Identification method for hyperbolic tangent model parameters of magneto-rheological torsional vibration damper

Abstract

The invention discloses a method for identifying parameters of a hyperbolic tangent model of a magnetorheological torsional vibration damper, which comprises the following steps of: firstly, testing the mechanical property of the magnetorheological torsional vibration damper by adopting a servo fatigue motor to obtain a hysteretic curve of the magnetorheological torsional vibration damper, and obtaining a plurality of groups of data by changing frequency and current; the method comprises the following steps of: identifying parameters of a hyperbolic tangent model by utilizing experimental data, firstly solving a stiffness coefficient k according to a physical rule, then solving a viscous damping coefficient c based on mathematical characteristics of the hyperbolic tangent model, finally limiting identification ranges of other parameters according to the mathematical characteristics, performing parameter identification by applying an optimization algorithm, and finally identifying the parameters of the hyperbolic tangent model. And parameter identification is realized by fitting parameter values under different currents. The method has the advantages that sudden change and unsmoothness of the zero point area are eliminated, the recognition precision and the solution quality are improved, the success rate is greatly increased, the calculation time is greatly shortened, and double advantages in efficiency and performance are shown.

Description

Technical Field

[0001] The present invention relates to the field of intelligence and intelligent material technology, and more specifically, to a method for identifying hyperbolic tangent model parameters of a magnetorheological torsional vibration damper. Background Art

[0002] At present, the research on magnetorheological torsional vibration dampers has attracted the attention of many experts and scholars. It adjusts the magnetic field strength of the magnetorheological fluid environment by controlling the current in the coil, thereby changing the viscosity coefficient of the magnetorheological fluid. The change in the viscosity of the magnetorheological fluid directly affects the shear force during the rotation of the device, thereby achieving precise control of the output damping force of the torsional vibration damper. It has the advantages of continuous adjustability, low power consumption and fast response, and is suitable for a variety of vibration reduction demand scenarios. However, due to its special mechanical properties-hysteresis characteristics, its mathematical model has strong nonlinearity, making the establishment process quite complicated and difficult.

[0003] Domestic and foreign scholars have conducted extensive research on it and established many models to describe the nonlinear characteristics of magnetorheological devices. At present, the commonly used mathematical models of magnetorheological dampers include the Bingham model, the Bouc-Wen model, the polynomial model, and the hyperbolic tangent model. Among them, the Bingham model was first proposed to describe the nonlinear characteristics of magnetorheological devices, but it cannot describe the characteristics of the low-speed zone very well; the Bouc-Wen model can accurately reflect the low-speed and high-speed hysteresis characteristics of the magnetorheological damper, but the Bouc-Wen model involves many undetermined parameters except for the easily determined viscous damping coefficient, and the differential term calculation is too complicated and easily affected. The polynomial model has limitations such as the influence of the excitation frequency; the polynomial model has a simple expression and relatively high accuracy, but its parameters have no actual physical meaning, and there are obvious oscillations in the high-speed zone; compared with the Bingham model, the hyperbolic tangent model has higher accuracy and can well describe the hysteresis characteristics of the magnetorheological torsional vibration damper. Compared with the Bouc–Wen model that can also reflect the hysteresis characteristics, the hyperbolic tangent model has the advantages of identification parameters without differential terms, small influence by the excitation frequency, and high accuracy in the speed transition zone. In addition, compared with the polynomial model, the parameters of the hyperbolic tangent model have certain physical meanings and are smoother in the high-speed area. Therefore, it is widely used in the forward and inverse model establishment and control of magnetorheological devices.

[0004] Since there are many parameters to be identified in the hyperbolic tangent model, intelligent algorithms are generally used to identify them. Hu Haigang et al. used the least squares method to identify the hyperbolic tangent model, and the fitting accuracy was relatively good, but it was more complicated when programming; NMKwok et al. used the particle swarm optimization algorithm (PSO) to identify in the identification process; Zhu et al. used genetic algorithms (GA) to identify the parameters of the system. However, the above-mentioned intelligent algorithm identification is easy to fall into the local optimal solution, which is time-consuming and rarely uses the mathematical characteristics of the model function. In addition, the numerical value of the parameter also significantly affects the fullness of the hysteresis loop and the continuity in the zero point area. Since only intelligent algorithms are generally used, such as genetic algorithms, no method for limiting the range of parameter values ​​is proposed for this problem, resulting in the torque-displacement curve of the hyperbolic tangent model fitting often appearing discontinuous at the displacement of 0, affecting the control effect, and only using intelligent algorithms to identify the parameters of the hyperbolic tangent model will make the identified parameters have no actual physical meaning. Summary of the invention

