A method for optimizing thrust characteristics of cylindrical permanent magnet linear motor based on matrix algorithm

Abstract

The present invention discloses a method for optimizing the thrust characteristics of a cylindrical permanent magnet linear motor based on a matrix algorithm. The method utilizes a SEM measurement model to group the stator tooth height, stator tooth width, slot height, slot width, air gap width, pole width, and pole height parameters to construct two dynamic measurement models. The SEM measurement model is then quantified to obtain the TPMLM average thrust value and the TPMLM thrust fluctuation value. A sub-Hankel matrix is established based on a G-L fractional-order derivative algorithm. The two sub-Hankel matrices are rotationally transformed into a Hankel matrix bundle. The determinant of the Hankel matrix bundle is solved and used as the optimization coefficient for the motor thrust and thrust fluctuation values. Compared with the prior art, the present invention utilizes an SEM measurement model, a matrix algorithm, and a G-L fractional-order derivative algorithm to optimize the motor design, thereby improving the thrust of the cylindrical permanent magnet linear motor and reducing the thrust fluctuation of the motor, thereby improving the motor performance and reducing the cost of permanent magnet materials.

Description

Technical Field

[0001] The present invention belongs to the technical field of motor structural parameter design, and in particular relates to a method for optimizing the thrust characteristics of a cylindrical permanent magnet linear motor based on a matrix algorithm. Background Art

[0002] In the design of permanent magnet linear motors (TPMLMs), stator core splitting and slotting are fundamental structural features that enable their functionality. However, these design choices inevitably lead to two significant effects: end effect and cogging. These effects profoundly impact motor performance, particularly thrust ripple.

[0003] Cogging is caused by the interaction between the stator teeth and the permanent magnets. This interaction results in periodic variations in the cogging force, which in turn causes thrust instability. Similarly, end effect occurs at the ends of the stator core. Due to the discontinuous distribution of the magnetic field in these areas, additional end forces are generated, further increasing thrust fluctuations. These thrust fluctuations not only reduce the motor's operating efficiency but also limit its suitability for precision control and high-performance applications.

[0004] To comprehensively improve the performance of permanent magnet linear motors and expand their application prospects, the key lies in reducing thrust fluctuations and increasing average thrust through design optimization. However, how to achieve this is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] Purpose of the invention: In response to the problems pointed out in the background technology, the present invention discloses a method for optimizing the thrust characteristics of a cylindrical permanent magnet linear motor based on a matrix algorithm. The SEM measurement model, matrix algorithm and GL fractional-order derivative algorithm are used to optimize the design of the motor, thereby improving the thrust of the cylindrical permanent magnet linear motor and reducing the thrust fluctuation of the motor.

[0006] Technical solution: The present invention discloses a method for optimizing the thrust characteristics of a cylindrical permanent magnet linear motor based on a matrix algorithm, comprising the following steps:

[0007] Step 1: Model the TPMLM parameter phasor as a mathematical model consisting of stator tooth height components h0 and h1, stator tooth width component w2, slot height component h2, slot width component w3, air gap width component w4, pole width component w1, and pole height component h3. Divide the eight structural parameters into two groups: the first group is the structural parameters h0, h1, and w2 related to the stator teeth; the second group is the other five structural parameters h2, h3, w1, w3, and w4. Collect multiple values of the first and second groups.

[0008] Step 2: Construct the SEM measurement model, use the collected data to quantify the SEM measurement model, and mathematically express the relationship between the latent variables and manifest variables, as well as the latent variables of the SEM measurement model;

[0009] Step 3: Obtain the TPMLM average thrust latent variable F and the TPMLM thrust fluctuation fitting characteristic latent variable K through the SEM measurement model;

[0010] Step 4: Based on the GL fractional derivative algorithm, a sub-Hankel matrix is established for the TPMLM average thrust latent variable and the TPMLM thrust fluctuation fitting characteristic latent variable data set, and the two sub-Hankel matrices are rotated and transformed into a Hankel matrix bundle;

[0011] Step 5: Obtain the determinant of the Hankel matrix bundle as the optimization coefficient of the TPMLM average thrust and TPMLM thrust fluctuation. Determine the optimal parameters by multiplying the optimization coefficient with the TPMLM average thrust latent variable F and the TPMLM thrust fluctuation fitting characteristic latent variable K obtained in step 3.

