Magnetorheological damper parameter identification method and system based on improved grey wolf optimization algorithm

Abstract

The invention provides a magneto-rheological damper parameter identification method and system based on an improved grey wolf optimization algorithm. The magneto-rheological damper parameter identification method comprises the steps that a forward mechanical model of a magneto-rheological damper is established, and a plurality of parameters needing to be identified are determined; performing parameter identification on the forward mechanical model by using an improved grey wolf algorithm to obtain identification results of a plurality of parameters; according to the identification result, a simulation output result of the forward mechanical model is obtained; according to the simulation output result and the experiment output result of the magneto-rheological damper, whether the forward mechanical model meets a preset evaluation accuracy condition or not is judged; according to a judgment result of a preset evaluation accuracy condition, a final forward mechanical model is generated, and a nonlinear convergence factor and a dynamic weight strategy are introduced, so that the global search capability and the local development precision of the algorithm are remarkably improved, and the problem that parameter identification is easy to fall into local optimum is effectively solved; and reliable support is provided for accurate modeling and control of the magnetorheological damper.

Description

Technical Field

[0001] This invention relates to the technical field of magnetorheological dampers, and in particular to a method and system for identifying magnetorheological damper parameters based on an improved gray wolf optimization algorithm. Background Technology

[0002] Magnetorheological dampers (MRDs) are characterized by fast response speed and a wide adjustable damping force range, and are widely used in vehicle suspension, building seismic isolation, and other fields. However, MRDs also exhibit strong nonlinearity and hysteresis characteristics, making accurate modeling a key challenge in control. Traditional parameter identification methods such as least squares and genetic algorithms have significant shortcomings: least squares is sensitive to initial values ​​and prone to getting trapped in local optima, while genetic algorithms have slow convergence speed and limited accuracy. Swarm intelligence algorithms such as the Grey Wolf Optimization Algorithm have been introduced into the field of MRD control due to their excellent global search capabilities; however, the standard Grey Wolf Optimization Algorithm uses a linear convergence factor, which is prone to premature convergence in the later stages of iteration, leading to insufficient parameter identification accuracy.

[0003] In magnetorheological damper modeling, the hyperbolic tangent model has attracted much attention due to its ability to accurately describe the saturation characteristics of damping force. However, its six key parameters, including stiffness coefficient and damping coefficient, exhibit strong coupling, making accurate acquisition difficult using traditional identification methods. Existing parameter identification technologies face the following challenges: First, standard optimization algorithms lack the ability to balance global exploration and local exploitation in parameter search; second, they are sensitive to initial parameters and easily affected by local extrema; third, there is a lack of dedicated optimization strategies for the hyperbolic tangent model. Although algorithms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE) have been attempted, the population diversity of these algorithms decreases significantly in later iterations, making it difficult to meet the requirements of high-precision modeling. While the Grey Wolf Optimization algorithm has advantages such as simple structure and few parameters, its fixed linear convergence mechanism limits its application in complex nonlinear systems. Therefore, a high-precision parameter identification method for the hyperbolic tangent model is urgently needed, which plays a crucial role in the precise control of magnetorheological dampers. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for parameter identification of magnetorheological dampers based on an improved gray wolf optimization algorithm. The method involves establishing a forward mechanical model of the magnetorheological damper and determining several parameters to be identified; using the improved gray wolf algorithm to identify the parameters of the forward mechanical model, obtaining the identification results; obtaining the simulation output of the forward mechanical model based on the identification results; determining whether the forward mechanical model meets the preset accuracy evaluation conditions based on the simulation output and the experimental output of the magnetorheological damper; and generating the final forward mechanical model based on the determination results of the preset accuracy evaluation conditions. By introducing a nonlinear convergence factor and a dynamic weighting strategy, the global search capability and local optimization accuracy of the algorithm are significantly improved, effectively overcoming the problem of parameter identification easily getting trapped in local optima, and providing reliable support for the accurate modeling and control of magnetorheological dampers.

