A semi-active control method for magneto-rheological damper of strong impact vibration control

Abstract

Disclosed is a semi-active control method for a magneto-rheological damper for strong impact vibration control, comprising the following steps: step one, testing and obtaining an output curve of the magneto-rheological damper; step two, building a Bouc-wen model in MATLAB / SIMULINK; step three, using a particle swarm optimization algorithm as an optimization tool to perform parameter optimization processing, comprising: determining a target function; step four, judging the accuracy identified by the optimization algorithm; step five, performing polynomial fitting; step six, evaluating the output of the model; step seven, establishing a mathematical and mechanical model of a vibration control object under the action of strong impact vibration; and step eight, based on the semi-active control model, building a MATLAB / SIMULINK control module, carrying out vibration control analysis, and calculating the control effect, and exporting the control current and the output of the magneto-rheological damper.

Description

Technical Field

[0001] This invention relates to the field of vibration control technology, and more specifically to a semi-active control method for magnetorheological dampers for strong impact vibration control, which can be applied, for example, to the semi-active vibration control of magnetorheological dampers in the fields of earthquake, wind vibration, civil and military vehicles, and artillery firing engineering. Background Technology

[0002] In recent years, with increasingly stringent vibration control requirements and objectives in the construction, equipment, and military industries, traditional passive vibration isolation technologies have struggled to adapt to changes in vibration sources. While active control technologies offer superior performance, they suffer from complex system designs and high energy consumption. Therefore, engineers and scholars have sought a method that bridges the gap between passive and active control—semi-active control. Semi-active control based on magnetorheological dampers is one such method. Parameter identification of the magnetorheological damper is crucial for accurate and effective semi-active control. Determining the appropriate method for parameter identification and applying the identified model to high-impact vibration environments is of significant practical importance. Summary of the Invention

[0003] To address the aforementioned problems, this invention proposes a semi-active control method for magnetorheological dampers (MRDs) under strong impact vibration. Based on mechanical experiments with MRDs, this technique obtains the output of the MRD under different frequencies, amplitudes, and currents. Then, it introduces a swarm optimization algorithm (Particle Swarm Optimization), which offers advantages such as simple structure, convenient parameter setting, and high computational efficiency. By proposing an effective target fitness function, the parameter optimization process becomes faster and more efficient. Furthermore, the parameter optimization range is optimized step-by-step, ultimately obtaining a relatively accurate and effective parameter optimization range, ensuring both efficiency and accuracy. Based on the identified parameters, polynomial fitting is performed between different currents and the identified parameters to predict model parameters under different currents, achieving accurate identification of eight unknown parameters in the MRD mechanical model. Finally, based on the identified model, it is applied to the semi-active control of the MRD under strong impact vibration loads.

[0004] More specifically, according to one aspect of the present invention, a semi-active control method for a magnetorheological damper for controlling strong impact vibrations is provided, comprising:

[0005] Step 1: Test and obtain the output curves of the magnetorheological damper under simple harmonic motion load, different vibration frequencies, different vibration amplitudes, and different currents or voltages;

[0006] Step 2: Build the Bouc-wen model in MATLAB / SIMULINK. The mechanical expressions are given by equations (1) and (2):

[0007]

[0008] In the formula, F represents the controllable damping force of the magnetorheological damper; x represents the relative displacement of the magnetorheological damper. This indicates the relative velocity of the magnetorheological damper; The derivative of the virtual state variable z is represented; A, β, γ, α, c0, k0, n and x0 are eight unknown parameters to be identified.

[0009] Step 3: Utilize the particle swarm optimization algorithm as an optimization tool to perform parameter optimization, including:

[0010] Set initial values ​​for the model parameters;

[0011] The objective function is determined as follows: Where m is the number of experimental data points, F Fit and F Exp The damping forces are obtained through simulation and experimentation using particle swarm optimization algorithm, respectively. This is the difference between the maximum and minimum damping force values ​​in the test data;

[0012] Set the initial search range for eight unknown parameters: A, β, γ, α, c0, k0, n, and x0, including the lower and upper limits of the search range.

[0013] Based on the optimization results, the search upper limit of the eight unknown parameters is adjusted until the results cannot be further optimized, and the final parameter search range is determined.