[0012] Furthermore, the SEM measurement model is as follows:

[0013] X1=e1σ+δ

[0014] X2=e2γ+ξ

[0015] Where: X1 and X2 are the output indicators of the external signal source, e1 is the factor loading of the external signal source output indicator on the exogenous latent variable, σ is the exogenous latent variable, δ is the measurement error of the external signal source output indicator f1; e2 is the factor loading of the external signal source output indicator on the exogenous latent variable, γ is the exogenous latent variable, ξ is the measurement error of the external signal source output indicator f2; the measurement error range of δ is [-2.0, +2.0], and the measurement error range of ξ is [-0.9, +0.9].

[0016] Furthermore, the collected data is used to quantify the data of the SEM measurement model, and the relationship between the latent variables, manifest variables and latent variables of the measurement model is mathematically expressed as follows:

[0017]

[0018] Among them, F is the TPMLM average thrust latent variable, K is the fitting characteristic latent variable of TPMLM thrust fluctuation, e i , i=1,2,3……n is the factor loading of the output index of the external signal source to the exogenous latent variable;

[0019] SEM measurement model e i, i=1,2,3……n connects the external signal source output indicators to quantify the factor loading of the exogenous latent variable:

[0020]

[0021] Furthermore, a sub-Hankel matrix is established for the TPMLM average thrust latent variable and the TPMLM thrust fluctuation fitting characteristic latent variable data set, and the two sub-Hankel matrices are combined into a Hankel matrix bundle, as follows:

[0022] S1: Set f(t) to be continuous in the interval [a, b], generate N equally spaced nodes in the interval [0, t], with a step size of L, and the first-order derivative of f(t) is expressed as follows using backward difference:

[0023]

[0024] Among them, f(-1) is not continuous in the interval and is 0; t i =t0+iL,t i is the time value of the i-th equidistant node in the interval [0, t]. Since the nodes are equidistant, the time interval between each node is fixed, that is, the step length L; t0 is the first node in the interval [0, t]; i h From the first node t0 to the i-th node t i The time interval is multiplied by i. Since the step length is L, the total distance from the first node to the i-th node is i times the step length, that is, iL;

[0025] S2: For the Nth node backward difference derivative expression of the mean thrust of one component of the TPMLM parameter phasor:

[0026]

[0027] Where, ▽ is the backward difference of the function f(t) at the node t0, which is used to approximate the derivative of the point; the backward difference is the difference between the function value from the current point t1 to the previous point t0, divided by the step size L; f0 is the value of the function f(t) at the first node t0, f1 is the value of the function f(t) at the second node t1, f N is the function f(t) at the last node t N The value of

[0028] S3: The backward difference of the Nth node is expressed in vector matrix form as:

[0029]

[0030] Where M is the vector matrix of f′(t), T N is the function value vector matrix obtained at equidistant nodes, A1 N is the first-order backward difference vector matrix;

[0031] S4: The backward difference vector matrix Q(L) of the n-th order derivative of f(t) obtained by the above transformation is expressed as follows:

[0032]

[0033] in,

[0034] S5: Given the backward difference vector matrix Q(L) of the Nth node from S4, find the backward difference approximation matrix at the Nth node:

[0035]

[0036] Among them, α is the order value of the backward difference vector matrix Q, L -α The αth power of the reciprocal of the step length L represented by ;

[0037] S6: Extract the phasor matrix vector Q from the S5 backward difference approximation matrix h0 :

[0038]

[0039] S7: Q in S6 h0 The matrix is rotated 90° counterclockwise to obtain the fitting characteristic Hankel matrix J of the average thrust of TPMLM F , converted into matrix product for calculation;

[0040] S8: Repeat the above operation to obtain the fitting characteristic Hankel matrix J of TPMLM thrust fluctuation K .

[0041] Furthermore, the specific process of solving the determinant value of the Hankel matrix bundle is:

[0042] Step 1) Fitting characteristic Hankel matrix J of TPMLM average thrust F , solve for J F The determinant value, the fitting characteristic Hankel matrix J of the TPMLM average thrust F for:

[0043]

[0044] Step 2) Obtain the fitting characteristic Hankel matrix J of the PMLM average thrust F The determinant of , multiply the diagonal elements of the lower triangular matrix, where the diagonal elements are:

[0045]

[0046] Get the determinant value:

[0047] Step 3) Since each term contains L -a , extract it and calculate the product of the remaining parts:

[0048]

[0049] Step 4) Repeat the above steps to obtain the fitting characteristic Hankel matrix J of PMLM thrust fluctuation K The determinant value of ;

[0050] Step 5) The fitting characteristic Hankel matrix J of the PMLM average thrust is obtained F Multiply the determinant value of by the theoretical formula calculated value of TPMLM average thrust F, and we get |J F |·F=F new ;

[0051] Step 6) Repeat steps 1) to 5) to obtain the fitting characteristic Hankel matrix J of PMLM thrust fluctuation K Multiplying the determinant value of with the theoretical formula value of TPMLM thrust fluctuation K, we get

[0052] |J K |·K=K new .

