Modeling method, analysis method, device, equipment, medium and product of magnetorheological damper

Abstract

The invention provides a modeling method, an analysis method, a device, equipment, a medium and a product of a magneto-rheological damper.When an inverse model of the magneto-rheological damper is constructed, test data of the magneto-rheological damper are obtained firstly, the test data are used for representing that under the condition that magneto-rheological fluid is at multiple different temperatures, and the inverse model of the magneto-rheological damper is obtained; and according to the relative motion parameters of the magneto-rheological damper and the mapping relation between the output damping force and the input current, an inverse model of the magneto-rheological damper is established according to test data. According to the inverse model constructed by the invention, the input current required by the magnetorheological damper can be determined more accurately and effectively when the magnetorheological fluid is at different temperatures, so that the accuracy and effectiveness of the inverse model of the magnetorheological damper are improved.

Description

Technical Field

[0001] The present application relates to the technical field of magnetorheological dampers, and in particular to a modeling method, analysis method, device, equipment, medium and product of a magnetorheological damper. Background Art

[0002] A magnetorheological damper is a device that provides damping force based on magnetorheological fluid, and the magnetic field of the magnetorheological fluid can be changed by controlling the current flowing through the magnetorheological virtual device, thereby changing the output damping force of the magnetorheological damper.

[0003] In the prior art, in order to analyze and use the magnetorheological damper, the control device of the magnetorheological damper can establish an inverse model of the magnetorheological damper, wherein the input of the inverse model includes the output damping force of the magnetorheological damper, and relative motion parameters such as the relative displacement and relative speed of the magnetorheological damper, and the output of the parameter model is the input current of the magnetorheological damper. The control device can calculate the input current currently required by the magnetorheological damper according to the inverse model, and then control the magnetorheological damper to provide the corresponding output damping force according to the determined input current.

[0004] How to provide a more accurate inverse model of a magnetorheological damper so as to more accurately analyze and control the magnetorheological damper is a technical problem that needs to be solved in the art. Summary of the invention

[0005] The present application provides a modeling method, analysis method, device, equipment, medium and product of a magnetorheological damper to provide a more accurate inverse model of the magnetorheological damper.

[0006] The first aspect of the present application provides a modeling method for a magnetorheological damper, comprising: obtaining test data of the magnetorheological damper, the test data being used to characterize a mapping relationship between relative motion parameters, output damping force and input current of the magnetorheological damper when the magnetorheological fluid is at multiple different temperatures; establishing an inverse model of the magnetorheological damper based on the test data; and the inverse model being used to determine the input current provided to the magnetorheological damper.

[0007] The second aspect of the present application provides an analysis method for a magnetorheological damper, comprising: obtaining the relative motion parameters, output damping force, and current temperature of the magnetorheological damper; inputting the relative motion parameters, the output damping force, and the temperature into an inverse model of the magnetorheological damper to obtain the input current of the magnetorheological damper output by the inverse model; wherein the inverse model is established according to the method provided in the first aspect of the present application.

[0008] The third aspect of the present application provides a modeling device for a magnetorheological damper, comprising: an acquisition module for acquiring test data of the magnetorheological damper, wherein the test data is used to characterize the mapping relationship between the relative motion parameters, output damping force and input current of the magnetorheological damper when the magnetorheological fluid is at multiple different temperatures; an establishment module for establishing an inverse model of the magnetorheological damper based on the test data; the inverse model is used to determine the input current provided to the magnetorheological damper.

[0009] The fourth aspect of the present application provides an analysis device for a magnetorheological damper, comprising: an acquisition module for acquiring the relative motion parameters, output damping force, and current temperature of the magnetorheological damper; an analysis module for inputting the relative motion parameters, the output damping force and the temperature into an inverse model of the magnetorheological damper to obtain the input current of the magnetorheological damper output by the inverse model; wherein the inverse model is established according to the method provided in the first aspect of the present application.

[0010] The fifth aspect of the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method described in the first aspect or the second aspect of the present application.

[0011] A sixth aspect of the present application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, implement the method described in the first aspect or the second aspect of the present application.

[0012] A seventh aspect of the present application provides a computer program product, including a computer program, which, when executed, implements the method described in the first aspect or the second aspect of the present application.