[0014] Based on the determined parameter search range, a particle swarm optimization algorithm is used to optimize and identify the eight unknown parameters; and

[0015] Once the accuracy of the target fitness value is achieved, the parameters for optimization and identification are exported.

[0016] Step 4: Substitute the identified parameters back into the Bouc-wen model and evaluate the error between the output obtained from the identified model and the actual output to determine the accuracy of the optimized algorithm.

[0017] Step 5: Perform polynomial fitting on the 8 identification parameters under different currents;

[0018] Step 6: Based on the mathematical expression obtained by polynomial fitting, calculate and predict the Bouc-wen model parameters under other currents, and perform output calculations to evaluate the output of the model.

[0019] Step 7: Establish mathematical and mechanical models of the vibration control object under strong impact vibration, including three models: mass, support stiffness, and magnetorheological damper; and simulate the acceleration generated by the explosive load during strong impact vibration; and

[0020] Step 8: Based on the semi-active control model, build a MATLAB / SIMULINK control module, conduct vibration control analysis, calculate the control effect, and derive the control current and magnetorheological damper output.

[0021] According to an embodiment of the present invention, step one further includes determining the accuracy of the data obtained after the magnetorheological damper is tested on the shaking table, and the output data is directly related to the accuracy of parameter identification.

[0022] According to an embodiment of the present invention, in step one, the output curve includes a force-velocity curve and a force-displacement curve.

[0023] According to an embodiment of the present invention, in step three, the parameter settings are initialized by statistical or empirical estimation of the observed data.

[0024] According to an embodiment of the present invention, the order of the polynomial fitting should not exceed 6.

[0025] According to another aspect of the present invention, a semi-active control device for a magnetorheological damper for controlling strong impact vibration is provided, comprising:

[0026] The output curve test module for magnetorheological dampers is used to test and obtain the output curves of magnetorheological dampers under simple harmonic vibration loads, different vibration frequencies, different vibration amplitudes, and different currents or voltages.

[0027] The Bouc-wen model building module is used to build the Bouc-wen model in MATLAB / SIMULINK. The mechanical expressions are represented by equations (1) and (2):

[0028]

[0029] In the formula, F represents the controllable damping force of the magnetorheological damper; x represents the relative displacement of the magnetorheological damper. This indicates the relative velocity of the magnetorheological damper; The derivative of the virtual state variable z is represented; A, β, γ, α, c0, k0, n and x0 are eight unknown parameters to be identified.

[0030] The parameter optimization module is used to perform parameter optimization using the particle swarm optimization algorithm as an optimization tool, including:

[0031] Set initial values ​​for the model parameters;

[0032] The objective function is determined as follows: Where m is the number of experimental data points, F Fit and F Exp The damping forces are obtained through simulation and experimentation using particle swarm optimization algorithm, respectively. This is the difference between the maximum and minimum damping force values ​​in the test data;

[0033] Set the initial search range for eight unknown parameters: A, β, γ, α, c0, k0, n, and x0, including the lower and upper limits of the search range.

[0034] Based on the optimization results, the search upper limit of the eight unknown parameters is adjusted until the results cannot be further optimized, and the final parameter search range is determined.

[0035] Based on the determined parameter search range, a particle swarm optimization algorithm is used to optimize and identify the eight unknown parameters; and

[0036] Once the accuracy of the target fitness value is achieved, the parameters for optimization and identification are exported.

[0037] The accuracy judgment module for the optimization algorithm is used to re-substitute the recognized parameters into the Bouc-wen model and evaluate the error between the output obtained from the recognition model and the actual output in order to judge the accuracy of the optimization algorithm recognition.

[0038] The polynomial fitting module is used to perform polynomial fitting on eight identification parameters under different currents.

[0039] The model output evaluation module is used to calculate and predict Bouc-wen model parameters under other currents based on the mathematical expression obtained by polynomial fitting, and to perform output calculations to evaluate the output performance of the model.

[0040] The high-impact vibration load simulation module is used to establish mathematical and mechanical models of vibration control objects under high-impact vibration, including three models: mass, support stiffness, and magnetorheological damper; and to simulate the acceleration generated by the explosive load during high-impact vibration.

[0041] The semi-active control construction and analysis module is used to build a MATLAB / SIMULINK control module based on the semi-active control model, conduct vibration control analysis, calculate the control effect, and derive the control current and magnetorheological damper output.

