Magnetorheological damper semi-active control technology for strong impact vibration control

Abstract

The invention discloses a magneto-rheological damper semi-active control method for strong impact vibration control. The magneto-rheological damper semi-active control method comprises the steps that firstly, an output curve of a magneto-rheological damper is tested and obtained; step 2, a Bouc-wen model is built in MATLAB (matrix laboratory) / SIMULINK (single matrix laboratory); step 3, performing parameter optimization processing by using a particle swarm optimization algorithm as an optimization tool, including: determining a target function as # imgabs0 #; step 4, judging the recognition precision of the optimization algorithm; 5, performing polynomial fitting; 6, evaluating the output condition of the model; 7, establishing mathematical and mechanical models of the vibration control object under the action of strong impact vibration; and step 8, based on the semi-active control model, building an MATLAB / SIMULINK control module, carrying out vibration control analysis, calculating a control effect, and exporting a control current and a magnetorheological damper output condition.

Description

Technical Field

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

[0002] In recent years, with the increasing demand and control targets for vibration control in construction engineering, equipment and military fields, traditional passive vibration isolation technology is difficult to adapt to changes in vibration sources. Active control technology has good effects, but the design system is complex and consumes a lot of energy. For this reason, engineers and scholars have begun to seek a method between passive and active control - semi-active control. Semi-active control based on magnetorheological dampers is one of the methods. Parameter identification of magnetorheological dampers is the key to accurate and effective semi-active control. What method is used to carry out parameter identification of magnetorheological dampers and apply the identification model to the field of strong impact vibration has important practical significance. Summary of the invention

[0003] In view of the above problems, the present invention proposes a semi-active control technology of magnetorheological damper for strong impact vibration control. Based on the mechanical test of magnetorheological damper, the technology obtains the output of magnetorheological damper under different frequencies, different amplitudes and different current conditions; then introduces the swarm intelligence optimization algorithm particle swarm optimization algorithm, which has the advantages of simple structure, convenient parameter setting and high calculation efficiency; by proposing an effective target fitness function, the parameter optimization process is made faster and more effective; in addition, the range of parameter optimization is also optimized one by one, and finally a more accurate and effective parameter optimization range is obtained, which ensures the efficiency and accuracy of parameter optimization; on the basis of obtaining the identification parameters, polynomial fitting of different currents and identification parameters is performed, aiming to predict the model parameters under different currents, and realize the accurate identification of 8 unknown parameters of the mechanical model of magnetorheological damper, and finally, on the basis of the identification model, it is applied to the semi-active control of magnetorheological damper under strong impact vibration load.

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

[0005] Step 1, testing and obtaining the output curve of the magnetorheological damper under simple harmonic vibration load, different vibration frequencies, different vibration amplitudes, and different currents or voltages;

[0006] Step 2: Build the Bouc-wen model in MATLAB / SIMULINK, and the mechanical expressions are expressed as equations (1) and (2):

[0007]

[0008] Wherein, F represents the controllable damping force of the magnetorheological damper; x represents the relative displacement of the magnetorheological damper; represents the relative velocity of the MR damper; represents the differential of the virtual state variable z; A, β, γ, α, c0, k0, n and x0 are a total of 8 unknown parameters to be identified;

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

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

[0011] Determine the objective function as Where m is the number of test data points, F Fit and F Exp are the damping forces obtained by simulation and experiment through particle swarm optimization algorithm, respectively; is the difference between the maximum and minimum damping force values ​​in the test data;

[0012] Set the initial search range of 8 unknown parameters, namely A, β, γ, α, c0, k0, n and x0, including the lower and upper limits of the search range;

[0013] Adjust the search upper limits of the eight unknown parameters according to the optimization results until the results cannot be further optimized, and determine the final parameter search range;

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

[0015] After reaching the target fitness value accuracy, the optimal identification parameters are derived;

[0016] Step 4: According to the identified parameters, re-substitute into the Bouc-wen model and evaluate the error between the output obtained according to the identified model and the actual output to determine the accuracy of the optimization algorithm identification;

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

[0018] Step 6: Calculate and predict the parameters of the Bouc-wen model under other currents based on the mathematical expressions obtained by polynomial fitting, 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 of mass, support stiffness, and magnetorheological damper, and simulate the acceleration generated by the explosion load under strong impact vibration; and

[0020] Step 8: Based on the semi-active control model, build the MATLAB / SIMULINK control module, carry out 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, the step 1 further includes determining the accuracy of the data obtained after the magnetorheological damper is tested on a vibration 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 1, the output curve includes a force-speed curve and a force-displacement curve.