[0013] In summary, the modeling method, analysis method, device, equipment, medium and product of the magnetorheological damper provided by the present application, when constructing the inverse model of the magnetorheological damper, first obtain the test data of the magnetorheological damper, wherein the test data is used to characterize the mapping relationship between the relative motion parameters, output damping force and input current of the magnetorheological damper when the magnetorheological fluid is at multiple different temperatures, so as to establish the inverse model of the magnetorheological damper according to the test data. Based on the inverse model constructed by the present application, it is possible to more accurately and effectively determine the input current required for the magnetorheological damper when the magnetorheological fluid is at different temperatures, thereby providing a more accurate inverse model of the magnetorheological damper, and further ensuring the accuracy and effectiveness of the analysis and control of the magnetorheological damper through the inverse model. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0015] Figure 1 Schematic diagram of the working principle of magnetorheological damper;

[0016] Figure 2 is a schematic diagram of an inverse model of a magnetorheological damper;

[0017] Figure 3 A schematic flow chart of an embodiment of a modeling method for a magnetorheological damper provided in this application;

[0018] Figure 4 A schematic diagram of an embodiment of the test data provided by this application;

[0019] Figure 5 A curve diagram of the test data provided by this application;

[0020] Figure 6 A schematic diagram of the model structure of the inverse model established for this application;

[0021] Figure 7 A schematic structural diagram of another embodiment of the magnetorheological damper provided in the present application;

[0022] Figure 8 A schematic diagram of the model structure of an embodiment of an inverse model provided in this application;

[0023] Fig. 9 A schematic diagram of processing the inverse model using the Hippo optimization algorithm provided in this application;

[0024] Fig.10 A schematic diagram of the convergence curve of the inverse model provided in this application;

[0025] Fig.11 A schematic diagram of the results of verifying the inverse model provided in this application;

[0026] Fig.12 A schematic diagram of a flow chart of an embodiment of an analysis method for a magnetorheological damper provided in the present application;

[0027] Fig.13 A structural schematic diagram of an embodiment of a modeling device for a magnetorheological damper provided in the present application;

[0028] Fig.14A schematic structural diagram of an embodiment of an analysis device for a magnetorheological damper provided in the present application;

[0029] Fig.15 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] A magnetorheological damper is a device that provides damping force based on magnetorheological fluid. Based on the characteristics of magnetorheological fluid that changes physical properties under the action of an external magnetic field, the output damping force provided by the magnetorheological virtual device can be adjusted by changing the input current of the magnetorheological damper. Magnetorheological dampers are widely used in the automotive, medical, aerospace and other fields due to their simple structure, low power consumption, large output damping force and rapid response.

[0033] Figure 1 The schematic diagram of the working principle of the magnetorheological damper is shown in Figure 2. Figure 1 As shown, an external power supply provides an input current I to the magnetorheological damper to provide a magnetic field to the magnetorheological fluid in the magnetorheological damper, thereby changing the characteristics of the magnetorheological fluid, and further adjusting the output damping force F of the magnetorheological damper based on the relative displacement x and the relative velocity v.

[0034] In order to more effectively control the magnetorheological damper and more accurately determine the current value of the input current I that needs to be provided to the magnetorheological damper, the user of the magnetorheological damper can analyze the magnetorheological damper through an inverse model of the magnetorheological damper.

[0035] For example, Figure 2 is a schematic diagram of the inverse model of a magnetorheological damper, as shown in Figure 2 The input parameters of the inverse model of the magnetorheological damper shown are the output damping force F, relative displacement x and relative velocity v of the magnetorheological damper, and the output parameter of the inverse model is the input current I of the magnetorheological damper. Therefore, for the user of the magnetorheological damper, the current relative displacement x and relative velocity v of the magnetorheological damper, as well as the required output damping force can be input into the inverse model, so as to determine the current value of the input current I that needs to be provided to the magnetorheological damper according to the output parameters of the inverse model, and then the magnetorheological damper can be more effectively controlled accordingly according to the more accurate input current I.

[0036] Specifically, in the actual working process, the magnetorheological damper forms a magnetic field by inputting current to excite the magnetorheological damper, and adjusts the current intensity to adjust the magnetic field intensity and then adjusts the damping force output by the damper. Due to the strong nonlinear and hysteresis characteristics of the magnetorheological damper, how to design an accurate and reliable control method to determine the input current intensity to excite the magnetorheological damper to accurately output the expected damping force is the key to ensuring that the magnetorheological damper has a good shock absorption effect.

[0037] For including Figure 2 The inverse model provided in the prior art shown generally determines the input current I based on the relative motion parameters of the magnetorheological damper and the output damping force F, and does not consider the influence of temperature on the magnetorheological damper.

[0038] In the actual use of the magnetorheological damper, not only will the ambient temperature of the magnetorheological damper change greatly, but the magnetorheological damper will also convert part of the mechanical energy into heat during the relative motion, causing the temperature of the magnetorheological damper to rise.

[0039] Furthermore, when the temperature of the magnetorheological damper changes, the characteristics of the magnetorheological fluid in the magnetorheological damper will also change due to the influence of the temperature, thereby causing the output damping force of the magnetorheological damper to change.

[0040] Therefore, since the inverse model in the prior art assumes that the temperature of the magnetorheological fluid of the magnetorheological damper is constant when establishing the model, and ignores the influence of temperature on the magnetorheological damper, the established inverse model cannot accurately and effectively analyze and control the magnetorheological damper under different temperatures, thereby affecting the shock absorbing effect of the magnetorheological damper.

