to counteract the vibration transmitted from the disturbance point to the response point through the force applied by the control point. Specifically, the correlation logic is: Vibration transmission path: Disturbance force generated by the disturbance point → transmitted through the vibration structure transmission path → to the response point, causing vibration at the response point.

[0021] Control intervention path: The controller outputs a control command based on the error signal e(n) collected at the response point → after being amplified by the power amplifier, it is transmitted to the electromagnetic actuator installed at the control point → the actuator applies a reverse control force at the control point (opposite to the phase of the disturbance and matched in amplitude) → this control force is transmitted to the response point through the same structural transmission path.

[0022] Closed-loop feedback logic: At the response point, the original vibration d(n) from the disturbance point is superimposed with the control vibration (y(n)) from the control point to form an error signal e(n) → the sensor collects e(n) and feeds it back to the controller → the controller adjusts the actuator output of the control point according to e(n) until e(n) approaches 0 (i.e., the control vibration completely cancels the original vibration).

[0023] The method specifically includes the following steps: S1. Acquire the error signal e(n) of the response point of the controlled structure and the output signal u(n) of the control system. Using the output signal u(n), error signal e(n), secondary channel model information, and physical characteristics of the disturbance source, obtain the reconstructed reference signal d'(n) through reference signal reconstruction technology (in cases where certain reference signals are difficult to obtain, a reconstructed reference signal is used as the input to the control system to ensure the accuracy of the reconstructed reference signal). d(n) is defined as the acceleration signal at the response point caused by the disturbance force at the disturbance point. Essentially, it is the reference physical signal generated by the vibration disturbance source itself. Corresponding to the disturbance force released by vibration sources (disturbance points) such as gear squealing in the reducer in the electric drive assembly of new energy vehicles, after being transmitted to the response point (such as the relevant position of the rear suspension mounting), it should theoretically generate an acceleration signal. It is a reference quantity describing the vibration disturbance itself and is the core reference for measuring the magnitude of the error in closed-loop control (the ultimate control goal is to make the superimposed error signal e(n) approach 0, i.e., to cancel the influence of d(n)).

[0024] Based directly on the disturbance effect reflected by the error signal e(n), combined with the signal transmission law described by the secondary channel estimation model, and superimposed with the physical characteristic parameters (frequency, amplitude range) of the disturbance source itself, an accurate reference signal d'(n) equivalent to d(n) is reconstructed through algorithm derivation.

[0025] S2. The reconstructed reference signal d'(n) is input to the secondary channel estimation model that takes into account the electromechanical coupling effect of the system, and the signal x'(n) is obtained after model operation; S3. The error signal e(n) is input to the adaptive filter and the robust controller respectively for operation to obtain the first output control quantity uf(n) and the second output control quantity ur(n); Specifically, it includes: S301. The error signal e(n) and the signal x'(n) are used as the input signals of the normalized least mean square algorithm, and are fed into the adaptive filter for processing to output the first preliminary control quantity. The first preliminary control quantity is multiplied by the weight β of the normalized least mean square algorithm to obtain the first output control quantity uf(n) of the normalized least mean square algorithm; S302. The error signal e(n) is input to the robust controller for processing to output the second preliminary control quantity. The second preliminary control quantity is multiplied by the weight (1-β) of the robust controller to obtain the second output control quantity ur(n) of the robust controller. The robust controller is an H∞ hybrid sensitivity robust controller, used to compensate for the impact of the reconstructed reference signal on the robustness of the control system.

[0026] In S301 and S302, as shown in Figure 3, the adaptive weight control strategy, namely the weight determination method of the normalized minimum mean square algorithm and the robust controller, is as follows: (1) Set the weight of the normalized minimum mean square algorithm to β, and the weight of the robust controller to (1-β). The sum of the two weights is always 1. At the same time, preset the maximum value βmax, the minimum value βmin (corresponding to the minimum and maximum values ​​of the robust controller weight), the error threshold Δ, and the weight adjustment step size δ1 and δ2; (2) Define a signal acquisition window with a length of 50, acquire the error signal e(n) in the window and perform squaring to obtain e²(n), calculate the mean of e²(n) in the window, and at the same time obtain the mean of the square of the reconstructed reference signal d'(n) (i.e., A²), and finally obtain the mean of e in the window. (3) If the ratio is greater than the error threshold Δ, it is determined that the error is large. The control strategy takes the rapid convergence of error as the core objective, that is: if β does not reach the minimum value βmin (i.e. the robust controller weight does not reach the maximum value), then β is reduced by a step size δ2; if β has reached the minimum value βmin (i.e. the robust controller weight has reached the maximum value), then β is kept unchanged; (4) If the ratio is less than the error threshold Δ, it is determined that the error is small. The control strategy takes the pursuit of smaller steady-state error as the core objective, that is: if β does not reach the maximum value βmax (i.e. the robust controller weight does not reach the minimum value), then β is increased by a step size δ1; if β has reached the maximum value βmax (i.e. the robust controller weight has reached the minimum value), then β is kept unchanged.