[0005] The purpose of the present invention is to design and develop a method for identifying the parameters of the hyperbolic tangent model of a magnetorheological torsional vibration damper, by constructing a hyperbolic tangent model to identify the stiffness coefficient and the viscous damping coefficient, and combining a genetic algorithm to identify other parameters to improve accuracy and identification efficiency.

[0006] The technical solution provided by the present invention is:

[0007] A method for identifying hyperbolic tangent model parameters of a magnetorheological torsional vibration damper comprises the following steps:

[0008] Step 1: Collect the relative speed, relative angle displacement input and torsional moment corresponding to the loading;

[0009] Step 2: Construct the hyperbolic tangent model of magnetorheological torsional vibration damper:

[0010]

[0011]

[0012] Where T is the torsional moment, c is the viscous damping coefficient of the magnetorheological torsional vibration damper, k is the stiffness coefficient of the magnetorheological torsional vibration damper, θ(t) is the relative angular displacement input, t is the time, α is the expansion coefficient of the hysteresis function, z(θ(t)) is the hysteresis operator, is the relative speed, β is the coefficient determined by the magnetorheological fluid characteristics and the structure of the magnetorheological torsional vibration damper, δ is the coefficient determined by the magnetorheological fluid and the control system, and sign(·) is the sign function;

[0013] Step 3: According to the hyperbolic tangent model of the magnetorheological torsional vibration damper, the viscous damping coefficient of the magnetorheological torsional vibration damper, the stiffness coefficient of the magnetorheological torsional vibration damper, the expansion coefficient of the hysteresis function, the coefficient determined by the magnetorheological fluid characteristics and the magnetorheological torsional vibration damper structure, and the constraint conditions of the coefficient determined by the magnetorheological fluid and the control system are obtained:

[0014]

[0015]

[0016] α>0;

[0017]

[0018] Where, T ′ is the torsional moment corresponding to the relative angular displacement input after the change, q is a constant, for The relative speed of the i-th data point in the curve graph, N is the number of test data points, y i for The response value of the i-th data point in the curve graph, for The average relative velocity of the parallel region in the curve, y avg for The average of the response values ​​of the parallel regions in the curve graph;

[0019] Step 4: The viscous damping coefficient of the magnetorheological torsional vibration damper, the stiffness coefficient of the magnetorheological torsional vibration damper, the expansion coefficient of the hysteresis function, the coefficient determined by the magnetorheological fluid characteristics and the magnetorheological torsional vibration damper structure, and the coefficient determined by the magnetorheological fluid and the control system satisfy:

[0020] c=0.1166+1.0806I-0.1424I 2 +0.006I 3 ;

[0021] k = 0.0055;

[0022] α=0.5538+0.4176I+0.608I 2 -0.0745I 3 ;

[0023] δ=1.2894+1.4238I-0.521I 2 +0.063I 3 ;

[0024] β=6.5832+4.1262I-0.9494I 2 .

[0025] Preferably, the relative angular displacement input satisfies:

[0026] θ(t)=Asin(2πt);

[0027] Where A is the amplitude of the excitation, and A>0.

[0028] Preferably, the relative angular displacement input after the change corresponds to a torsional moment satisfying:

[0029]

[0030] In the formula, θ ′ (t) is the relative angle displacement input after the change.

[0031] Preferably, the relative angular displacement input after the change satisfies:

[0032] θ ′ (t) = θ(t) + q;

[0033] Where q is a constant.

[0034] Preferably, the expansion coefficient of the hysteresis function, the coefficient determined by the magnetorheological fluid characteristics and the magnetorheological torsional vibration damper structure, and the coefficient determined by the magnetorheological fluid and the control system are all identified by a genetic algorithm.