[0005] This invention provides a method for identifying magnetorheological damper parameters based on an improved gray wolf optimization algorithm, comprising the following steps: Step S1: Establish a forward mechanical model of the magnetorheological damper and determine several parameters that need to be identified in the forward mechanical model; Step S2: Use the improved gray wolf algorithm to identify the parameters of the forward mechanical model and obtain the identification results of the parameters. Step S3: Based on the identification results, obtain the simulation output results of the positive mechanical model; based on the simulation output results and the experimental output results of the magnetorheological damper, determine whether the positive mechanical model meets the preset evaluation accuracy conditions; Step S4: Generate the final positive mechanical model based on the judgment result of the preset accurate evaluation conditions.

[0006] In one embodiment disclosed in this application, in step S1, a forward mechanical model of the magnetorheological damper is established, and several parameters that need to be identified in the forward mechanical model are determined, including: A forward mechanical model of the magnetorheological damper in the form of a hyperbolic tangent function is established; wherein the forward mechanical model is as follows: , The six parameters that need to be identified in the positive mechanical model are determined; among them, The scaling factor for the hysteresis loop. Let be the slope of the hysteresis loop. This is the hysteresis loop width coefficient. The viscous damping coefficient is... For bias damping force, This is the stiffness coefficient. It is a symbolic function.

[0007] In one embodiment disclosed in this application, in step S2, the improved gray wolf algorithm is used to identify the parameters of the forward mechanics model to obtain the identification results of the plurality of parameters, including: Set the population size and maximum number of iterations for the improved gray wolf algorithm, and initialize the search agent for the improved gray wolf algorithm by random sampling; Obtain the objective function value of each search agent to determine the position of the top three search agents; determine whether the improved gray wolf algorithm has reached the maximum number of iterations. If yes, output the optimal position; otherwise, calculate the objective function value of each search agent to update the position of the search agent, increment the iteration count by 1, and then determine whether the improved gray wolf algorithm has reached the maximum number of iterations. Repeat this process until the maximum number of iterations is reached. Based on the optimal output result, the identification result of each parameter is determined from the respective numerical range of the parameters.

[0008] In one embodiment disclosed in this application, in step S3, the simulation output result of the positive mechanical model is obtained based on the identification result; based on the simulation output result and the experimental output result of the magnetorheological damper, it is determined whether the positive mechanical model meets the preset evaluation accuracy conditions, including: Substitute the identification results into the forward mechanical model to obtain the simulated output damping force of the forward mechanical model under the same excitation amplitude and frequency conditions for different control currents; Obtain the experimental output damping force of the magnetorheological damper with respect to the different control currents during the experiment; By comparing the simulated output damping force and the experimental output damping force for each control current, the simulation deviation of the positive mechanical model is obtained, thereby determining whether the positive mechanical model meets the preset evaluation accuracy conditions.

[0009] In one embodiment disclosed in this application, in step S4, a final positive mechanical model is generated based on the judgment result of the preset evaluation accuracy conditions, including: If the forward mechanical model meets the preset evaluation accuracy conditions, then the forward mechanical model substituted with the identification results will be used as the final forward mechanical model. If the forward mechanics model does not meet the preset evaluation accuracy conditions, the improved Grey Wolf algorithm is used again to identify the parameters of the forward mechanics model until the preset evaluation accuracy conditions are met.

[0010] This invention also provides a magnetorheological damper parameter identification system based on an improved gray wolf optimization algorithm, comprising: The model building module is used to establish a forward mechanical model of the magnetorheological damper and determine several parameters that need to be identified in the forward mechanical model. The parameter identification module is used to identify the parameters of the forward mechanics model using the improved gray wolf algorithm, and obtain the identification results of the several parameters. The model simulation module is used to obtain the simulation output results of the positive mechanical model based on the identification results; The model accuracy evaluation module is used to determine whether the positive mechanical model meets the preset evaluation accuracy conditions based on the simulation output results and the experimental output results of the magnetorheological damper. The model generation module is used to generate the final positive mechanical model based on the judgment result of the preset evaluation accuracy conditions.

[0011] In one embodiment disclosed in this application, the model building module is used to establish a forward mechanical model of the magnetorheological damper and determine several parameters that need to be identified in the forward mechanical model, including: A forward mechanical model of the magnetorheological damper in the form of a hyperbolic tangent function is established; wherein the forward mechanical model is as follows: , The six parameters that need to be identified in the positive mechanical model are determined; among them, The scaling factor for the hysteresis loop. Let be the slope of the hysteresis loop. This is the hysteresis loop width coefficient. The viscous damping coefficient is... For bias damping force, This is the stiffness coefficient. It is a symbolic function.