[0027] S4. The first output control quantity uf(n) and the second output control quantity ur(n) are superimposed and applied to the control point of the controlled structure to generate the final output signal of the controller, which is then transmitted to the secondary channel (composed of a power amplifier, an electromagnetic actuator, and a structural transmission path). After signal amplification, electromagnetic actuator force output, and structural transmission, the output signal y(n) is obtained. S5. The output signal y(n) is superimposed with the original reference signal d(n) to form a new error signal e(n), which is used as the input signal for the next round of control calculation. The original reference signal d(n) is the acceleration signal at the response point caused by the disturbance force at the structural disturbance point.

[0028] Following S5, a statistical method for controller performance (error, convergence time) under a single frequency disturbance is also included. This controller performance statistical method can accurately describe the speed and accuracy of signal convergence and has high consistency across multiple experiments. Specifically, when the error signal e(n) reaches the control target, the response point signals without control and after control are collected. Bandpass filtering is performed on the response point signals without control and after control to remove irrelevant noise. The envelope of the filtered signal is then extracted and singular values ​​are removed. The preprocessed envelope is fitted, and the mean value of the fitted envelope entering the stable phase is taken. The intersection of this mean value and the fitted envelope curve is defined as the convergence time. The ratio of this mean value to the mean value of the response point signal envelope without control is defined as the control error. Based on the convergence time and control error, the quantitative evaluation of the closed-loop control performance of S1-S5 is completed. The control target is for the superimposed acceleration signal to approach 0.

[0029] This invention also provides a variable-weight adaptive robust compensation structure high-frequency vibration active control system. Based on the variable-weight adaptive robust compensation structure high-frequency vibration active control method described above, the control system includes an accelerometer, a voltage acquisition module, a controller, a host computer, a voltage output module, a power amplifier, an electromagnetic actuator, and a controlled structure. The accelerometer is connected to the response point of the controlled structure and the voltage acquisition module. The voltage acquisition module is connected to the controller. The controller is connected to the host computer and the voltage output module. The voltage output module is connected to the power amplifier. The power amplifier is connected to the electromagnetic actuator. The electromagnetic actuator is connected to the control point of the controlled structure. The accelerometer acquires the acceleration error signal e(n) of the response point, i.e., the acceleration error of the controlled structure. The acceleration signal d(n) generated by the disturbance force at the structural disturbance point and the acceleration signal y(n) converted from the output force of the electromagnetic actuator are superimposed and transmitted to the voltage acquisition module via an adaptive amplifier. This signal is converted into an analog signal and transmitted to the controller. The controller calls the control strategy preset by the FPGA module in LabVIEW on the host computer to perform calculations and generate a control signal synthesized by the normalized least mean square algorithm output uf(n) and the robust controller output ur(n). This control signal is converted into a digital signal by the voltage output module and transmitted to the power amplifier for amplification. It is then output to the electromagnetic actuator at the control point to drive the actuator to output a physical control force. This control force is converted into the acceleration signal y(n) at the response point through the structural transmission path and superimposed with d(n) at the response point to achieve the control objective of making the superimposed acceleration signal approach 0.