[0042] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory and one or more processors;

[0043] The memory is used to store one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors perform the method as described in this invention.

[0044] This invention proposes an effective target fitness function (objective function) to make the parameter optimization process faster and more efficient. Furthermore, the range of parameter optimization is optimized one by one, ultimately obtaining a relatively accurate and effective parameter optimization range, ensuring the efficiency and accuracy of parameter optimization. Based on the obtained identification parameters, polynomial fitting of different currents with the identification parameters is performed to predict the model parameters under different currents, achieving accurate identification of eight unknown parameters of the magnetorheological damper mechanical model. Finally, based on the identified model, it is applied to the semi-active control of the magnetorheological damper under strong impact vibration loads. Attached Figure Description

[0045] Figure 1 This is a schematic flowchart of a semi-active control method for a magnetorheological damper for controlling strong impact vibration according to an embodiment of the present invention.

[0046] Figure 2 A schematic diagram of the Bouc-Wen model structure of a semi-active control method for magnetorheological dampers for strong impact vibration control according to an embodiment of the present invention.

[0047] Figure 3 The flowchart of the Bouc-Wen model calculation for the semi-active control method of magnetorheological damper for strong impact vibration control according to an embodiment of the present invention is shown.

[0048] Figure 4 The image shows a comparison of the predicted and actual output of the magnetorheological damper under different currents in the semi-active control method of the magnetorheological damper for strong impact vibration control according to the embodiment of the present invention.

[0049] Figure 5 To simulate the strong impact vibration load in the semi-active control method of magnetorheological damper for strong impact vibration control according to the embodiment of the present invention, the near-field region (A) and far-field region (B) of nuclear explosion are taken as examples.

[0050] Figure 6 The flowchart of the semi-active control method of magnetorheological damper under strong impact vibration load in the semi-active control method of magnetorheological damper for strong impact vibration control according to the embodiment of the present invention is shown.

[0051] Figure 7 This is a schematic diagram of a semi-active control device for a magnetorheological damper used for controlling strong impact vibration according to an embodiment of the present invention; and

[0052] Figure 8 This is a schematic diagram of an electronic device structure according to an embodiment of the present invention. Detailed Implementation

[0053] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The content shown is intended to fully illustrate the content of the present invention, but is not intended to limit the present invention.

[0054] It should be understood that the models and tools involved in this invention, such as the Bouc-Wen model, MATLAB / SIMULINK, particle swarm optimization algorithm, and polynomial fitting, are known in themselves. Therefore, this invention focuses on how to combine and optimize the above-mentioned tools or models to design the semi-active control method of magnetorheological dampers for strong impact vibration control.

[0055] Figure 1 This is a schematic flowchart illustrating a semi-active control method for a magnetorheological damper used for controlling strong impact vibration according to an embodiment of the present invention. (Reference) Figure 1 The semi-active control method for magnetorheological dampers for strong impact vibration control according to an embodiment of the present invention may include:

[0056] First, a magnetorheological damper is tested to obtain its mechanical data. This includes obtaining the output curves of the damper under simple harmonic motion loads at different frequencies, amplitudes, and currents (or voltages), such as force-velocity and force-displacement curves. The output data directly affects the accuracy of parameter identification; therefore, the accuracy of the data obtained after the vibration table test must be ensured.

[0057] Step 2: Build the Bouc-wen model in MATLAB / SIMULINK. Figure 2 A schematic diagram of the Bouc-Wen model structure of a semi-active control method for magnetorheological dampers for strong impact vibration control according to an embodiment of the present invention. Figure 3 The flowchart shows the Bouc-Wen model calculation of a semi-active control method for magnetorheological dampers used for strong impact vibration control according to an embodiment of the present invention.

[0058] The Bouc-wen model, in terms of its mechanical expression, can be represented as: and The identified parameters to be identified are eight unknown parameters: A, β, γ, α, c0, k0, n, and x0. Here, F represents the controllable damping force of the magnetorheological damper, and x represents the relative displacement of the magnetorheological damper. This indicates the relative velocity of the magnetorheological damper; The derivative of the virtual state variable z is represented by c0; c0 is the viscous damping coefficient; k0 is the stiffness coefficient; x0 is the initial displacement deviation; α is the ratio of stiffness before and after yielding; z is the hysteresis variable; γ, β, and A are model parameters determined by the magnetorheological fluid and the control system; n is a coefficient.