[0041] Based on this, the present application provides a magnetorheological damper and its modeling, analysis method, equipment and product to consider the influence of temperature on the magnetorheological damper and provide a more accurate inverse model of the magnetorheological damper. The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0042] Figure 3 This is a flow chart of an embodiment of a modeling method for a magnetorheological damper provided in this application, such as Figure 3 The method shown can be used to establish an inverse model of a magnetorheological damper, and can be executed by any controller or electronic device with relevant data processing capabilities. For example, the controller can be a CPU, MCU, SoC, etc., and the electronic device can be a computer, a server, or a service station, etc. Specifically, Figure 3 The modeling approach for the magnetorheological damper shown includes:

[0043] S101: Acquire test data of a magnetorheological damper.

[0044] Specifically, in the embodiment of the present application, the test data can be used to characterize the mapping relationship between the relative motion parameters, the output damping force and the input current of the magnetorheological damper when the magnetorheological fluid is at multiple different temperatures. That is, the test data provided in this embodiment can be used to describe the mechanical behavior of the magnetorheological damper at different temperatures. Therefore, the test data considers more comprehensive factors, which is conducive to improving the accuracy of the inverse model established subsequently.

[0045] For example, Figure 4 A schematic diagram of an embodiment of the test data provided in this application, such as Figure 4 As shown, the test data provided by this application includes: when the magnetorheological fluid is at different temperatures T1, T2, etc., the mapping relationship between the relative motion parameters, output damping force and input current of the magnetorheological damper at each temperature. Figure 4 In the example shown, the test data includes: when the magnetorheological fluid is at temperature T1, the mapping relationship between the relative displacement x11, relative velocity v11, output damping force F11 and input current I11 of the magnetorheological damper, the mapping relationship between the relative displacement x12, relative velocity v12, output damping force F12 and input current I12 of the magnetorheological damper... the mapping relationship between the relative displacement x1N, relative velocity v1N, output damping force F1N and input current I1N of the magnetorheological damper, Figure 4 In the example shown, N mapping relationships are included in each temperature case as an example rather than a limitation. It can be understood that the present application does not make any specific limitation on the number of temperatures in the test data and the number of mapping relationships in each temperature case.

[0046] For example, Figure 5 A curve diagram of the test data provided by this application, such as Figure 5 The corresponding relationship between the relative displacement of the magnetorheological damper and the output damping force when the magnetorheological fluid is at different temperatures is shown. Figure 5 The curve diagram shown can also be called the dynamometer diagram of the magnetorheological damper at different temperatures. Figure 5 In the example shown, a schematic diagram is shown in which the output damping force F changes between -2000N and 2500N when the relative displacement of the magnetorheological damper changes between -5mm and 5mm at temperatures of 40°C, 30°C, 20°C, 10°C, 0°C, -10°C and -20°C. Other parameters can be set to displacement amplitude of 5mm, displacement frequency of 16Hz, action current of 1A, etc. It can be seen that when the magnetorheological damper is at different temperatures, even if the relative motion parameters of the magnetorheological damper are the same, its output damping force F will be affected by temperature and thus change.

[0047] S102: Construct a dynamic model of the magnetorheological damper according to the test data obtained in S101.

[0048] For example, Figure 6 The schematic diagram of the model structure of the inverse model established for this application is as follows: Figure 6 The inputs of the inverse model shown are the relative motion parameters of the magnetorheological damper, the output damping force F, and the characteristic parameter K corresponding to the temperature of the magnetorheological fluid, wherein the relative motion parameters include at least one of the relative displacement x and the relative velocity v of the magnetorheological damper.

[0049] In one embodiment, the inverse model provided in the present application specifically inputs a characteristic parameter K corresponding to the temperature of the magnetorheological damper, wherein the characteristic parameter K corresponding to the temperature is used to characterize the influence of the temperature of the magnetorheological fluid of the magnetorheological damper on the output damping force.

[0050] In one embodiment, the characteristic parameter K corresponding to the temperature can be expressed by the parameter expression of the dynamic model of the magnetorheological damper in the following formula 1:

[0051] Formula 1

[0052] Among them, K is the characteristic parameter corresponding to temperature, f is the hysteresis characteristic parameter, c is the damping characteristic parameter, k is the stiffness characteristic parameter, x is the relative displacement, v is the relative velocity, and F is the output damping force. And there is , α and b are constants.

[0053] In one embodiment, the characteristic parameter K corresponding to the temperature can be specifically expressed by the following formula 2:

[0054] Formula 2

[0055] Among them, α1, α2, α3 and α4 are constants, and t is the temperature value of the magnetorheological damper.