[0012] In one embodiment disclosed in this application, the parameter identification module is used to identify parameters of the forward mechanics model using an improved gray wolf algorithm, and to obtain the identification results of the plurality of parameters, including: Set the population size and maximum number of iterations for the improved gray wolf algorithm, and initialize the search agent for the improved gray wolf algorithm by random sampling; Obtain the objective function value of each search agent to determine the position of the top three search agents; determine whether the improved gray wolf algorithm has reached the maximum number of iterations. If yes, output the optimal position; otherwise, calculate the objective function value of each search agent to update the position of the search agent, increment the iteration count by 1, and then determine whether the improved gray wolf algorithm has reached the maximum number of iterations. Repeat this process until the maximum number of iterations is reached. Based on the optimal output result, the identification result of each parameter is determined from the respective numerical range of the parameters.

[0013] In one embodiment disclosed in this application, the model simulation module is used to obtain the simulation output result of the positive mechanical model based on the identification result, including: Substitute the identification results into the forward mechanical model to obtain the simulated output damping force of the forward mechanical model under the same excitation amplitude and frequency conditions for different control currents; The model accuracy evaluation module is used to determine whether the forward mechanical model meets the preset evaluation accuracy conditions based on the simulation output results and the experimental output results of the magnetorheological damper, including: Obtain the experimental output damping force of the magnetorheological damper with respect to the different control currents during the experiment; By comparing the simulated output damping force and the experimental output damping force for each control current, the simulation deviation of the positive mechanical model is obtained, thereby determining whether the positive mechanical model meets the preset evaluation accuracy conditions.

[0014] In one embodiment disclosed in this application, the model generation module is used to generate a final forward mechanical model based on the judgment result of the preset evaluation accuracy conditions, including: If the forward mechanical model meets the preset evaluation accuracy conditions, then the forward mechanical model substituted with the identification results will be used as the final forward mechanical model. If the forward mechanics model does not meet the preset evaluation accuracy conditions, the improved Grey Wolf algorithm is used again to identify the parameters of the forward mechanics model until the preset evaluation accuracy conditions are met.

[0015] Compared to existing technologies, this method and system for parameter identification of magnetorheological dampers based on an improved gray wolf optimization algorithm establishes a forward mechanical model of the magnetorheological damper and determines several parameters that need to be identified. The improved gray wolf algorithm is used to identify the parameters of the forward mechanical model, yielding identification results. Based on the identification results, simulation outputs of the forward mechanical model are obtained. Based on the simulation outputs and experimental outputs of the magnetorheological damper, it is determined whether the forward mechanical model meets the preset accuracy evaluation conditions. Based on the determination results of the preset accuracy evaluation conditions, the final forward mechanical model is generated. By introducing a nonlinear convergence factor and a dynamic weighting strategy, the algorithm's global search capability and local optimization accuracy are significantly improved, effectively overcoming the problem of parameter identification easily getting trapped in local optima, and providing reliable support for the accurate modeling and control of magnetorheological dampers.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the magnetorheological damper parameter identification method based on the improved gray wolf optimization algorithm provided by this invention.

[0020] Figure 2 This is the flowchart of the improved Grey Wolf algorithm.

[0021] Figure 3 It is a comparison of the objective function values ​​of linear convergence factors and nonlinear convergence factors under different currents.

[0022] Figure 4 This is a schematic diagram of the framework of the magnetorheological damper parameter identification system based on the improved gray wolf optimization algorithm provided by the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] See Figure 1 This is a flowchart illustrating the magnetorheological damper parameter identification method based on the improved gray wolf optimization algorithm provided in an embodiment of the present invention. The magnetorheological damper parameter identification method based on the improved gray wolf optimization algorithm includes: Step S1: Establish the forward mechanical model of the magnetorheological damper and determine several parameters that need to be identified in the forward mechanical model; Step S2: Use the improved gray wolf algorithm to identify the parameters of the forward mechanics model and obtain the identification results of several parameters. Step S3: Based on the identification results, obtain the simulation output results of the forward mechanical model; based on the simulation output results and the experimental output results of the magnetorheological damper, determine whether the forward mechanical model meets the preset evaluation accuracy conditions. Step S4: Based on the judgment results of the preset accurate evaluation conditions, generate the final positive mechanical model.