[0059] Step 3: After building the Bouc-wen model, use the particle swarm optimization algorithm as an optimization tool to optimize the model parameters.

[0060] Specifically, the optimization process may include:

[0061] Initial values ​​for model parameters can be set by statistical analysis of observed data or empirical estimation.

[0062] The objective function is determined as follows: Where m is the number of experimental data points, F Fit and F Exp The damping forces are obtained through simulation and experimentation using particle swarm optimization algorithm, respectively. This represents the difference between the maximum and minimum damping force values ​​in the experimental data. The adaptive value function (objective function) proposed in this invention was comprehensively selected by the inventors based on their accurate understanding of the output mechanism of magnetorheological dampers and combined with engineering and optimization calculation experience. This adaptive value function has a good optimization effect on the parameters to be identified.

[0063] An initial search range is set for eight unknown parameters: A, β, γ, α, c0, k0, n, and x0, including a lower and upper limit. The definition of the initial search range can generally be determined based on prior knowledge. The upper limits of the search for each of the eight unknown parameters are adjusted according to the optimization results until the results cannot be further optimized, and the final parameter search range is determined. Based on the determined parameter search range, a particle swarm optimization algorithm is used to optimize and identify the eight unknown parameters. After achieving the accuracy required for the target fitness value, the optimized and identified parameters are derived, thus completing the optimization and identification process. Table 1 below shows the parameter identification results of the Bouc-Wen model under different currents according to the embodiment of the present invention. This table will guide the polynomial fitting of the identified parameters to different currents, providing a basis for predicting model parameters under other different currents.

[0064] Table 1: Parameter identification results of Bouc-Wen model under different currents

[0065]

[0066] Step four: Substitute the identified parameters back into the Bouc-wen model and evaluate the error between the output obtained from the identified model and the actual output to determine the accuracy of the optimized algorithm's identification. Figure 4This is a comparison chart of the predicted and actual output of the magnetorheological damper under different currents, based on the semi-active control method of the magnetorheological damper for strong impact vibration control according to an embodiment of the present invention.

[0067] Step five involves obtaining the identification parameters under different currents, then performing polynomial fitting on the eight identification parameters under different currents to predict the parameters under other currents. This step is crucial for the applicability of the invention, enabling accurate and comprehensive identification of the eight unknown parameters of the magnetorheological damper's mechanical model. Furthermore, the order of the polynomial fitting should generally not exceed six.

[0068] Then, based on the mathematical expression obtained by polynomial fitting, the Bouc-wen model parameters under other currents are calculated and predicted, and the output is calculated to evaluate the output of the model.

[0069] Finally, a mathematical and mechanical model of the vibration control object under strong impact vibration is established. This mathematical and mechanical model can include three parts: mass, support stiffness, and magnetorheological damper. The acceleration generated by the explosive load during strong impact vibration is simulated to determine a more reliable vibration load result for simulating equivalent strong impact vibration loads. Figure 5 To simulate the strong impact vibration load in the semi-active control method of magnetorheological damper for strong impact vibration control according to the embodiment of the present invention, taking the near-field region (A) and far-field region (B) of a nuclear explosion as examples, the following is a summary: Figure 5 The effects of strong impact vibration loads can be observed, including their duration, intensity, and high demands on the control system. Then, based on a semi-active control model, a MATLAB / SIMULINK control module is built to conduct vibration control analysis, calculate the control effect, and derive the control current and magnetorheological damper output. Figure 6 The attached diagram illustrates the semi-active control process of a magnetorheological damper under strong impact vibration load in a semi-active control method for strong impact vibration control according to an embodiment of the present invention. Figure 6 It can be seen how to realize the magnetorheological semi-active control process and main control idea based on the method proposed in this invention.