[0056] The embodiments of this application provide Figure 6 The inverse model property shown is similar to Figure 2 Compared with the inverse model in the prior art shown in the figure, since the test data obtained when establishing the inverse model can be used to describe the mechanical behavior of the magnetorheological damper at different temperatures, the inverse model of the magnetorheological damper constructed based on the test data provided in the embodiment of the present application, the input parameter also includes the characteristic parameter K corresponding to the temperature of the magnetorheological fluid, so that the magnetorheological damper can describe the influence of temperature on the input current of the magnetorheological damper, avoiding the error of the input current obtained due to the inverse model not considering the temperature influence, so that the inverse model provided in the present application is closer to the actual usage condition.

[0057] Furthermore, the inverse model constructed based on the embodiment of the present application can more accurately and effectively describe the input current corresponding to the output damping force of the magnetorheological damper when the magnetorheological fluid is at different temperatures, thereby providing a more rigorous theoretical support for the analysis and control of the magnetorheological damper. Based on the inverse model, the input current required for the magnetorheological damper can be determined more accurately and effectively under different temperatures, thereby providing a more accurate inverse model of the magnetorheological damper, thereby ensuring the accuracy and effectiveness of the analysis and control of the magnetorheological damper.

[0058] Figure 7 A schematic diagram of the structure of another embodiment of the magnetorheological damper provided in the present application is shown in FIG. Figure 7 The magnetorheological damper shown in Figure 6 Based on the inverse model shown, the input of the inverse model specifically includes: the relative motion parameters of the magnetorheological damper at the current sampling moment and multiple historical sampling moments before the current sampling moment, the output damping force F, and the characteristic parameter K corresponding to the temperature of the magnetorheological fluid at each sampling moment.

[0059] exist Figure 7In the example shown, taking the current sampling moment t1 as an example, the two historical sampling moments before the current sampling moment t1 include the previous sampling moment t2 of the current sampling moment t1, and the previous sampling moment t3 of the previous sampling moment t2. Then, when constructing the dynamic model of the magnetorheological damper according to the acquired test data in S102, the inverse model of the magnetorheological damper is established specifically according to the test data at the current sampling moment and the multiple historical sampling moments before the current sampling moment. For example, according to the output damping force F1, relative displacement x1, relative velocity v1, characteristic parameter K1 corresponding to the temperature at the current sampling moment t1, and input current I, the output damping force F2, relative displacement x2, relative velocity v2, characteristic parameter K2 corresponding to the temperature at the previous sampling moment t2, and the output damping force F3, relative displacement x3, relative velocity v3, characteristic parameter K3 corresponding to the temperature at the previous sampling moment t3, the inverse model of the magnetorheological damper is jointly constructed, thereby obtaining the following: Figure 7 The model structure of the inverse model is shown.

[0060] from Figure 7 It can be seen from the model structure of the inverse model shown in that when the input current I is determined by the inverse model, the input of the inverse model includes the relative motion parameters of the magnetorheological damper at the current sampling moment and multiple historical sampling moments before the current sampling moment, the output damping force F, and the characteristic parameter K corresponding to the temperature of the magnetorheological fluid, and Figure 6 The inverse model shown in only determines the input current I based on the relative motion parameters of the magnetorheological damper, the output damping force F, and the characteristic parameter K corresponding to the temperature of the magnetorheological fluid at the current sampling moment. Compared with the inverse model shown in the inverse model, the inverse model has more input physical quantities, specifically adding the relative motion parameters, the output damping force F, and the characteristic parameter K corresponding to the temperature of the magnetorheological fluid at the historical sampling moments, thereby providing the inverse model with more information and features through the physical quantities at the historical sampling moments, so as to improve the performance of the inverse model, and further enhance the accuracy and effectiveness of the inverse model.

[0061] In another inverse model training method, the inverse model of the magnetorheological damper can be jointly constructed based on the output damping force F1, relative displacement x1, relative velocity v1, corresponding characteristic parameters K1 of temperature and input current I at the current sampling moment t1, the output damping force F2, relative displacement x2, relative velocity v2, corresponding characteristic parameters K2 of temperature and input current I2 at the previous sampling moment t2, and the output damping force F3, relative displacement x3, relative velocity v3, corresponding characteristic parameters K3 of temperature and input current I3 at the previous sampling moment t3. For the inverse model constructed by this training method, the input parameters are the output damping force F1, relative displacement x1, relative velocity v1, and the corresponding characteristic parameter K1 of the temperature at the current sampling moment t1, the output damping force F2, relative displacement x2, relative velocity v2, and the corresponding characteristic parameter K2 of the temperature at the previous sampling moment t2, and the input current I2, the output damping force F3, relative displacement x3, relative velocity v3, and the corresponding characteristic parameter K3 of the temperature at the previous sampling moment t3, and the output parameter is the input current I at the current moment.