[0025] The beneficial effects of the above technical solution are as follows: The magnetorheological damper parameter identification method based on the improved gray wolf optimization algorithm establishes a forward mechanical model of the magnetorheological damper and determines several parameters that need to be identified; the improved gray wolf algorithm is used to identify the parameters of the forward mechanical model, and the identification results of several parameters are obtained; based on the identification results, the simulation output results of the forward mechanical model are obtained; based on the simulation output results and the experimental output results of the magnetorheological damper, it is determined whether the forward mechanical model meets the preset evaluation accuracy conditions; based on the judgment results of the preset evaluation accuracy conditions, the final forward mechanical model is generated, and a nonlinear convergence factor and dynamic weight strategy are introduced, which significantly improves the global search capability and local development accuracy of the algorithm, effectively overcomes the problem that parameter identification is prone to getting trapped in local optima, and provides reliable support for the accurate modeling and control of magnetorheological dampers.

[0026] Preferably, in step S1, a forward mechanical model of the magnetorheological damper is established, and several parameters that need to be identified in the forward mechanical model are determined, including: A forward mechanical model of the magnetorheological damper in the form of a hyperbolic tangent function is established; the forward mechanical model is as follows: , Determining the six parameters that need to be identified for the forward mechanics model; among them, The scaling factor for the hysteresis loop. Let be the slope of the hysteresis loop. This is the hysteresis loop width coefficient. The viscous damping coefficient is... For bias damping force, This is the stiffness coefficient. It is a symbolic function.

[0027] Considering that the hyperbolic tangent function form of the forward mechanical model has fewer parameters to be identified, the above formula's forward mechanical model only contains six parameters to be identified. Using the improved Grey Wolf algorithm for parameter identification can reduce the workload and improve the accuracy of parameter identification. Considering that the convergence factor of the improved Grey Wolf algorithm affects the parameter identification results, in order to establish a forward mechanical model with higher fitting accuracy, it is necessary to select and improve the convergence factor of the improved Grey Wolf algorithm. The convergence factor of the improved Grey Wolf algorithm represents the algorithm's search capability. This invention uses a nonlinear convergence factor instead of the traditional linear convergence factor. The nonlinear convergence factor a(t) is shown in the following equation: , In the above formula, a initial and a final Let a be the initial and final values ​​of the convergence factor, respectively. initial and a final The value of can be, but is not limited to, 2 and 0, where t is the current iteration number, Max_iter is the maximum iteration number, and e is a natural constant.

[0028] Compared with the traditional linear convergence factor, the nonlinear convergence factor based on the logarithmic function can gradually reduce the influence of the convergence factor during the iteration of the improved Grey Wolf algorithm, thereby balancing the convergence speed and stability of the convergence factor. Furthermore, as the current applied to the magnetorheological damper increases, the optimization effect becomes more obvious, ensuring that the forward mechanical model can accurately characterize the nonlinear hysteresis characteristics of the magnetorheological damper.

[0029] The setting of parameters such as wolf pack size (i.e., number of wolves) N and iteration number T in the improved gray wolf algorithm will affect the recognition accuracy of the improved gray wolf algorithm. In order to study the influence of the above different parameters on the recognition accuracy of the algorithm, it is necessary to set the initial range of the above parameters. The constraint range of wolf pack size (i.e., number of wolves) N and iteration number T can be N∈[50, 250], T∈[300, 500].

[0030] Accordingly, the objective function to be optimized in the improved Grey Wolf algorithm is as follows: , In the above formula, F ti To utilize the predicted damping force obtained from the improved gray wolf algorithm, F i The magnitude of the damping force is obtained by conducting characteristic tests on the magnetorheological damper, where n is the number of damping forces collected.

[0031] For the parameters α, β, δ, c, k, and f0 that need to be identified in the forward mechanical model of hyperbolic tangent function form, the initial parameter values ​​corresponding to the above parameters are α∈[-80, 0], β∈[0, 10], δ∈[-30, -20], c∈[-10, 0], k∈[-5, -5], and f0∈[0, 10].