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

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

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

[0026] Output curve test module of magnetorheological damper , used to test and obtain the output curve of the magnetorheological damper under simple harmonic vibration load, different vibration frequencies, different vibration amplitudes, different currents or voltages;

[0027] Bouc-wen model building module, Used to build the Bouc-wen model in MATLAB / SIMULINK, the mechanical expressions are expressed as equations (1) and (2):

[0028]

[0029] Wherein, F represents the controllable damping force of the magnetorheological damper; x represents the relative displacement of the magnetorheological damper; represents the relative velocity of the MR damper; represents the differential of the virtual state variable z; A, β, γ, α, c0, k0, n and x0 are a total of 8 unknown parameters to be identified;

[0030] Parameter optimization processing module, It is used to use the particle swarm optimization algorithm as an optimization tool to perform parameter optimization processing, including:

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

[0032] Determine the objective function as Where m is the number of test data points, F Fit and F Exp are the damping forces obtained by simulation and experiment through particle swarm optimization algorithm, respectively; is the difference between the maximum and minimum damping force values ​​in the test data;

[0033] Set the initial search range of 8 unknown parameters, namely A, β, γ, α, c0, k0, n and x0, including the lower and upper limits of the search range;

[0034] Adjust the search upper limits of the eight unknown parameters according to the optimization results until the results cannot be further optimized, and determine the final parameter search range;

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

[0036] After reaching the target fitness value accuracy, the optimal identification parameters are derived;

[0037] The precision judgment module of the optimization algorithm identification is used to re-substitute the Bouc-wen model according to the identified parameters and evaluate the error between the output obtained according to the identification model and the actual output to judge the precision of the optimization algorithm identification;

[0038] Polynomial fitting module, used to perform polynomial fitting on 8 identification parameters under different currents;

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

[0040] The strong impact vibration load simulation module is used to establish the mathematical and mechanical models of the vibration control object under the strong impact vibration, including three models of mass, support stiffness, and magnetorheological damper. The strong impact vibration is simulated by the acceleration generated by the explosion load; and

[0041] The semi-active control construction and analysis module is used to build the 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, there is also provided an electronic device, 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 implement the method described in the present invention.

[0044] The present invention makes the parameter optimization process faster and more effective by proposing an effective target fitness function (objective function); in addition, the range of parameter optimization is also optimized one by one, and finally a relatively accurate and effective parameter optimization range is obtained, thereby ensuring the efficiency and accuracy of parameter optimization; on the basis of obtaining the identification parameters, polynomial fitting of different currents and identification parameters is performed, aiming to predict the model parameters under different currents, thereby realizing the accurate identification of 8 unknown parameters of the mechanical model of the magnetorheological damper, and finally, on the basis of the identification model, it is applied to the semi-active control of the magnetorheological damper under strong impact vibration loads. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of a process flow of a magnetorheological damper semi-active control technology for strong impact vibration control according to an embodiment of the present invention;

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

[0047] Figure 3 A Bouc-Wen model calculation flow chart of a magnetorheological damper semi-active control technology for strong impact vibration control according to an embodiment of the present invention;

[0048] Figure 4 A comparison diagram of the predicted output and actual output of the magnetorheological damper under different currents according to the magnetorheological damper semi-active control technology for strong impact vibration control according to the implementation scheme of the present invention;

[0049] Figure 5 For the simulation of strong impact vibration load in the semi-active control technology of magnetorheological damper for strong impact vibration control according to the implementation scheme of the present invention, the near area of ​​nuclear explosion (A) and the far area of ​​nuclear explosion (B) are taken as examples;

[0050] Figure 6 A flowchart of a semi-active control process of a magnetorheological damper under a strong impact vibration load in a semi-active control technology of a magnetorheological damper for strong impact vibration control according to an embodiment of the present invention;

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

[0052] Figure 8 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention is further described in detail below through specific embodiments in conjunction with the accompanying drawings. The shown contents are used to fully illustrate the contents of the present invention, but are not used to limit the present invention.