[0062] More specifically, the inverse model provided by the present application can be a neural network model, specifically a BP neural network model with a two-layer feedforward structure, and the neural network model includes an input layer, a hidden layer and an output layer. Among them, the hidden layer includes sigmoid hidden neurons, and the output layer includes a two-layer feedforward neural network of linear output neurons, the hidden layer size is 25 layers, the training algorithm is Bayesian regularization, and the performance function is mean square error. The inverse model implemented by the neural network model in the present application has stronger feature extraction ability and learning ability, and can ensure the accuracy of the established inverse model.

[0063] For example, Figure 8 A schematic diagram of the model structure of an embodiment of the inverse model provided in this application, such as Figure 8 by Figure 7 The inverse model shown in the figure takes a neural network model as an example, and the inverse model includes: an input layer, a hidden layer and an output layer. The input layer is used to receive external input data: output damping force F1, relative displacement x1, relative velocity v1, characteristic parameter K1 corresponding to temperature, output damping force F2, relative displacement x2, relative velocity v2, characteristic parameter K2 corresponding to temperature, output damping force F3, relative displacement x3, relative velocity v3, characteristic parameter K3 corresponding to temperature, and pass the characteristics of the input data to the hidden layer. The hidden layer is used to perform nonlinear transformation and other processing on the data to extract the characteristics of the data, and through the setting of multiple hidden layers, more abstract features in the data can be extracted layer by layer. The output layer can be used to generate the final prediction result of the input current I according to the characteristics of the data extracted by the hidden layer.

[0064] In order to train Figure 8When the device for training the model obtains the test data, it also obtains the model structure of the inverse model and the initial value of at least one model parameter in the model structure. The model parameters can be specifically as follows: Figure 8 The weights and thresholds of each node in the neural network model shown. Subsequently, based on the acquired test data, the target value of at least one model parameter in the inverse model is determined, so that the inverse model is constructed based on the determined target value of at least one model parameter and the model structure of the inverse model. The present application constructs the inverse model by model training, which has a more direct construction logic, and the inverse model obtained by training has higher accuracy and effectiveness.

[0065] In one embodiment, if Figure 8 When the output layer of the inverse model shown is used as the error function through the root mean square, in the process of model training, the temperature, motion parameters and output damping force in the test data can be used as the input of the model, substituted into the model structure, and the test output data of the model can be obtained. Then, according to the root mean square of the difference between the test output data and the theoretical output data of the inverse model, the value of at least one parameter in the inverse model is adjusted. Finally, when the determined root mean square is the minimum, the value of at least one model parameter is the target value. Among them, the error function is used to optimize the parameters in the inverse model, the calculation principle is intuitive, easy to understand and implement, and has a high calculation efficiency, thereby improving the efficiency of constructing the inverse model of the magnetic variable damper.

[0066] In one embodiment, after obtaining the model structure of the inverse model and the initial value of at least one model parameter in the model structure, the initial value of at least one model parameter in the model structure is also optimized, and then, based on the test data and the optimized model structure, the target value of at least one model parameter of the inverse model is determined. Among them, since the model parameters in the model structure are determined based on empirical values, there is a large error with the target value. If the model is trained directly based on the initial value, in addition to the poor accuracy of the obtained model, it will also greatly increase the amount of calculation during model training, affecting the calculation efficiency and training efficiency of the model. Therefore, in the embodiment of the present application, before training the model based on the test data, at least one model parameter in the model structure is also adjusted and optimized, so as to speed up the convergence speed and learning efficiency during model training, thereby improving the accuracy of the inverse model.

[0067] In a specific implementation, the device for establishing the inverse model can specifically use a hippopotamus optimization algorithm to optimize the initial value of at least one model parameter in the model structure. The basic strategy of the hippopotamus optimization algorithm includes three stages: updating the position of the hippopotamus population in the pond or river, defending against predators, and escaping from predators.

[0068] For example, Fig. 9 The schematic diagram of using the Hippo optimization algorithm to process the inverse model provided in this application is as follows: Fig. 9 As shown, after the model structure of the inverse model is determined, the number of hippopotamus population N and the maximum number of iterations T are set according to the neural network structure of the inverse model, and the position of the hippopotamus in the hippopotamus population is initialized, and then the position of the hippopotamus in the hippopotamus population is updated according to the current iteration result when the iteration starts, wherein the position of the male hippopotamus in the hippopotamus population in the pond or river is calculated specifically according to the following formula three:

[0069] Formula 3

[0070] in, For the position of male hippos, Write the optimal hippo position for the current iteration, is the current position of the male hippopotamus during the iteration process, and To calculate the coefficient, i is the index of the male hippopotamus in the population and m is the number of dimensions during iteration.