[0035] Preferably, the genetic algorithm comprises:

[0036] Step a, creating an initial population with the relative velocity, relative angular displacement input and torsional moment corresponding to the loading;

[0037] Step b, define the fitness evaluation criteria, and set the initial population size, the probability of configuration crossover, the probability of gene mutation, and the maximum number of iterations;

[0038] Step c, randomly initializing the expansion coefficient of the hysteresis function, the coefficient determined by the magnetorheological fluid characteristics and the magnetorheological torsional vibration damper structure, and the coefficient determined by the magnetorheological fluid and the control system;

[0039] Step d, initializing a set of chromosomes for each parameter;

[0040] Step e, using the fitness function value to evaluate each chromosome;

[0041] Step f: Based on the fitness evaluation results of each chromosome, a roulette wheel selection strategy is adopted to form a new population;

[0042] Among them, the probability of each chromosome being selected is proportional to its fitness;

[0043] Step g, updating chromosomes through crossover and mutation;

[0044] Step h: When the value of the fitness function converges or reaches the number of iterations, the optimal model parameters are output.

[0045] Preferably, the fitness function is:

[0046]

[0047] Where, T i exp is the true torsional moment of the ith data point, T i sim is the calculated torsional moment for the ith data point.

[0048] Preferably, the initial population size is 400;

[0049] The probability of the configuration crossover occurring is 0.8;

[0050] The probability of the gene mutation is 0.2;

[0051] The maximum number of iteration rounds is 500.

[0052] The beneficial effects of the present invention are:

[0053] (1) The present invention designs and develops a method for identifying the parameters of the hyperbolic tangent model of a magnetorheological torsional vibration damper. The method uses the physical meaning and mathematical characteristics of the hyperbolic tangent model to first identify the stiffness coefficient and the viscous damping coefficient, then narrows the identification range, and uses an intelligent algorithm to identify the remaining parameters. The method can well describe the mechanical properties of the magnetorheological torsional vibration damper. The principle is intuitive and the calculation is efficient. It can accurately capture the core mechanical performance characteristics of the magnetorheological torsional vibration damper, provide a solid theoretical basis for the semi-active control strategy of the magnetorheological torsional vibration damper, and ensure the effective implementation and optimization of the control strategy.

[0054] (2) The method for identifying the hyperbolic tangent model parameters of a magnetorheological torsional vibration damper designed and developed by the present invention uses the mathematical properties of the hyperbolic tangent model and the physical properties of the magnetorheological torsional vibration damper to give certain physical meanings to the hyperbolic tangent model parameters, solve the problem of possible mutations in the zero-point region of the torque-angle curve, and reduce the situation where the previous algorithm identification is prone to fall into the local optimal solution and takes a long time.

[0055] (3) The method for identifying the parameters of the hyperbolic tangent model of a magnetorheological torsional vibration damper designed and developed by the present invention is significantly superior to previous technologies in terms of the quality and success rate of the average solution, thereby improving the accuracy. By reducing the identification parameters and narrowing the search range, the iterative convergence speed is effectively improved, and the running time of the algorithm is significantly shortened, thereby improving the recognition efficiency. Moreover, the identification method has been constructed into a complete and systematic methodological system from its construction to the verification stage. This system is not only highly practical, but can also serve as an important tool to guide theoretical learning and promote the in-depth understanding and application of relevant theoretical knowledge. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The figure is a schematic diagram of the relationship curve between torsional torque and rotation angle of the magnetorheological torsional vibration damper under different loading conditions of the present invention.

[0057] Figure 2 The figure is a schematic diagram of the relationship curve between torsional torque and rotation speed of the magnetorheological torsional vibration damper under different loading conditions of the present invention.

[0058] Figure 3 It is a schematic diagram of the relationship curve between the hysteresis operator and the rotation speed under different loading currents described in the present invention.

[0059] Figure 4 It is a schematic diagram of the relationship curve between the intermediate parameter and the rotation speed under different loading currents described in the present invention.

[0060] Figure 5 (a) is a schematic diagram of the relationship curve between the hysteresis operator and the rotation angle under different β values ​​and δ values ​​described in the present invention.

[0061] Figure 5 (b) is a schematic diagram of the relationship curve between the hysteresis operator and the rotation speed under different β values ​​and δ values ​​described in the present invention.

[0062] Figure 6 This is a schematic diagram of the relationship curve between c value and current of the present invention.

[0063] Figure 7 This is a schematic diagram of the relationship curve between α value and current according to the present invention.