[0030] The control strategy integrates a nonlinear model of the power amplifier-actuator mechanism, a secondary channel estimation model considering electromechanical coupling effects, an H∞ hybrid sensitivity robust controller, and an adaptive variable weight control strategy. The adaptive variable weight control strategy uses a normalized minimum mean square (MMS) algorithm and a robust controller weight determination method. It adaptively adjusts the weights of the MMS and robust controllers based on the ratio of the mean of the squared error signal to the mean of the squared reference signal within the window, ensuring that the MMS control weight increases under low error conditions and the robust control weight increases under high error conditions. The nonlinear model of the power amplifier-electromagnetic actuator mechanism accurately describes the nonlinear characteristics of the actuator under different input frequencies and amplitudes. The secondary channel estimation model considering the system's electromechanical coupling effects reflects the actual secondary channel model well. The H∞ hybrid sensitivity robust controller effectively compensates for the impact of the reconstructed reference signal on the robustness of the control system.

[0031] This invention mounts the actuator on structures such as the reducer housing, which provides more installation space and is easier to implement in engineering compared to traditional methods. The secondary channel estimation model considers the nonlinear characteristics of the secondary channel and the errors in the output frequency and amplitude of the electromagnetic actuator, enabling a more accurate description of the changes in the actual signal after passing through the secondary channel, thus improving algorithm performance. Addressing the challenge of finding unaffected areas within the same structure in active structural vibration control, a reference signal reconstruction technique is proposed. Since the accuracy of the secondary channel estimation model cannot reach 100%, reconstructing the reference signal weakens system robustness; this is addressed by introducing a robust controller to compensate and improve the robustness of the control system. To address the disturbance factor of the electromagnetic actuator's output frequency and amplitude errors, the weights of the normalized minimum mean square controller and the robust controller are adaptively adjusted by the ratio of the mean square of the error signal within the window to the mean square of the reference signal. This ensures that the minimum mean square control weight increases under low error conditions and the robust control weight increases under high error conditions, guaranteeing rapid and stable convergence of the system. Under single-frequency excitation, the convergence time and error defined in this invention show strong consistency in multiple experiments.

[0032] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0033] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for active control of high-frequency vibration in a variable-weight adaptive robust compensation structure, characterized in that, include: S1. Acquire the error signal e(n) at the response point of the controlled structure, and obtain y'(n) after the controller output signal u(n) is processed by the secondary channel estimation model, so as to obtain the reconstructed reference signal d'(n) through the reference signal reconstruction technique; S2. Input the reconstructed reference signal d'(n) into the secondary channel estimation model that takes into account the electromechanical coupling effect of the system, and obtain the signal x'(n) after model calculation; S3. Input the error signal e(n) into the adaptive filter and the robust controller respectively for calculation to obtain the first output control quantity u. f (n) and the second output control quantity u r (n); S4, set the first output control quantity u f (n) and the second output control quantity u r (n) is superimposed on the control point of the controlled structure to generate the final output signal of the controller to be transmitted to the secondary channel. After signal amplification, electromagnetic actuator force output and structural transmission, the output signal y(n) is obtained; S5, the output signal y(n) is superimposed with the original reference signal d(n) to form a new error signal e(n), and the new error signal e(n) is used as the input signal for the next round of control operation.

2. The method for active control of high-frequency vibration in a variable-weight adaptive robust compensation structure according to claim 1, characterized in that, S3 specifically includes: S301, taking the error signal e(n) and signal x'(n) as input signals of the normalized least mean square algorithm, feeding them into an adaptive filter for processing to output a first preliminary control quantity, multiplying the first preliminary control quantity by the weight β of the normalized least mean square algorithm to obtain the first output control quantity u of the normalized least mean square algorithm. f (n); S302, Input the error signal e(n) to the robust controller for processing to output a second preliminary control quantity, and multiply the second preliminary control quantity by the weight (1-β) of the robust controller to obtain the second output control quantity u of the robust controller. r (n).

3. The active control method for high-frequency vibration in a variable-weight adaptive robust compensation structure according to claim 2, characterized in that, In S301 and S302, the weight determination method for the normalized minimum mean square algorithm and the robust controller is as follows: the weight of the normalized minimum mean square algorithm is set to β, and the weight of the robust controller is (1-β), while the maximum value of β is preset. max Minimum value β min Error threshold Δ, and weight adjustment step size δ1, δ2; define a signal acquisition window of a set length, acquire the error signal e(n) in the window and perform squaring operation to obtain e²(n), calculate the mean of e²(n) in the window, and at the same time obtain the mean of the square A² of the reconstructed reference signal d'(n), and finally obtain the ratio of the mean of e²(n) in the window to the mean of A²; If the ratio is greater than the error threshold Δ, it is determined that the error is large, and the control strategy focuses on rapidly converging the error, that is: if β does not reach the minimum value β min If β has reached its minimum value, then decrease β by one step δ2. min If the ratio is less than the error threshold Δ, then β remains constant; if the ratio is less than the error threshold Δ, it is determined that the error is small, and the control strategy aims to pursue a smaller steady-state error, that is: if β does not reach the maximum value β max If β has reached its maximum value β, then increase β by a step size δ1. max If β remains unchanged, then β remains unchanged.