[0032] Preferably, in step S2, the improved gray wolf algorithm is used to identify the parameters of the forward mechanics model, and the identification results of several parameters are obtained, including: Set the population size and maximum number of iterations for the improved gray wolf algorithm, and initialize the search agent for the improved gray wolf algorithm by random sampling; Obtain the objective function value of each search agent to determine the position of the top three search agents; determine whether the improved gray wolf algorithm has reached the maximum number of iterations. If yes, output the optimal position; otherwise, calculate the objective function value of each search agent to update the position of the search agent, increment the iteration count by 1, and then determine whether the improved gray wolf algorithm has reached the maximum number of iterations. Continue this process until the maximum number of iterations is reached. Based on the optimal output result, the identification result of each parameter is determined from the respective numerical ranges of several parameters.

[0033] Please see Figure 2The improved Grey Wolf algorithm was used to identify the six parameters of the forward mechanical model, and the identification results of each parameter were obtained, providing a reliable parameter basis for the subsequent construction of a high-precision forward mechanical model. The parameter identification results and objective function values ​​of the six parameters under different currents applied to the magnetorheological damper, corresponding to the algorithm flow of the improved Grey Wolf algorithm, are shown in Table 1 below.

[0034] Table 1

[0035] Based on Table 1 above, accurate and reliable data are provided for the subsequent construction of a high-precision forward mechanical model.

[0036] Please also see Figure 3 Under the same current conditions, the objective function value of the linear convergence factor is greater than that of the nonlinear convergence factor. Therefore, the convergence factor of the improved Grey Wolf algorithm is set as a nonlinear convergence factor. Compared with the traditional linear convergence factor, the nonlinear convergence factor can gradually reduce the influence of the convergence factor during the iteration process of the improved Grey Wolf algorithm, thereby balancing the convergence speed and stability of the convergence factor. It can be highly matched with the nonlinear hysteresis characteristics of the magnetorheological damper and improve the accuracy of the forward mechanical model.

[0037] Preferably, in step S3, based on the identification results, the simulation output results of the forward mechanical model are obtained; based on the simulation output results and the experimental output results of the magnetorheological damper, it is determined whether the forward mechanical model meets the preset evaluation accuracy conditions, including: Substitute the identification results into the forward mechanical model to obtain the simulated output damping force (i.e., the fitted damping force) of the forward mechanical model under the same excitation amplitude and frequency conditions for different control currents. Obtain the test output damping force (i.e. test damping force) of the magnetorheological damper with respect to different control currents during the experiment. By comparing the simulated output damping force and the experimental output damping force for each control current, the simulation deviation of the forward mechanical model is obtained, thereby determining whether the forward mechanical model meets the preset evaluation accuracy conditions.

[0038] After substituting the identification results of the above six parameters into the forward mechanical model, the accuracy of the forward mechanical model is evaluated by comparing and analyzing the simulated output damping force of the forward mechanical model under the same excitation amplitude and frequency conditions with respect to different control currents, and the experimental output damping force of the magnetorheological damper with respect to different control currents during the experiment. Specifically, the simulation output damping force and experimental output damping force for each control current are compared to obtain the simulation deviation of the forward mechanical model. If the simulation deviation is less than the preset deviation threshold, the forward mechanical model is judged to meet the preset evaluation accuracy conditions; otherwise, the forward mechanical model is judged not to meet the preset evaluation accuracy conditions. The damping force calculated by the forward mechanical model constructed using the six parameters identified by the improved Grey Wolf algorithm is highly consistent with the actual damping force of the magnetorheological damper in terms of displacement and velocity. This enables the constructed forward mechanical model to accurately characterize the damping force-displacement-velocity characteristics of the magnetorheological damper, ensuring precise control of the magnetorheological damper.

[0039] Preferably, in step S4, based on the judgment result of the preset accurate evaluation conditions, a final positive mechanical model is generated, including: If the forward mechanical model meets the preset evaluation accuracy conditions, the forward mechanical model with the identification results substituted into it will be used as the final forward mechanical model. If the forward mechanics model does not meet the preset accuracy evaluation conditions, the improved Grey Wolf algorithm is used again to identify the parameters of the forward mechanics model until the preset accuracy evaluation conditions are met.

[0040] In the above manner, when the forward mechanical model meets the preset evaluation accuracy conditions, the forward mechanical model substituted with the identification results is used as the final forward mechanical model, providing a model basis for the precise control of the magnetorheological damper.