[0064] Figure 8 This is a schematic diagram of the relationship curve between β value and current according to the present invention.

[0065] Fig. 9 This is a schematic diagram of the relationship curve between the delta value and the current of the present invention.

[0066] Fig.10 It is a schematic diagram of the comparison curve of the torque-angle curve of the simulation value and the test value of the magnetorheological torsional vibration damper model with different current loading at a frequency of 1 Hz and an amplitude of 10° described in the present invention.

[0067] Fig.11 It is a schematic diagram of the comparison curve of the torque-angle curve of the simulation value and the test value of the magnetorheological torsional vibration damper model with different current loading at a frequency of 0.5 Hz and an amplitude of 10° described in the present invention.

[0068] Fig.12 It is a schematic diagram comparing the success rates of the three parameter identification methods described in the present invention and the average quality of the solutions.

[0069] Fig.13 It is a schematic diagram comparing the average fitness curves and running time of the three parameter identification methods described in the present invention. DETAILED DESCRIPTION

[0070] The present invention is further described in detail below so that those skilled in the art can implement it according to the description.

[0071] The present invention provides a method for identifying hyperbolic tangent model parameters of a magnetorheological torsional vibration damper, comprising:

[0072] Step 1: collect the relative speed (rotation speed), relative angular displacement input (rotation angle) and torsional moment corresponding to the load through different sensors;

[0073] In this embodiment, a servo fatigue machine is used to test the mechanical properties of the magnetorheological torsional vibration damper. Under sinusoidal cyclic loading, the loading frequency includes 0.5 Hz and 1 Hz, the loading current includes 0 A, 1.0 A, 2.0 A, 3.0 A and 4.0 A, and the amplitude is 10°. Figure 1 As shown in FIG. 1 , the torque-angle relationship curve is shown when the loading frequency is 1 Hz, the amplitude is 10° and the current is different; Figure 2 As shown in the figure, the torque-speed relationship curves under the conditions of loading frequency of 1 Hz, amplitude of 10° and different currents, it can be seen that the damping torque increases with the increase of current, and the magnetorheological torsional vibration damper has obvious mechanical hysteresis characteristics.

[0074] Step 2: Construct the hyperbolic tangent model of magnetorheological torsional vibration damper:

[0075]

[0076] Where T is the torsional moment, c is the viscous damping coefficient of the magnetorheological torsional vibration damper (N·s·m / rad), k is the stiffness coefficient of the magnetorheological torsional vibration damper (N·m), θ(t) is the relative angular displacement input (°) (i.e., the amplitude), t is the time, α is the expansion coefficient of the hysteresis function (N·m), and z(θ(t)) is the hysteresis operator. is the relative speed (rad / s)

[0077] In the formula, the hysteresis operator satisfies:

[0078]

[0079] Where β is a coefficient (dimensionless) determined by the magnetorheological fluid characteristics and the structure of the magnetorheological torsional vibration damper, δ is a coefficient (dimensionless) determined by the magnetorheological fluid and the control system, and sign(·) is a sign function;

[0080] The relative angular displacement input satisfies:

[0081] θ(t)=Asin(2πt);

[0082] Where A is the amplitude of the excitation (A>0), unit is °;

[0083] Step 3: In order to accurately capture the hysteresis response of the magnetorheological torsional vibration damper through the hyperbolic tangent model table, the functions corresponding to the five parameters ∩=[c, k, α, β, δ] should be determined, specifically:

[0084] 1. About the identification of k value:

[0085] Because the stiffness characteristics of the magnetorheological torsional vibration damper described by the stiffness coefficient k are independent of the magnetorheological fluid characteristics and are not affected by the current, the k value does not need to be identified at different currents. Two sinusoidal cyclic loadings are performed when the current is 0A. The relative angular displacement input is θ(t), and the relative angular displacement input after the change is θ′(t). The relative angular displacement input after the change satisfies:

[0086] θ′(t)=θ(t)+q;

[0087] Where q is a constant;

[0088] When θ(t)>0, sign(θ)=1, Therefore, we know that:

[0089] z(θ(t))=z(θ′(t));

[0090] From this we can see that:

[0091]

[0092] Then the k value satisfies:

[0093]

[0094] 2. About the identification of c value:

[0095] The hyperbolic tangent model of the magnetorheological torsional vibration damper is transformed according to the identified stiffness coefficient k value:

[0096]

[0097] In the formula, y is the intermediate parameter, from which we can get curve,

[0098] The overall trend of the hyperbolic tangent model data is used to determine the c value, such as Figure 3 , Figure 4 As shown, the hysteresis loop Z There is a parallel region between the curves, namely Figure 3 The middle circle area, this area The points on the curve can be considered parallel to the X-axis. From the calculation formula of the intermediate parameter, it can be seen that the expansion coefficient α is only The Y-axis direction of the hysteresis loop Z is proportionally enlarged based on the curve, and due to Is the formation Figure 4 The circled area is tilted along the X-axis, so c can be obtained by fitting the slope of the line segment through the data points in this area. The c value can be expressed as:

[0099]

[0100] In the formula, for The relative speed of the i-th data point in the curve graph, N is the number of test data points, y i for The response value of the i-th data point in the curve graph, for The average relative speed of the parallel area (circled area) in the curve graph, y avg for The average of the responses in parallel regions of a graph.

[0101] 3. Regarding the identification of α value, β value and δ value:

[0102] First, determine the value range of the three parameters. The α value is the expansion coefficient of the hysteresis function, that is, α>0.

[0103] The values ​​of β and δ play a decisive role in the mechanical hysteresis characteristics of the magnetorheological torsional vibration damper. The two parameters directly affect the shape of the hysteresis loop Z, such as Figure 5 As shown, the direction of the hysteresis loop Z is defined as the direction of the protruding part in the z>0 region, which is given by Figure 5 (a) It can be seen that the sign of the parameter δ affects the direction of the z-θ(t) curve. When δ>0, the z-θ(t) curve faces the right; when δ<0, the z-θ(t) curve faces the left. Figure 5 (b) It can be seen that the sign of parameter β affects The direction of the curve, when β>0, The curve goes to the right; when β<0, The curve points to the left, which can be used to determine the signs of the parameters β and δ.

[0104] The value range of parameters β and δ satisfies:

[0105]

[0106] In this embodiment, if Figure 1-3 As shown in Figure 2, the signs of β and δ are both positive, so the β and δ values ​​can narrow the parameter value range to

[0107] After determining the value ranges of α, β and δ, the α, β and δ values ​​are determined. In this embodiment, the α, β and δ values ​​are identified using a genetic algorithm, which specifically includes the following steps:

[0108] Step a, creating an initial population with the relative velocity, relative angular displacement input and torsional moment corresponding to the loading;

[0109] Step b, define the fitness evaluation criteria, set the initial population size to 400, configure the probability of crossover to pc = 0.8, define the probability of gene mutation to pm = 0.2, and limit the maximum iteration round of the algorithm to X = 500;

[0110] Step c, randomly initialize α value, β value and δ value;

[0111] Step d, initializing a set of chromosomes for each parameter;

[0112] Step e: Use the fitness function value to evaluate each chromosome and compare it with the previous one. The fitness function expression is:

[0113]

[0114] Where, T i exp is the true torsional moment of the ith data point, T i sim is the calculated torsional moment for the ith data point;

[0115] Step f: According to the fitness evaluation results of each chromosome, a roulette wheel selection strategy is adopted, in which the probability of each chromosome being selected is proportional to its fitness, thereby forming a new population.

[0116] Step g, updating chromosomes through crossover and mutation;

[0117] Step h, evaluate the new chromosome and check whether it meets the stopping criteria, where the value of the fitness function fitnes is as small as possible. If the number of iterations is higher than the limit number of iterations, report the best chromosome as the solution and output the optimal model parameters.

[0118] Among them, during the operation of the genetic algorithm, the constraints composed of linear inequalities or equations must also be satisfied. The specific expression of these conditions is

[0119] C·x≤b;

[0120] LB≤x≤UB;

[0121] In the formula, C is the inequality constraint matrix, b is the inequality constraint vector, x is the variable, LB is the lower bound of the variable, and UB is the upper bound of the variable;

[0122] α>0, β>0, δ>0 and Substituting into it we get:

[0123] LB = [0; 0; 0];

[0124]

[0125] b = 5.4;

[0126] Step 4, fitting a variation function of ∩=[c, k, α, β, δ] according to the identified multiple groups of ∩=[c, k, α, β, δ] data under different loads;