[0071] And, the position of female hippopotamus or juvenile hippopotamus in the hippopotamus population is calculated according to the following formula 4:

[0072] Formula 4

[0073] in, , For the location of female hippos or juvenile hippos, is the optimal hippo position of the current iteration, is the average value of randomly selected hippos, is the current position of the female hippopotamus during the iteration process, and To calculate the coefficient, A juvenile hippopotamus separated from the group and To calculate the coefficient, is the lower bound or reference position of the predator in the jth dimension, is the upper bound or target position of the predator in the jth dimension.

[0074] Subsequently, the position of the predator is randomly generated, and the position of the hippo population is updated according to the defense strategy. Specifically, the position of the predator can be determined according to the following formula 5:

[0075] Formula 5

[0076] in, for the location of the predator, is the lower bound or reference position of the predator in the jth dimension, is the upper bound or target position of the predator in the jth dimension. is the calculation coefficient, and m is the number of dimensions during iteration.

[0077] And, according to the following formula 6, determine the defense mode adopted by the hippopotamus when the hippopotamus is attacked by a predator or invades its territory:

[0078] Formula 6

[0079] in, is the posture of the hippopotamus facing the predator, RL is the random vector determined by the Levy flight strategy, is the position of the predator in the jth dimension, is a calculation parameter used to adjust the ratio, c and d are constants used to control the size of the denominator, g is a calculation parameter that changes periodically between [0-1], and D is a distance-related constant. To calculate the coefficient, is the objective function value of the predator and hippopotamus individuals.

[0080] Subsequently, when the hippopotamus cannot use defensive reactions to repel predators, they will choose to flee the current area. When the new location chosen by the hippopotamus improves the objective function value, it is considered that the hippopotamus has moved to a safer location, and the location of the hippopotamus population is updated with the escape strategy according to the following formula 7:

[0081] Formula 7

[0082] in, is the current position of the hippopotamus during the iteration process, and is the calculation coefficient. Finally, the optimal position of the hippo population in the iteration process is determined by the following formula 8:

[0083] Formula 8

[0084] in, is the objective function value, The nearest safe location found for the hippopotamus. is the objective function value of the safe position during the iteration process.

[0085] When the optimal position of the hippopotamus population is determined during the iteration process, the initial value of at least one model parameter in the model can be adjusted according to the determined optimal position to achieve optimization of the initial value.

[0086] After the initial value of at least one model parameter in the model structure of the inverse model is optimized by the above-mentioned Hippopotamus optimization algorithm, the target value of at least one model parameter in the inverse model is determined to initialize the weights and thresholds in the neural network. Then, the inverse model can be trained according to the acquired test data, and the error function of the neural network can be tested. When it is determined that the error function meets the termination condition, the inverse model finally obtained by training is output. If the termination condition of the error function is not met, it returns to iterate again, initializes the Hippopotamus position, and re-optimizes the initial value of at least one parameter according to the Hippopotamus optimization algorithm.

[0087] Fig.10 A schematic diagram of the convergence curve of the inverse model provided in the present application, wherein, taking the inverse model as a neural network model as an example, the solid line shows the corresponding relationship between the number of iterations and the mean square error during the training process of the inverse model without parameter optimization processing, and the dotted line shows the corresponding relationship between the number of iterations and the mean square error during the training process of the inverse model after the Hippo optimization algorithm is processed. It can be seen that, under the same number of iterations, the inverse model after the Hippo optimization algorithm has a better degree of convergence during the training process, the mean square error decreases faster, and convergence and stability are achieved more effectively.

[0088] In one embodiment, after the inverse model of the magnetorheological damper is constructed, the accuracy of the inverse model is further verified. For example, after the inverse model is constructed, the temperature, relative motion parameters and output damping force are tested on the inverse model to obtain the test input current output by the inverse model. Also, when the magnetorheological fluid of the magnetorheological damper is at the test temperature, the actual input current required by the magnetorheological damper at the same relative motion parameters and output damping force is obtained. Thus, the inverse model is verified based on the test input current and the actual input current. Specifically, the inverse model can be verified based on whether the test input current output by the inverse model can correctly excite the magnetorheological damper to provide the corresponding output damping force.

[0089] For example, Fig.11 The schematic diagram of the results of verifying the inverse model provided in this application shows that, within the time range of 0-500s, after the calculated input current of the inverse model constructed by the modeling method provided in this application is provided to the magnetorheological damper, the calculated result of the predicted output damping force is closer to the target output damping force actually required by the magnetorheological damper, making the inverse model have higher accuracy.

[0090] Fig.12 A schematic diagram of a flow chart of an embodiment of an analysis method for a magnetorheological damper provided in the present application, as shown in FIG. Fig.12 The analytical method for the magnetorheological damper shown includes:

[0091] S201: Acquire the temperature of the magnetorheological fluid of the magnetorheological damper, as well as the relative motion parameters and output damping force of the magnetorheological damper.