[0041] See Figure 4 This is a schematic diagram of the framework of a magnetorheological damper parameter identification system based on an improved gray wolf optimization algorithm provided in an embodiment of the present invention. The magnetorheological damper parameter identification system based on the improved gray wolf optimization algorithm includes: The model building module is used to establish the forward mechanical model of the magnetorheological damper and determine several parameters that need to be identified in the forward mechanical model. The parameter identification module is used to identify the parameters of the forward mechanics model using the improved Grey Wolf algorithm, and obtain the identification results of several parameters. The model simulation module is used to obtain the simulation output results of the positive mechanical model based on the identification results; The model accuracy evaluation module is used to determine whether the forward mechanical model meets the preset evaluation accuracy conditions based on the simulation output results and the experimental output results of the magnetorheological damper. The model generation module is used to generate the final positive mechanical model based on the judgment results of the preset evaluation accuracy conditions.

[0042] The beneficial effects of the above technical solution are as follows: The magnetorheological damper parameter identification system based on the improved Grey Wolf optimization algorithm establishes a forward mechanical model of the magnetorheological damper and determines several parameters that need to be identified; the improved Grey Wolf algorithm is used to identify the parameters of the forward mechanical model, and the identification results of several parameters are obtained; based on the identification results, the simulation output results of the forward mechanical model are obtained; based on the simulation output results and the experimental output results of the magnetorheological damper, it is determined whether the forward mechanical model meets the preset evaluation accuracy conditions; based on the judgment results of the preset evaluation accuracy conditions, the final forward mechanical model is generated, and a nonlinear convergence factor and dynamic weight strategy are introduced to significantly improve the global search capability and local development accuracy of the algorithm, effectively overcome the problem that parameter identification is prone to getting trapped in local optima, and provide reliable support for the accurate modeling and control of magnetorheological dampers.

[0043] Preferably, the model building module is used to establish a forward mechanical model of the magnetorheological damper and determine several parameters that need to be identified in the forward mechanical model, including: A forward mechanical model of the magnetorheological damper in the form of a hyperbolic tangent function is established; the forward mechanical model is as follows: , Determining the six parameters that need to be identified for the forward mechanics model; among them, The scaling factor for the hysteresis loop. Let be the slope of the hysteresis loop. This is the hysteresis loop width coefficient. The viscous damping coefficient is... For bias damping force, This is the stiffness coefficient. It is a symbolic function.

[0044] Preferably, the parameter identification module is used to identify parameters of the forward mechanics model using the improved gray wolf algorithm, obtaining identification results for several parameters, including: Set the population size and maximum number of iterations for the improved gray wolf algorithm, and initialize the search agent for the improved gray wolf algorithm by random sampling; Obtain the objective function value of each search agent to determine the position of the top three search agents; determine whether the improved gray wolf algorithm has reached the maximum number of iterations. If yes, output the optimal position; otherwise, calculate the objective function value of each search agent to update the position of the search agent, increment the iteration count by 1, and then determine whether the improved gray wolf algorithm has reached the maximum number of iterations. Continue this process until the maximum number of iterations is reached. Based on the optimal output result, the identification result of each parameter is determined from the respective numerical ranges of several parameters.

[0045] Preferably, the model simulation module is used to obtain the simulation output results of the positive mechanical model based on the identification results, including: Substitute the identification results into the forward mechanical model to obtain the simulated output damping force of the forward mechanical model under the same excitation amplitude and frequency conditions for different control currents; The model accuracy evaluation module is used to determine whether the forward mechanical model meets the preset accuracy evaluation conditions based on the simulation output and the experimental output of the magnetorheological damper, including: Obtain the experimental output damping force of the magnetorheological damper with respect to different control currents during the experiment; By comparing the simulated output damping force and the experimental output damping force for each control current, the simulation deviation of the forward mechanical model is obtained, thereby determining whether the forward mechanical model meets the preset evaluation accuracy conditions.

[0046] Preferably, the model generation module is used to generate a final forward mechanical model based on the judgment result of preset evaluation accuracy conditions, including: If the forward mechanical model meets the preset evaluation accuracy conditions, the forward mechanical model with the identification results substituted into it will be used as the final forward mechanical model. If the forward mechanics model does not meet the preset accuracy evaluation conditions, the improved Grey Wolf algorithm is used again to identify the parameters of the forward mechanics model until the preset accuracy evaluation conditions are met.