[0053] Beneficial effects:

[0054] The present invention adopts SEM measurement model, matrix algorithm and GL fractional-order derivative algorithm to optimize the design of the motor, thereby improving the thrust of the cylindrical permanent magnet linear motor and reducing the thrust fluctuation of the motor.

[0055] 1. Improved Motor Performance: This invention utilizes a SEM measurement model to group the stator tooth height, stator tooth width, slot height, slot width, air gap width, pole width, and pole height parameters to construct two dynamic measurement models. The SEM measurement model data is then quantified to determine the TPMLM average thrust value and TPMLM thrust fluctuation value, respectively. This precise SEM measurement model and matrix algorithm enable detailed grouping and quantitative analysis of key motor parameters, helping to understand the distribution of the motor's internal electromagnetic field, thereby improving the motor's operating efficiency and thrust through design optimization.

[0056] 2. Reduce thrust fluctuation: The present invention establishes a sub-Hankel matrix based on the GL fractional-order derivative algorithm, rotates and transforms the two sub-Hankel matrices into a Hankel matrix bundle, and solves the determinant value of the Hankel matrix bundle as the optimization coefficient of the motor thrust and thrust fluctuation value, effectively reducing the thrust fluctuation of the motor during operation and improving the accuracy and smoothness of its motion control.

[0057] 3. Reduce material costs: The present invention optimizes the design of motor parameter structure data, improves the thrust characteristics of the motor, helps to reduce the amount of permanent magnet material used, and thus reduces manufacturing costs.

[0058] 4. Expand the scope of application: By improving motor performance and reducing manufacturing costs, the present invention makes this cylindrical permanent magnet linear motor more competitive in various precision control and high-performance application fields. The optimized motor can be widely used in industrial automation, precision machinery, electric transportation and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is the structural diagram of the TPMLM model of the present invention;

[0060] Figure 2 This is the TPMLM structural parameter diagram of the present invention;

[0061] Figure 3 This is a schematic diagram of the SEM structural equation of the present invention;

[0062] Figure 4 Schematic diagram of the matrix algorithm structure of the present invention. DETAILED DESCRIPTION

[0063] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0064] The present invention discloses a method for optimizing the thrust characteristics of a cylindrical permanent magnet linear motor based on a matrix algorithm, which specifically includes the following steps:

[0065] Step 1: Model the TPMLM parameter phasor as a mathematical model consisting of stator tooth height components h0 and h1, stator tooth width component w2, slot height component h2, slot width component w3, air gap width component w4, pole width component w1, and pole height component h3.

[0066] Step 2: Divide the eight structural parameters into two groups. The first group is the stator tooth height components h0 and h1, and the stator tooth width component w2. The second group is the remaining five structural parameters: the slot height component h2, the pole height component h3, the pole width component w1, the slot width component w3, and the air gap width component w4.

[0067] Step 3: Construct SEM measurement model:

[0068] X1=e1σ+δ

[0069] X2=e2γ+ξ

[0070] Where X1 and X2 are the external signal source output indicators, e1 is the factor loading of the external signal source output indicator on the exogenous latent variable, σ is the exogenous latent variable, δ is the measurement error of the external signal source output indicator f1; e2 is the factor loading of the external signal source output indicator on the exogenous latent variable, γ is the exogenous latent variable, and ξ is the measurement error of the external signal source output indicator f2. The measurement error range of δ is [-2.0, +2.0], and the measurement error range of ξ is [-0.9, +0.9].

[0071] Step 4: Collect and input the first set of values for the stator tooth structural parameters: stator tooth height components h0 and h1, and stator tooth width component w2. Collect and input the second set of values: slot height component h2, magnetic pole height component h3, magnetic pole width component w1, slot width component w3, and air gap width component w4.