[0127] like Figure 6-9 As shown in the figure, regarding the relationship between the parameters of the magnetorheological torsional vibration damper and the applied current, under the conditions of constant strain amplitude and frequency, increasing the current applied to the magnetorheological torsional vibration damper will cause its key model parameters (c, α, δ) to increase significantly. This growth trend can be approximately described by a third-order polynomial model. At the same time, although another parameter β also changes with the current, its change is relatively gentle and is suitable for fitting with a second-order polynomial model. When the relative angle displacement input is 10° and the frequency is 1Hz, the parameter values ​​under five different current inputs of 0A, 1.0A, 2.0A, 3.0A, and 4.0A are tested respectively, and the change functions of each parameter can be fitted as follows:

[0128] c=0.1166+1.0806I-0.1424I 2 +0.006I 3 ;

[0129] k = 0.0055;

[0130] α=0.5538+0.4176I+0.608I 2 -0.0745I3 ;

[0131] δ=1.2894+1.4238I-0.521I 2 +0.063I 3 ;

[0132] β=6.5832+4.1262I-0.9494I 2 .

[0133] Verify the above parameter change function:

[0134] like Fig.10 , Fig.11 As shown, the torque-angle curve and torque-speed curve of the magnetorheological torsional vibration damper model with different current loading are the simulation value and the experimental value at the frequency of 1 Hz and the amplitude of 10° and the frequency of 0.5 Hz and the amplitude of 10° respectively. The simulation value of the model is consistent with the experimental value, and the torque-angle curve has no mutation in the zero point area and is over-smoothed, which proves that the identification method of the present invention accurately identifies the hyperbolic tangent model parameters. By proposing an identification range calculation method for the hyperbolic tangent model parameters, the problem of torque-angle curve mutation in previous identification is reduced.

[0135] Define the success rate of the optimization algorithm. Successful identification means that the absolute error between the best solution and the optimal solution searched by the fitness optimization algorithm is less than 10%. The success rate refers to the ratio of the number of successful identifications to the number of experiments:

[0136]

[0137] Among them, n1 is the number of successful recognitions, and n is the number of experiments.

[0138] The quality of the solution of the optimization algorithm is defined as the closeness of the best solution found by the optimization algorithm to the optimal value:

[0139]

[0140] Among them, m is the fitness function value that can achieve the optimal solution, and x is the minimum fitness function value of each current condition under 500 parameter identifications. ′ is the fitness function value in each experiment;

[0141] Then, the average quality of the solution is:

[0142]

[0143] In the formula, ρ i is the quality of the solution of the ith experiment of the algorithm, and n is the number of experiments.

[0144] like Fig.12 , Fig.13 As shown, the parameter identification method of the present invention is compared with the simultaneous identification of all parameters using only genetic algorithm or particle swarm algorithm. Three excitation currents (I=0A; I=2A; I=4A) are taken. The average quality of the solution and the recognition success rate of the present invention are significantly improved compared with the genetic algorithm and the particle swarm algorithm. The average fitness curve of 300 iterations of 30 independent runs shows that the genetic algorithm fails to obtain the best fitness value at the end of the simulation, and is still updating the fitness function when iterating 200 to 300 times, while the particle swarm algorithm and the identification method of the present invention can both obtain the best fitness value at the end of the simulation, and the identification method of the present invention converges to the final value the fastest, and the identification method of the present invention has the shortest calculation time, which proves that the efficiency of the identification method of the present invention in identifying the hyperbolic tangent model is better than that of using only genetic algorithm or particle swarm.

[0145] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the embodiments shown and described herein.