[0047] The magnetorheological damper parameter identification system based on the improved gray wolf optimization algorithm of the present invention has the same operation and effect as the magnetorheological damper parameter identification method based on the improved gray wolf optimization algorithm described above. Therefore, the magnetorheological damper parameter identification system based on the improved gray wolf optimization algorithm will not be described again here.

[0048] As can be seen from the above embodiments, the magnetorheological damper parameter identification method and system based on the improved Grey Wolf optimization algorithm establishes a forward mechanical model of the magnetorheological damper and determines several parameters that need to be identified; the improved Grey Wolf algorithm is used to identify the parameters of the forward mechanical model, and the identification results of several parameters are obtained; based on the identification results, the simulation output results of the forward mechanical model are obtained; based on the simulation output results and the experimental output results of the magnetorheological damper, it is determined whether the forward mechanical model meets the preset evaluation accuracy conditions; based on the judgment results of the preset evaluation accuracy conditions, the final forward mechanical model is generated, and a nonlinear convergence factor and dynamic weight strategy are introduced to significantly improve the global search capability and local development accuracy of the algorithm, effectively overcome the problem that parameter identification is prone to getting trapped in local optima, and provide reliable support for the accurate modeling and control of magnetorheological dampers.

[0049] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for identifying magnetorheological damper parameters based on an improved gray wolf optimization algorithm, characterized in that, It includes the following steps: Step S1: Establish a forward mechanical model of the magnetorheological damper and determine several parameters that need to be identified in the forward mechanical model; Step S2: Use the improved gray wolf algorithm to identify the parameters of the forward mechanical model and obtain the identification results of the parameters. Step S3: Based on the identification results, obtain the simulation output results of the positive mechanical model; Based on the simulation output results and the experimental output results of the magnetorheological damper, determine whether the forward mechanical model meets the preset evaluation accuracy conditions; Step S4: Generate the final positive mechanical model based on the judgment result of the preset accurate evaluation conditions.

2. The method for identifying magnetorheological damper parameters based on the improved gray wolf optimization algorithm as described in claim 1, characterized in that: In step S1, a forward mechanical model of the magnetorheological damper is established, and several parameters that need to be identified in the forward mechanical model are determined, including: A forward mechanical model of the magnetorheological damper in the form of a hyperbolic tangent function is established; wherein the forward mechanical model is as follows: , The six parameters that need to be identified in the positive mechanical model are determined; among them, The scaling factor for the hysteresis loop. Let be the slope of the hysteresis loop. This is the hysteresis loop width coefficient. The viscous damping coefficient is... For bias damping force, This is the stiffness coefficient. It is a symbolic function.

3. The method for identifying magnetorheological damper parameters based on the improved gray wolf optimization algorithm as described in claim 1, characterized in that: In step S2, the improved gray wolf algorithm is used to identify the parameters of the forward mechanics model, and the identification results of the several parameters are obtained, including: Set the population size and maximum number of iterations for the improved gray wolf algorithm, and initialize the search agent for the improved gray wolf algorithm by random sampling; Obtain the objective function value of each search agent to determine the position of the top three search agents; determine whether the improved gray wolf algorithm has reached the maximum number of iterations. If yes, output the optimal position; otherwise, calculate the objective function value of each search agent to update the position of the search agent, increment the iteration count by 1, and then determine whether the improved gray wolf algorithm has reached the maximum number of iterations. Repeat this process until the maximum number of iterations is reached. Based on the optimal output result, the identification result of each parameter is determined from the respective numerical range of the parameters.

4. The method for identifying magnetorheological damper parameters based on the improved gray wolf optimization algorithm as described in claim 1, characterized in that: In step S3, the simulation output of the positive mechanical model is obtained based on the identification result. Based on the simulation output and the experimental output of the magnetorheological damper, determine whether the forward mechanical model meets the preset evaluation accuracy conditions, including: Substitute the identification results into the forward mechanical model to obtain the simulated output damping force of the forward mechanical model under the same excitation amplitude and frequency conditions for different control currents; Obtain the experimental output damping force of the magnetorheological damper with respect to the different control currents during the experiment; By comparing the simulated output damping force and the experimental output damping force for each control current, the simulation deviation of the positive mechanical model is obtained, thereby determining whether the positive mechanical model meets the preset evaluation accuracy conditions.