4. The active control method for high-frequency vibration in a variable-weight adaptive robust compensation structure according to claim 3, characterized in that, In S302, the robust controller is an H∞ hybrid sensitivity robust controller, used to compensate for the impact of the reconstructed reference signal on the robustness of the control system.

5. The active control method for high-frequency vibration in a variable-weight adaptive robust compensation structure according to claim 1, characterized in that, Following S5, the method further includes: when the error signal e(n) reaches the control target, acquiring the response point signal without control and the response point signal after control; performing bandpass filtering on the response point signal without control and the response point signal after control to remove irrelevant noise; extracting the envelope of the filtered signal and removing singular values; fitting the preprocessed envelope; taking the mean value of the fitted envelope entering the stable phase; defining the time corresponding to the intersection of the mean value and the envelope fitting curve as the convergence time; defining the ratio of the mean value to the mean value of the envelope after removing singular values ​​from the response point signal without control as the control error; and determining the quantitative evaluation of the closed-loop control performance of S1-S5 based on the convergence time and control error; wherein, the control target is that the superimposed acceleration signal approaches 0.

6. The active control method for high-frequency vibration in a variable-weight adaptive robust compensation structure according to claim 1, characterized in that, The disturbance point, control point, and response point set on the controlled structure are physically linked through the structural transmission path. The control point is set in the region of the controlled structure where the stiffness is greater than the set value, so as to reduce the structural vibration response.

7. The method for active control of high-frequency vibration in a variable-weight adaptive robust compensation structure according to claim 6, characterized in that, In S4, the secondary channel consists of a power amplifier, an electromagnetic actuator, and a structural transmission path.

8. A variable-weight adaptive robust compensation type active control system for high-frequency vibration in structures, characterized in that, Based on the variable-weight adaptive robust compensation structure high-frequency vibration active control method according to any one of claims 1-7, the control system includes an acceleration sensor, a voltage acquisition module, a controller, a host computer, a voltage output module, a power amplifier, an electromagnetic actuator, and a controlled structure. The acceleration sensor is connected to the response point of the controlled structure and the voltage acquisition module, the voltage acquisition module is connected to the controller, the controller is connected to the host computer and the voltage output module, the voltage output module is connected to the power amplifier, the power amplifier is connected to the electromagnetic actuator, and the electromagnetic actuator is connected to the control point of the controlled structure. The acceleration sensor acquires the acceleration error signal e(n) of the response point, which is the result of superimposing the vibration acceleration signal d(n) of the controlled structure and the acceleration signal y(n) converted from the output force of the electromagnetic actuator. The signal is then transmitted to the voltage acquisition module via an adaptive amplifier, converted into an analog signal, and transmitted to the controller. The controller calls the control strategy preset by the FPGA module in LabVIEW on the host computer to perform calculations and generate an output u by the normalized least mean square algorithm. f (n) and the output u of the robust controller r (n) The control signal is synthesized and converted into a digital signal by the voltage output module. It is then transmitted to the power amplifier for amplification and output to the electromagnetic actuator at the control point. The electromagnetic actuator is driven to output a physical control force. This control force is converted into an acceleration signal y(n) at the response point through the structural transmission path and superimposed with d(n) at the response point to achieve the control target that the superimposed acceleration signal approaches 0.

9. The variable-weight adaptive robust compensation type active control system for high-frequency vibration in structures according to claim 8, characterized in that, The control strategy integrates a nonlinear model of the power amplifier-electromagnetic actuator mechanism, a secondary channel estimation model considering electromechanical coupling effects, an H∞ hybrid sensitivity robust controller, and an adaptive variable weight control strategy; wherein, the adaptive variable weight control strategy is a normalized least mean square algorithm and a weight determination method for the robust controller.

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