[0072] The input data collected in this embodiment are as follows: Tables 1 and 2:

[0073] Table 1 Structural parameters of stator teeth

[0074]

[0075] Table 2 The remaining five structural parameters

[0076]

[0077]

[0078] Step 5: Quantify the data for the SEM measurement model and mathematically express the relationship between the latent variables, manifest variables, and latent variables of the measurement model as follows:

[0079]

[0080] Among them, F is the TPMLM average thrust latent variable, K is the fitting characteristic latent variable of TPMLM thrust fluctuation, e i(i=1,2,3……n) is the factor loading of the output indicator of the external signal source to the exogenous latent variable.

[0081] Step 6: Measure the model for SEM i (i=1,2,3……n) Connect the output indicators of external signal sources to quantify the factor loading of exogenous latent variables:

[0082]

[0083] Step 7: Calculate the TPMLM average thrust value and TPMLM thrust fluctuation value through the SEM measurement model, as shown in Tables 3 and 4 below:

[0084] Table 3 Thrust values for Group 1

[0085]

[0086] Table 4 Thrust values for group 2

[0087]

[0088] Step 8: Create a sub-Hankel matrix for the TPMLM average thrust latent variable and the TPMLM thrust fluctuation fitting characteristic latent variable data set, and combine the two sub-Hankel matrices into a Hankel matrix bundle. The specific process is as follows:

[0089] S1: Set f(t) to be continuous in the interval [a,b], generate N equally spaced nodes in the interval [0,t], with a step size of L, where t i =t0+iL(i=0,1,2,...,N), the first-order derivative of f(t) is expressed as follows using backward difference:

[0090]

[0091] Among them, f(-1) is not continuous in the interval and is 0.

[0092] t i : The time value of the i-th equidistant node in the interval [0, t]. Since the nodes are equidistant, the time interval between each node is fixed, that is, the step size L.

[0093] t0: The first node in the interval [0, t], usually the starting point, here equal to 0.

[0094] i h :From the first node (t0) to the i-th node (t i ) multiplied by i. Since the step size is L, the total distance from the first node to the i-th node is i times the step size, i.e. iL.

[0095] S2: For the Nth node backward difference derivative expression of the mean thrust of one component of the TPMLM parameter phasor:

[0096]

[0097] ▽: Backward difference of function f(t) at node t0, used to approximate the derivative at that point. Backward difference is the difference between the function value at the current point t1 and the previous point t0, divided by the step size L.

[0098] f0: The value of function f(t) at the first node t0.

[0099] f1: The value of function f(t) at the second node t1.

[0100] f N :Function f(t) at the last node t N The value of .

[0101] S3: The backward difference of the Nth node is expressed in vector matrix form as:

[0102]

[0103] Where M is the vector matrix of f′(t), T N is the function value vector matrix obtained at equidistant nodes, A 1 N is the first-order backward difference vector matrix.

[0104] S4: The backward difference vector matrix Q(L) of the n-th order derivative of f(t) obtained by the above transformation is expressed as follows:

[0105]

[0106] in,

[0107] S5: The backward difference vector matrix of the Nth node is given by step S4. S4 gives the backward difference vector matrix Q(L) of the nth order derivative of f(t). Find the backward difference approximation matrix at the Nth node:

[0108]

[0109] Among them, α is the order value of the backward difference vector matrix Q, L -α Represents the αth power of the reciprocal of the step size L.

[0110] S6: Extract the phasor matrix vector Q from the S5 backward difference approximation matrix h0 :

[0111]

[0112] S7: Q in S6 h0 The matrix is rotated 90° counterclockwise to obtain the fitting characteristic Hankel matrix J of the average thrust of TPMLM F , converted into matrix product for calculation, making the calculation easier.

[0113] S8: Repeat the above operation to convert the fitting characteristic Hankel matrix J of TPMLM thrust fluctuation into K .

[0114] The specific process of solving the determinant value of the Hankel matrix bundle is:

[0115] 1) Fitting characteristics of PMLM average thrust Hankel matrix J F , solve for J F The determinant value, the fitting characteristic Hankel matrix J of the TPMLM average thrust F :

[0116]

[0117] 2) Obtain the fitting characteristic Hankel matrix J of the PMLM average thrust F The determinant of , multiply the diagonal elements of the lower triangular matrix, where the diagonal elements are,

[0118]

[0119] Get the determinant value:

[0120] 3) Since each term contains L -a , extract it and calculate the product of the remaining parts:

[0121]

[0122] 4) Repeat the above steps to obtain the fitting characteristic Hankel matrix J of PMLM thrust fluctuation K The determinant value of .