[0070] Figure 7 This is a schematic diagram of a semi-active control device for a magnetorheological damper used for controlling strong impact vibration according to an embodiment of the present invention. Figure 7As shown, the device includes: a magnetorheological damper output curve testing module 210, used to test and obtain the output curve of the magnetorheological damper under simple harmonic vibration load, different vibration frequencies, different vibration amplitudes, and different currents or voltages; a Bouc-wen model building module 220, used to build a Bouc-wen model in MATLAB / SIMULINK; a parameter optimization processing module 230, used to perform parameter optimization processing using particle swarm optimization algorithm as an optimization tool; an optimization algorithm identification accuracy judgment module 240: used to re-substitute the identified parameters into the Bouc-wen model and evaluate the error between the output obtained from the identified model and the actual output to judge the accuracy of the optimization algorithm identification; and a polynomial fitting module 250, used for... The system employs polynomial fitting for eight identification parameters under different currents. A model output evaluation module 260 calculates and predicts Bouc-wen model parameters under other currents based on the mathematical expressions obtained from the polynomial fitting, and performs output calculations to evaluate the model's output. A strong impact vibration load simulation module 270 establishes mathematical and mechanical models of the vibration control object under strong impact vibration, including three models: mass, support stiffness, and magnetorheological damper. It also simulates the acceleration generated by the explosive load during strong impact vibration. A semi-active control construction and analysis module 280 builds a MATLAB / SIMULINK control module based on the semi-active control model, conducts vibration control analysis, calculates the control effect, and derives the control current and magnetorheological damper output.

[0071] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, such as... Figure 8 As shown, the electronic device includes a processor 310, a memory 320, an input device 330, and an output device 340; the number of processors 310 in the electronic device can be one or more. Figure 3 Taking a processor 310 as an example; the processor 310, memory 320, input device 330, and output device 340 in the electronic device can be connected via a bus or other means. Figure 8 Taking the example of a connection between China and Israel via a bus.

[0072] The memory 320, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the semi-active control method for magnetorheological dampers for strong impact vibration control in this embodiment of the invention (e.g., magnetorheological damper output curve testing module 210; Bouc-wen model building module 220; parameter optimization processing module 230; optimization algorithm identification accuracy judgment module 240; polynomial fitting module 250; model output condition evaluation module 260; strong impact vibration load simulation module 270; semi-active control building and analysis module 280). The processor 310 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 320, thereby realizing the above-mentioned semi-active control method for magnetorheological dampers for strong impact vibration control.

[0073] This invention presents a semi-active control method for magnetorheological dampers used in high-impact vibration control. The method is ingeniously designed and has significant key features. Starting from the power output mechanism of magnetorheological dampers, it proposes a parameter identification method widely applicable to various types of magnetorheological dampers. This method is simple to implement, closely integrating swarm intelligence optimization with parameter identification. By setting a clever adaptive value function and determining an effective parameter search range, it ensures the effectiveness of parameter identification and applies accurate parameter models to the semi-active control of high-impact vibration, demonstrating strong applicability and operability. This invention transforms the complex nonlinear power output and mathematically complex parameter identification problem of magnetorheological dampers into a streamlined parameter identification process. The process is simple, requires minimal prior knowledge from engineers, and is of great significance to the development of vibration control in extreme high-impact environments.

[0074] The above description of the embodiments is intended to enable those skilled in the art to understand and apply the present invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the embodiments described herein, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.

Claims

1. A semi-active control method for magnetorheological dampers used for strong impact vibration control, characterized in that, include: Step 1: Test and obtain the output curves of the magnetorheological damper under simple harmonic motion load, different vibration frequencies, different vibration amplitudes, and different currents or voltages; Step 2: Build the Bouc-wen model in MATLAB / SIMULINK. The mechanical expressions are given by equations (1) and (2): In the formula, F represents the controllable damping force of the magnetorheological damper; x represents the relative displacement of the magnetorheological damper. This indicates the relative velocity of the magnetorheological damper; The derivative of the virtual state variable z is represented; A, β, γ, α, c0, k0, n and x0 are eight unknown parameters to be identified. Step 3: Utilize the particle swarm optimization algorithm as an optimization tool to perform parameter optimization, including: Set initial values ​​for the model parameters; The objective function is determined as follows: Where m is the number of experimental data points, F Fit and F Exp The damping forces are obtained through simulation and experimentation using particle swarm optimization algorithm, respectively. This is the difference between the maximum and minimum damping force values ​​in the test data; Set the initial search range for eight unknown parameters: A, β, γ, α, c0, k0, n, and x0, including the lower and upper limits of the search range. Based on the optimization results, the search upper limit of the eight unknown parameters is adjusted until the results cannot be further optimized, and the final parameter search range is determined. Based on the determined parameter search range, a particle swarm optimization algorithm is used to optimize and identify the eight unknown parameters; and Once the accuracy of the target fitness value is achieved, the parameters for optimization and identification are exported. Step 4: Substitute the identified parameters back into the Bouc-wen model and evaluate the error between the output obtained from the identified model and the actual output to determine the accuracy of the optimized algorithm. Step 5: Perform polynomial fitting on the 8 identification parameters under different currents; Step 6: Based on the mathematical expression obtained by polynomial fitting, calculate and predict the Bouc-wen model parameters under other currents, and perform output calculations to evaluate the output of the model. Step 7: Establish mathematical and mechanical models of the vibration control object under strong impact vibration, including three models: mass, support stiffness, and magnetorheological damper; and simulate the acceleration generated by the explosive load during strong impact vibration; and Step 8: Based on the semi-active control model, build a MATLAB / SIMULINK control module, conduct vibration control analysis, calculate the control effect, and derive the control current and magnetorheological damper output.