[0092] S202: Input the temperature, relative motion parameters and output damping force into the inverse model of the magnetorheological damper to obtain the input current of the magnetorheological damper output by the inverse model, wherein the inverse model is established according to the modeling method of the magnetorheological damper in the aforementioned embodiment of the present application.

[0093] In summary, in the analysis method of the magnetorheological damper provided in this embodiment, the inverse model can be used to more accurately and effectively describe the input current required for the magnetorheological damper to provide an output damping force when the magnetorheological fluid is at different temperatures, thereby providing a more rigorous theoretical support for the analysis and control of the magnetorheological damper. Based on the inverse model, the magnetorheological damper can be analyzed more accurately and effectively.

[0094] In the aforementioned embodiments of the present application, the modeling method of the magnetorheological damper provided in the embodiments of the present application is introduced. In order to realize the various steps and functions in the modeling method of the magnetorheological damper provided in the embodiments of the present application, the device as the execution subject can be implemented by hardware structure and / or software module, for example, in the form of hardware structure, software module, or hardware structure plus software module to realize the above functions. Whether one of the above functions is executed in the form of hardware structure, software module, or hardware structure plus software module depends on the specific application and design constraints of the technical solution.

[0095] For example, Fig.13 This is a structural schematic diagram of an embodiment of a modeling device for a magnetorheological damper provided in the present application, as shown in FIG. Fig.13 The modeling device 1000 of the magnetorheological damper shown includes: an acquisition module 1001 and an establishment module 1002. The acquisition module 1001 is used to acquire test data of the magnetorheological damper, and the test data is used to characterize the mapping relationship between the relative motion parameters, output damping force and input current of the magnetorheological damper when the magnetorheological fluid is at multiple different temperatures. The establishment module 1002 is used to establish an inverse model of the magnetorheological damper according to the test data; the inverse model is used to determine the input current provided to the magnetorheological damper.

[0096] The specific implementation method and principle of the modeling device of the magnetorheological damper mentioned above refer to the description of the modeling method of the magnetorheological damper mentioned above, which will not be repeated here.

[0097] For example, Fig.14 This is a structural schematic diagram of an embodiment of an analysis device for a magnetorheological damper provided in the present application, as shown in FIG. Fig.14The analysis device 2000 of the magnetorheological damper shown includes: an acquisition module 2001 and an analysis module 2002. The acquisition module 2001 is used to obtain the relative motion parameters, output damping force, and current temperature of the magnetorheological damper. The analysis module 2002 is used to input the relative motion parameters, the output damping force, and the temperature into the inverse model of the magnetorheological damper to obtain the input current of the magnetorheological damper output by the inverse model; wherein the inverse model is established according to the modeling method of the magnetorheological damper provided in the present application.

[0098] The specific implementation and principle of the above-mentioned analysis device for magnetorheological damper refer to the description of the above-mentioned analysis method for magnetorheological damper, which will not be repeated here.

[0099] It should be noted that it should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. And these modules can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the processing module can be a separately established processing element, or it can be integrated in a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called and executed by a processing element of the above device. The function of the above-mentioned module is determined. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

[0100] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more microprocessors (digital signal processors, DSP), or one or more field programmable gate arrays (FPGA), etc. For another example, when a module above is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0101] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)).

[0102] For example, Fig.15 A schematic diagram of the structure of an electronic device provided in this application, such as Fig.15 The electronic device 3000 shown includes one or more processors 3001 and a memory 3002 ; the memory 3002 is used to store computer-executable instructions, and the processor 3001 can execute the computer-executable instructions stored in the memory 3002 .

[0103] When the computer executable instructions are executed by the processor 3001, the processor 3001 implements a modeling method for a magnetorheological damper as in any of the aforementioned embodiments of the present application; or, when the computer executable instructions are executed by the processor 3001, the processor 3001 implements an analysis method for a magnetorheological damper as in any of the aforementioned embodiments of the present application.

[0104] In one embodiment, if Fig.15 The electronic device 3000 shown also includes a communication interface 3003 , wherein the processor 3001 can communicate with other devices through the communication interface 3003 , for example, to obtain test data.

[0105] The present application also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed, they can be used to implement a modeling method for a magnetorheological damper as described in any of the aforementioned embodiments of the present application.

[0106] The present application also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed, they can be used to implement an analysis method for a magnetorheological damper as described in any of the aforementioned embodiments of the present application.

[0107] An embodiment of the present application also provides a chip for executing instructions, wherein the chip is used to execute any of the magnetorheological damper modeling methods described above in the present application.

[0108] An embodiment of the present application also provides a chip for executing instructions, wherein the chip is used to execute any of the analysis methods for magnetorheological dampers described above in the present application.

[0109] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, implements any of the magnetorheological damper modeling methods described above in the present application.

[0110] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, implements any of the aforementioned magnetorheological damper analysis methods of the present application.