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

[0055] Figure 1 FIG. 1 is a flow chart of a semi-active control technology of a magnetorheological damper for strong impact vibration control according to an embodiment of the present invention. Figure 1 , a magnetorheological damper semi-active control method for strong impact vibration control according to an embodiment of the present invention may include:

[0056] First, the magnetorheological damper is tested to obtain the mechanical data of the magnetorheological damper, such as the output curve of the magnetorheological damper under simple harmonic vibration load, different vibration frequencies, different vibration amplitudes, and different currents (or voltages), including force and velocity curves and force and displacement curves. The output data is directly related to the accuracy of parameter identification, so the accuracy of the data obtained after the magnetorheological damper vibration table test should be guaranteed.

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

[0058] Bouc-wen model, the mechanical expression can be expressed as: and The parameters to be identified are A, β, γ, α, c0, k0, n and x0, a total of 8 unknown parameters, where F represents the controllable damping force of the magnetorheological damper; x represents the relative displacement of the magnetorheological damper; represents the relative velocity of the MR damper; represents the differential of the virtual state variable z; c0 is the viscous damping coefficient; k0 is the stiffness coefficient; x0 is the initial displacement deviation; α is the ratio of the 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; and 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] The initial values ​​of the model parameters can be set by statistics of observed data or empirical estimation.

[0062] Determine the objective function as Where m is the number of test data points, F Fit and F Exp are the damping forces obtained by simulation and experiment through particle swarm optimization algorithm, respectively; The fitness function (objective function) proposed in the present invention is comprehensively selected by the inventor based on the accurate understanding of the output mechanism of the magnetorheological damper and combined with engineering and optimization calculation experience. The fitness function has a good effect on optimizing the parameters to be identified.

[0063] Set the initial search range of 8 unknown parameters, namely A, β, γ, α, c0, k0, n and x0, including the lower limit and upper limit of the search range; the definition of the initial search range can generally be determined based on prior knowledge. According to the optimization results, adjust the search upper limits of the 8 unknown parameters respectively until the results cannot be further optimized, and determine the final parameter search range; according to the determined parameter search range, use the particle swarm optimization algorithm to perform optimal identification of the 8 unknown parameters; after reaching the accuracy of the target fitness value, derive the optimal identification parameters, thereby completing the optimal identification process: Table 1 below shows the Bouc-Wen model parameter identification results under different currents according to the implementation scheme of the present invention. This table will guide the polynomial fitting of the identification parameters and different currents, and provide a basis for the prediction of model parameters under other different currents.

[0064] Table 1: Bouc-Wen model parameter identification results at different currents

[0065]

[0066] Step 4: Re-substitute the Bouc-wen model according to the identified parameters, and evaluate the error between the output obtained according to the identified model and the actual output to determine the accuracy of the optimization algorithm identification. Figure 4This is a comparison diagram of the predicted output and actual output of the magnetorheological damper under different currents according to the magnetorheological damper semi-active control technology for strong impact vibration control according to an embodiment of the present invention.

[0067] Step 5, after obtaining the identification parameters under different currents, polynomial fitting is performed on the 8 identification parameters under different currents to predict the parameters under other currents. This is a step that the present invention can have good applicability, and realizes accurate and comprehensive identification of 8 unknown parameters of the magnetorheological damper mechanical model. In addition, the selection of the polynomial fitting order should generally not exceed 6th order.

[0068] Then, according to the mathematical expression obtained by polynomial fitting, the parameters of the Bouc-wen model under other currents are calculated and predicted, and the output calculation is performed 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. The mathematical and mechanical model can include three models: mass, support stiffness, and magnetorheological damper. The strong impact vibration is simulated by the acceleration generated by the explosion load to determine the simulation method of the strong impact vibration load to obtain a more reliable vibration load result. Figure 5 For the simulation of strong impact vibration load in the semi-active control technology of magnetorheological damper for strong impact vibration control according to the implementation scheme of the present invention, the near area of ​​nuclear explosion (A) and the far area of ​​nuclear explosion (B) are taken as examples. Figure 5 It can be seen that the strong impact vibration load has the following action forms, including duration, high intensity, and high requirements for the control system. Then, based on the semi-active control model, the MATLAB / SIMULINK control module is built to carry out vibration control analysis, calculate the control effect, and derive the control current and magnetorheological damper output. Figure 6 FIG. 1 is a flowchart of a semi-active control process of a magnetorheological damper under a strong impact vibration load in a semi-active control technology of a magnetorheological damper for strong impact vibration control according to an embodiment of the present invention, and the attached Figure 6 It can be seen how to realize the magnetorheological semi-active control process and the main control concept based on the method proposed in the present invention.