2. The semi-active control method for magnetorheological dampers for strong impact vibration control according to claim 1, characterized in that: Step one also includes determining the accuracy of the data obtained after the magnetorheological damper is tested on the shaking table, as the output data is directly related to the accuracy of parameter identification.

3. The semi-active control method for magnetorheological dampers for strong impact vibration control according to claim 1, characterized in that: In step one, the output curves include force versus velocity curves and force versus displacement curves.

4. The semi-active control method for magnetorheological dampers for strong impact vibration control according to claim 1, characterized in that: In step three, parameter settings are initialized through statistical or empirical estimation of observation data.

5. A semi-active control method for magnetorheological dampers for controlling strong impact vibration according to claim 1, characterized in that: The order of the polynomial fitting does not exceed 6.

6. A semi-active control device for a magnetorheological damper for controlling strong impact vibration, characterized in that, include: The output curve test module for magnetorheological dampers is used to test and obtain the output curves of magnetorheological dampers under simple harmonic vibration loads, different vibration frequencies, different vibration amplitudes, and different currents or voltages. The Bouc-wen model building module is used to build the Bouc-wen model in MATLAB / SIMULINK. The mechanical expressions are represented by equations (1) and (2): In the formula, F represents the controllable damping force of the magnetorheological damper; x represents the relative displacement of the magnetorheological damper. This indicates the relative velocity of the magnetorheological damper; The derivative of the virtual state variable z is represented; A, β, γ, α, c0, k0, n and x0 are eight unknown parameters to be identified. The parameter optimization module is used to perform parameter optimization using the particle swarm optimization algorithm as an optimization tool, including: Set initial values ​​for the model parameters; The objective function is determined as follows: Where m is the number of experimental data points, F Fit and F Exp The damping forces are obtained through simulation and experimentation using particle swarm optimization algorithm, respectively. This is the difference between the maximum and minimum damping force values ​​in the test data; Set the initial search range for eight unknown parameters: A, β, γ, α, c0, k0, n, and x0, including the lower and upper limits of the search range. Based on the optimization results, the search upper limit of the eight unknown parameters is adjusted until the results cannot be further optimized, and the final parameter search range is determined. Based on the determined parameter search range, a particle swarm optimization algorithm is used to optimize and identify the eight unknown parameters; and Once the accuracy of the target fitness value is achieved, the parameters for optimization and identification are exported. The accuracy judgment module for the optimization algorithm is used to re-substitute the recognized parameters into the Bouc-wen model and evaluate the error between the output obtained from the recognition model and the actual output in order to judge the accuracy of the optimization algorithm recognition. The polynomial fitting module is used to perform polynomial fitting on eight identification parameters under different currents; the model output evaluation module is used to calculate and predict Bouc-wen model parameters under other currents based on the mathematical expressions obtained from the polynomial fitting, and to perform output calculations to evaluate the output of the model. The high-impact vibration load simulation module is used to establish mathematical and mechanical models of vibration control objects under high-impact vibration, including three models: mass, support stiffness, and magnetorheological damper; and to simulate the acceleration generated by the explosive load during high-impact vibration. The semi-active control construction and analysis module is used to build a MATLAB / SIMULINK control module based on the semi-active control model, conduct vibration control analysis, calculate the control effect, and derive the control current and magnetorheological damper output.

7. An electronic device, characterized in that, include: Memory and one or more processors; The memory is used to store 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 method as described in any one of claims 1-5.

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