5. The method for identifying magnetorheological damper parameters based on the improved gray wolf optimization algorithm as described in claim 1, characterized in that: In step S4, based on the judgment result of the preset evaluation accuracy conditions, a final positive mechanical model is generated, including: If the forward mechanical model meets the preset evaluation accuracy conditions, then the forward mechanical model substituted with the identification results will be used as the final forward mechanical model. If the forward mechanics model does not meet the preset evaluation accuracy conditions, the improved Grey Wolf algorithm is used again to identify the parameters of the forward mechanics model until the preset evaluation accuracy conditions are met.

6. A magnetorheological damper parameter identification system based on an improved gray wolf optimization algorithm, characterized in that, include: The model building module is used to establish a forward mechanical model of the magnetorheological damper and determine several parameters that need to be identified in the forward mechanical model. The parameter identification module is used to identify the parameters of the forward mechanics model using the improved gray wolf algorithm, and obtain the identification results of the several parameters. The model simulation module is used to obtain the simulation output results of the positive mechanical model based on the identification results; The model accuracy evaluation module is used to determine whether the positive mechanical model meets the preset evaluation accuracy conditions based on the simulation output results and the experimental output results of the magnetorheological damper. The model generation module is used to generate the final positive mechanical model based on the judgment result of the preset evaluation accuracy conditions.

7. The magnetorheological damper parameter identification system based on the improved gray wolf optimization algorithm as described in claim 6, characterized in that: The model building module is used to establish a forward mechanical model of the magnetorheological damper and determine several parameters that need to be identified in the forward mechanical model, including: A forward mechanical model of the magnetorheological damper in the form of a hyperbolic tangent function is established; wherein the forward mechanical model is as follows: , The six parameters that need to be identified in the positive mechanical model are determined; among them, The scaling factor for the hysteresis loop. Let be the slope of the hysteresis loop. This is the hysteresis loop width coefficient. The viscous damping coefficient is... For bias damping force, This is the stiffness coefficient. It is a symbolic function.

8. The magnetorheological damper parameter identification system based on the improved gray wolf optimization algorithm as described in claim 6, characterized in that: The parameter identification module is used to identify the parameters of the forward mechanics model using the improved gray wolf algorithm, and obtain the identification results of the several parameters, including: Set the population size and maximum number of iterations for the improved gray wolf algorithm, and initialize the search agent for the improved gray wolf algorithm by random sampling; Obtain the objective function value of each search agent to determine the position of the top three search agents; determine whether the improved gray wolf algorithm has reached the maximum number of iterations. If yes, output the optimal position; otherwise, calculate the objective function value of each search agent to update the position of the search agent, increment the iteration count by 1, and then determine whether the improved gray wolf algorithm has reached the maximum number of iterations. Repeat this process until the maximum number of iterations is reached. Based on the optimal output result, the identification result of each parameter is determined from the respective numerical range of the parameters.

9. The magnetorheological damper parameter identification system based on the improved gray wolf optimization algorithm as described in claim 6, characterized in that: The model simulation module is used to obtain the simulation output results of the positive mechanical model based on the identification results, including: Substitute the identification results into the forward mechanical model to obtain the simulated output damping force of the forward mechanical model under the same excitation amplitude and frequency conditions for different control currents; The model accuracy evaluation module is used to determine whether the forward mechanical model meets the preset evaluation accuracy conditions based on the simulation output results and the experimental output results of the magnetorheological damper, including: Obtain the experimental output damping force of the magnetorheological damper with respect to the different control currents during the experiment; By comparing the simulated output damping force and the experimental output damping force for each control current, the simulation deviation of the positive mechanical model is obtained, thereby determining whether the positive mechanical model meets the preset evaluation accuracy conditions.

10. The magnetorheological damper parameter identification system based on the improved gray wolf optimization algorithm as described in claim 6, characterized in that: The model generation module is used to generate a final forward mechanical model based on the judgment result of the preset evaluation accuracy conditions, including: If the forward mechanical model meets the preset evaluation accuracy conditions, then the forward mechanical model substituted with the identification results will be used as the final forward mechanical model. If the forward mechanics model does not meet the preset evaluation accuracy conditions, the improved Grey Wolf algorithm is used again to identify the parameters of the forward mechanics model until the preset evaluation accuracy conditions are met.

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