[0070] Figure 7 Schematic diagram of a magnetorheological damper semi-active control device for strong impact vibration control according to an embodiment of the present invention. Figure 7As shown, the device includes: a magnetorheological damper output curve test module 210, which is used to test and obtain the output curve of the magnetorheological damper under simple harmonic vibration load, different vibration frequencies, different vibration amplitudes, different currents or voltages; a Bouc-wen model building module 220, which is used to build a Bouc-wen model in MATLAB / SIMULINK; a parameter optimization processing module 230, which is used to use a particle swarm optimization algorithm as an optimization tool to perform parameter optimization processing; an optimization algorithm identification accuracy judgment module 240: used to re-substitute the Bouc-wen model according to the identified parameters, and evaluate the error between the output obtained according to the identification model and the actual output to judge the accuracy of the optimization algorithm identification; a polynomial fitting module 250, which is used Polynomial fitting is performed on the eight identification parameters under different currents; a model output evaluation module 260 is used to calculate and predict the Bouc-wen model parameters under other currents based on the mathematical expression obtained by polynomial fitting, and to perform output calculation to evaluate the output of the model; a strong impact vibration load simulation module 270 is used to establish a mathematical and mechanical model of the vibration control object under strong impact vibration, including three models of mass, support stiffness, and magnetorheological damper, and to simulate the acceleration generated by the explosion load of strong impact vibration; a semi-active control construction and analysis module 280 is used to build a MATLAB / SIMULINK control module based on the semi-active control model, carry out vibration control analysis, calculate the control effect, and derive the control current and magnetorheological damper output.

[0071] Figure 8 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. 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 the processor 310 in the electronic device can be one or more. Figure 3 A processor 310 is taken as an example; the processor 310, the memory 320, the input device 330 and the output device 340 in the electronic device can be connected via a bus or other means. Figure 8 The example of connecting through bus is taken in the following.

[0072] The memory 320, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the magnetorheological damper semi-active control method for strong impact vibration control in the embodiment of the present invention (for example, the output curve test module 210 of the magnetorheological damper; the Bouc-wen model building module 220; the parameter optimization processing module 230; the accuracy judgment module 240 for optimization algorithm identification; the polynomial fitting module 250; the model output evaluation module 260; the strong impact vibration load simulation module 270; the 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, that is, realizes the above-mentioned magnetorheological damper semi-active control method for strong impact vibration control.

[0073] The semi-active control technology of magnetorheological damper for strong impact vibration control of the present invention is ingeniously designed and has significant key points. It can start from the mechanism of magnetorheological damper output and propose a parameter identification method that is widely applicable to various types of magnetorheological dampers. The method is simple to implement and closely combines group intelligence optimization with parameter identification. It also ensures the effect of parameter identification by setting an ingenious fitness function and determining an effective parameter search range. It also applies an accurate parameter model to semi-active control of strong impact vibration and has strong applicability and operability. The invention can transform the complex nonlinear output of magnetorheological dampers and the complex mathematical model parameter identification into a streamlined parameter identification process. The process is simple and does not require much prior knowledge from engineers. It is of great significance to the development of vibration control in extreme strong impact environments.

[0074] The above description of the embodiments is to facilitate the understanding and application of the present invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the embodiments herein, and 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 present invention should be within the scope of protection of the present invention.