Claims

1. A method for identifying hyperbolic tangent model parameters of a magnetorheological torsional vibration damper, characterized in that: The steps include: Step 1: Collect the relative speed, relative angle displacement input and torsional moment corresponding to the loading; Step 2: Construct the hyperbolic tangent model of magnetorheological torsional vibration damper: Where T is the torsional moment, c is the viscous damping coefficient of the magnetorheological torsional vibration damper, k is the stiffness coefficient of the magnetorheological torsional vibration damper, θ(t) is the relative angular displacement input, t is the time, α is the expansion coefficient of the hysteresis function, z(θ(t)) is the hysteresis operator, is the relative speed, β is the coefficient determined by the magnetorheological fluid characteristics and the structure of the magnetorheological torsional vibration damper, δ is the coefficient determined by the magnetorheological fluid and the control system, and sign(·) is the sign function; Step 3: According to the hyperbolic tangent model of the magnetorheological torsional vibration damper, the viscous damping coefficient of the magnetorheological torsional vibration damper, the stiffness coefficient of the magnetorheological torsional vibration damper, the expansion coefficient of the hysteresis function, the coefficient determined by the magnetorheological fluid characteristics and the magnetorheological torsional vibration damper structure, and the constraint conditions of the coefficient determined by the magnetorheological fluid and the control system are obtained: α>0； Where T' is the torsional moment corresponding to the relative angular displacement input after the change, q is a constant, for The relative speed of the i-th data point in the curve graph, N is the number of test data points, y i for The response value of the i-th data point in the curve graph, for The average relative velocity of the parallel region in the curve, y avg for The average of the response values ​​of the parallel regions in the curve graph; Step 4: The viscous damping coefficient of the magnetorheological torsional vibration damper, the stiffness coefficient of the magnetorheological torsional vibration damper, the expansion coefficient of the hysteresis function, the coefficient determined by the magnetorheological fluid characteristics and the magnetorheological torsional vibration damper structure, and the coefficient determined by the magnetorheological fluid and the control system satisfy: c=0.1166+1.0806I-0.1424I 2 +0.006I 3 ; k＝0.0055； α=0.5538+0.4176I+0.608I 2 -0.0745I 3 ; δ=1.2894+1.4238I-0.521I 2 +0.063I 3 ; β=6.5832+4.1262I-0.9494I 2 。 2. The method for identifying hyperbolic tangent model parameters of a magnetorheological torsional vibration damper according to claim 1, characterized in that: The relative angular displacement input satisfies: θ(t)=Asin(2πt); Where A is the amplitude of the excitation, and A>0.

3. The method for identifying hyperbolic tangent model parameters of a magnetorheological torsional vibration damper according to claim 2, characterized in that: The relative angular displacement input after the change corresponds to the torsional moment satisfying: Where θ′(t) is the relative angular displacement input after the change.

4. The method for identifying hyperbolic tangent model parameters of a magnetorheological torsional vibration damper according to claim 3, characterized in that: The relative angle displacement input after the change satisfies: θ′(t)=θ(t)+q; Where q is a constant.

5. The method for identifying hyperbolic tangent model parameters of a magnetorheological torsional vibration damper according to claim 4, characterized in that: The expansion coefficient of the hysteresis function, the coefficient determined by the magnetorheological fluid characteristics and the magnetorheological torsional vibration damper structure, and the coefficient determined by the magnetorheological fluid and the control system are all identified through a genetic algorithm.

6. The method for identifying hyperbolic tangent model parameters of a magnetorheological torsional vibration damper according to claim 5, characterized in that: The genetic algorithm comprises: Step a, creating an initial population with the relative velocity, relative angular displacement input and torsional moment corresponding to the loading; Step b, define the fitness evaluation criteria, and set the initial population size, the probability of configuration crossover, the probability of gene mutation, and the maximum number of iterations; Step c, randomly initializing the expansion coefficient of the hysteresis function, the coefficient determined by the magnetorheological fluid characteristics and the magnetorheological torsional vibration damper structure, and the coefficient determined by the magnetorheological fluid and the control system; Step d, initializing a set of chromosomes for each parameter; Step e, using the fitness function value to evaluate each chromosome; Step f: Based on the fitness evaluation results of each chromosome, a roulette wheel selection strategy is adopted to form a new population; Among them, the probability of each chromosome being selected is proportional to its fitness; Step g, updating chromosomes through crossover and mutation; Step h: When the value of the fitness function converges or reaches the number of iterations, the optimal model parameters are output.

7. The method for identifying hyperbolic tangent model parameters of a magnetorheological torsional vibration damper according to claim 6, characterized in that: The fitness function is: Where, T i exp is the true torsional moment of the ith data point, T i sim is the calculated torsional moment for the ith data point.

8. The method for identifying hyperbolic tangent model parameters of a magnetorheological torsional vibration damper according to claim 7, characterized in that: The initial size of the population is 400; The probability of the configuration crossover occurring is 0.8; The probability of the gene mutation is 0.2; The maximum number of iteration rounds is 500.

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