[0111] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A modeling method for a magnetorheological damper, characterized in that: include: Acquire test data of the magnetorheological damper, wherein the test data is used to characterize a mapping relationship between a relative motion parameter, an output damping force, and an input current of the magnetorheological damper when the magnetorheological fluid is at a plurality of different temperatures; An inverse model of the magnetorheological damper is established according to the test data; the inverse model is used to determine an input current provided to the magnetorheological damper.

2. The method according to claim 1, characterized in that The inverse model of the magnetorheological damper is established according to the test data, comprising: Acquire a model structure of the inverse model and an initial value of at least one model parameter in the model structure; Determining a target value of at least one model parameter of the inverse model according to the test data; The inverse model is constructed based on the target value of the at least one model parameter and the model structure of the inverse model.

3. The method according to claim 2, characterized in that Determining a target value of at least one model parameter of the inverse model according to the test data includes: Substituting the test data into the model structure to obtain test output data of the inverse model; adjusting the value of the at least one parameter according to the root mean square of the difference between the test output data and the theoretical output data of the inverse model; When it is determined that the root mean square is minimum, the value of the at least one model parameter is the target value.

4. The method according to claim 3, characterized in that The inverse model includes a neural network model, which is a two-layer feedforward structure. The neural network includes an input layer, a hidden layer and an output layer. The hidden layer includes hidden neurons, and the output layer includes linear output neurons.

5. The method according to claim 4, characterized in that After obtaining the model structure of the inverse model and the initial value of at least one model parameter in the model structure, the method further includes: An initial value of at least one model parameter in the model structure is optimized.

6. The method according to claim 5, characterized in that The optimizing process of the initial value of at least one model parameter in the model structure comprises: According to the model structure of the inverse model, the number of hippopotamus population N and the maximum number of iterations T are set; Based on the results of the current iteration, determine the location of the hippopotamus population; Randomly generate the location of predators and update the location of the hippo population based on the defense strategy; When it is determined that a defense strategy cannot be adopted, the location of the hippo population is updated according to the escape strategy; According to the optimal position of the hippo population during the iteration process, the initial value of at least one model parameter is adjusted.

7. The method according to any one of claims 1 to 6, characterized in that: The inverse model of the magnetorheological damper is established according to the test data, comprising: The inverse model of the magnetorheological damper is established based on the test data of the magnetorheological damper at each sampling moment in a current sampling moment and multiple historical sampling moments before the current sampling moment.

8. The method according to any one of claims 1 to 6, characterized in that: After establishing the inverse model of the magnetorheological damper according to the test data, the method further includes: Testing the temperature, relative motion parameters and output damping force of the inverse model to obtain a test input current output by the inverse model; Obtaining the actual input current required by the magnetorheological damper when the magnetorheological fluid of the magnetorheological damper is at the test temperature and the relative motion parameters and the output damping force are the same; The inverse model is verified according to the test input current and the actual input current.

9. The method according to any one of claims 1 to 6, characterized in that: The relative motion parameter includes at least one of a relative displacement or a relative speed of the magnetorheological damper.

10. The method according to any one of claims 1 to 6, characterized in that: The test data includes: characteristic parameters corresponding to temperature, and the characteristic parameters corresponding to temperature are used to characterize the influence of the temperature of the magnetorheological fluid of the magnetorheological damper on the output damping force.

11. A method for analyzing a magnetorheological damper, characterized in that: include: Obtaining the relative motion parameters, output damping force, and temperature of the magnetorheological fluid of the magnetorheological damper; The relative motion parameters, the output damping force and the temperature are input into the inverse model of the magnetorheological damper to obtain the input current of the magnetorheological damper output by the inverse model; wherein the inverse model is established according to the method described in any one of claims 1-10.

12. A modeling device for a magnetorheological damper, characterized in that: include: An acquisition module, used for acquiring test data of a magnetorheological damper, wherein the test data is used for characterizing a mapping relationship between a relative motion parameter, an output damping force and an input current of the magnetorheological damper when the magnetorheological fluid is at a plurality of different temperatures; A building module is used to build an inverse model of the magnetorheological damper according to the test data; the inverse model is used to determine the input current provided to the magnetorheological damper.

13. An analysis device for a magnetorheological damper, characterized in that: include: An acquisition module, used to acquire the relative motion parameters, output damping force, and current temperature of the magnetorheological damper; An analysis module is used to input the relative motion parameters, the output damping force and the temperature into an inverse model of the magnetorheological damper to obtain an input current of the magnetorheological damper output by the inverse model; wherein the inverse model is established according to the method described in any one of claims 1 to 10.

14. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1-11.

15. A computer-readable storage medium, characterized in that: Computer executable instructions are stored, and when the computer executable instructions are executed, the method according to any one of claims 1 to 11 is implemented.

16. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 11 when being executed.

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