Claims

1. A semi-active control method of a magnetorheological damper for strong impact vibration control, characterized in that: include: Step 1, testing and obtaining the output curve of the magnetorheological damper under simple harmonic vibration load, different vibration frequencies, different vibration amplitudes, and different currents or voltages; Step 2: Build the Bouc-wen model in MATLAB / SIMULINK, and the mechanical expressions are expressed as equations (1) and (2): Wherein, F represents the controllable damping force of the magnetorheological damper; x represents the relative displacement of the magnetorheological damper; represents the relative velocity of the magnetorheological damper; represents the differential of the virtual state variable z; A, β, γ, α, c0, k0, n and x0 are a total of 8 unknown parameters to be identified; Step 3: Use the particle swarm optimization algorithm as an optimization tool to perform parameter optimization, including: Set the initial values ​​of model parameters; Determine the objective function as Where m is the number of test data points, F Fit and F Exp are the damping forces obtained by simulation and experiment through particle swarm optimization algorithm, respectively; is the difference between the maximum and minimum damping force values ​​in the test data; Set the initial search range of 8 unknown parameters, namely A, β, γ, α, c0, k0, n and x0, including the lower and upper limits of the search range; Adjust the search upper limits of the eight unknown parameters according to the optimization results until the results cannot be further optimized, and determine the final parameter search range; Based on the determined parameter search range, the particle swarm optimization algorithm is used to identify the eight unknown parameters; and After reaching the target fitness value accuracy, the optimal identification parameters are derived; Step 4: According to the identified parameters, re-substitute into the Bouc-wen model and evaluate the error between the output obtained according to the identified model and the actual output to determine the accuracy of the optimization algorithm identification; Step 5: Perform polynomial fitting on the 8 identification parameters under different currents; Step 6: Calculate and predict the parameters of the Bouc-wen model under other currents based on the mathematical expressions obtained by polynomial fitting, 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 of mass, support stiffness, and magnetorheological damper, and simulate the acceleration generated by the explosion load under strong impact vibration; and Step 8: Based on the semi-active control model, build the MATLAB / SIMULINK control module, carry out vibration control analysis, calculate the control effect, and derive the control current and magnetorheological damper output.

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

3. The semi-active control method of a magnetorheological damper for strong impact vibration control according to claim 1, characterized in that: In step 1, the output curve includes a force-velocity curve and a force-displacement curve.

4. The semi-active control method of a magnetorheological damper 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 observed data.

5. The semi-active control method of a magnetorheological damper for strong impact vibration control according to claim 1, characterized in that: The polynomial fitting order should not exceed 6.

6. A magnetorheological damper semi-active control device for strong impact vibration control, characterized in that: include: The output curve test module of the magnetorheological damper is used to test and obtain the output curve of the magnetorheological damper under the action of simple harmonic vibration load, different vibration frequencies, different vibration amplitudes, 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 expressed as equations (1) and (2): Wherein, F represents the controllable damping force of the magnetorheological damper; x represents the relative displacement of the magnetorheological damper; represents the relative velocity of the magnetorheological damper; represents the differential of the virtual state variable z; A, β, γ, α, c0, k0, n and x0 are a total of 8 unknown parameters to be identified; Parameter optimization processing module, It is used to use the particle swarm optimization algorithm as an optimization tool to perform parameter optimization processing, including: Set the initial values ​​of model parameters; Determine the objective function as Where m is the number of test data points, F Fit and F Exp are the damping forces obtained by simulation and experiment through particle swarm optimization algorithm, respectively; is the difference between the maximum and minimum damping force values ​​in the test data; Set the initial search range of 8 unknown parameters, namely A, β, γ, α, c0, k0, n and x0, including the lower and upper limits of the search range; Adjust the search upper limits of the eight unknown parameters according to the optimization results until the results cannot be further optimized, and determine the final parameter search range; Based on the determined parameter search range, the particle swarm optimization algorithm is used to identify the eight unknown parameters; and After reaching the target fitness value accuracy, the optimal identification parameters are derived; The precision judgment module of optimization algorithm identification is used to re-substitute the Bouc-wen model according to the identified parameters and evaluate the error between the output obtained according to the identified model and the actual output to judge the precision of optimization algorithm identification; The polynomial fitting module is used to perform polynomial fitting on the eight identification parameters under different currents; the model output evaluation module is used to calculate and predict the Bouc-wen model parameters under other currents based on the mathematical expressions obtained by polynomial fitting, and perform output calculation to evaluate the output of the model; The strong impact vibration load simulation module is used to establish the mathematical and mechanical models of the vibration control object under the strong impact vibration, including three models of mass, support stiffness, and magnetorheological damper. It also simulates the acceleration of the strong impact vibration caused by the explosion load; and The semi-active control construction and analysis module is used to build the 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 according to any one of claims 1 to 5.

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