[0123] 5) The fitting characteristic Hankel matrix J of the PMLM average thrust is obtained F Multiply the determinant value of by the theoretical formula calculated value of TPMLM average thrust F, and we get |J F |·F=F new .

[0124] 6) Repeat 1 to 5) to obtain the fitting characteristic Hankel matrix J of PMLM thrust fluctuation K Multiply the determinant value of by the theoretical formula calculated value of TPMLM thrust fluctuation K to get |JK |·K=K new .

[0125] F new , K new The two determinant values are multiplied by F and K respectively. F and K are the specific values obtained by calculation in step 7. The two determinant values are optimization coefficients. The specific values of F and K are further optimized.

[0126] The above embodiments are intended only to illustrate the technical concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. They are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made in accordance with the spirit of the present invention are intended to be covered by the scope of protection of the present invention.

Claims

1. A method for optimizing thrust characteristics of a cylindrical permanent magnet linear motor based on a matrix algorithm, characterized in that: The steps include: Step 1: Model the parameter phasors of the permanent magnet linear motor (TPMLM) as a mathematical model consisting of stator tooth height components h0 and h1, stator tooth width component w2, slot height component h2, slot width component w3, air gap width component w4, pole width component w1, and pole height component h3. Divide the eight structural parameters into two groups: the first group is the structural parameters related to the stator teeth: h0, h1, and w2; the second group is the other five structural parameters: h2, h3, w1, w3, and w4. Collect multiple values of the first and second groups. Step 2: Construct two dynamic SEM measurement models, use the collected data to quantify the SEM measurement models, and mathematically express the relationships between the latent variables, manifest variables, and latent variables of the SEM measurement models; Step 3: Obtain the TPMLM average thrust latent variable F and the TPMLM thrust fluctuation fitting characteristic latent variable K through the SEM measurement model; Step 4: Based on the GL fractional derivative algorithm, a sub-Hankel matrix is established for the TPMLM average thrust latent variable and the TPMLM thrust fluctuation fitting characteristic latent variable data set, and the two sub-Hankel matrices are rotated and transformed into a Hankel matrix bundle; S1: Set f(t) to be continuous in the interval [a,b], generate N equidistant nodes in the interval [0,t], and the step size is L; S2: Calculate the derivative of the Nth node of the backward difference of the mean thrust, one of the components of the TPMLM parameter phasor; S3: Represent the backward difference of the Nth node in the form of a vector matrix; S4: Obtain the n-th order derivative backward difference vector matrix Q(L) of f(t) through the above transformation; S5: Given the backward difference vector matrix Q(L) of the Nth node by S4, find the backward difference approximation matrix at the Nth node; S6: Extract the phasor matrix vector from the backward difference approximation matrix of S5; S7: Q in S6 h0 The matrix is rotated 90° counterclockwise to obtain the fitting characteristic Hankel matrix J of the average thrust of TPMLM F ; S8: Repeat the above operation to obtain the fitting characteristic Hankel matrix J of TPMLM thrust fluctuation K ; Step 5: Obtain the determinant of the Hankel matrix bundle as the optimization coefficient of the TPMLM average thrust and TPMLM thrust fluctuation. Determine the optimal parameters by multiplying the optimization coefficient with the TPMLM average thrust latent variable F and the TPMLM thrust fluctuation fitting characteristic latent variable K obtained in step 3.

2. The method for optimizing thrust characteristics of a cylindrical permanent magnet linear motor based on a matrix algorithm according to claim 1, characterized in that: The collected data is used to quantify the data of the SEM measurement model, and the relationship between the latent variables, manifest variables and latent variables of the measurement model is mathematically expressed as follows: K=-740+e 11 w2+e 12 h3+e 13 w4+e 14 w1+e 15 h3 2 +e 16 w1 2 +e 17 w2h3+e 18 w2w4+e 19 w2w1+e 20 h3w4+e 21 h3w1+e 22 w4w1 Among them, F is the TPMLM average thrust latent variable, K is the fitting characteristic latent variable of TPMLM thrust fluctuation, e i is the factor loading of the external signal source output indicator to the exogenous latent variable, i = 1, 2, 3, ..., 22; The factor loading of the SEM measurement model connecting the external signal source output indicator to the exogenous latent variable is e i To quantify:

3. The method for optimizing thrust characteristics of a cylindrical permanent magnet linear motor based on a matrix algorithm according to claim 2, characterized in that: A sub-Hankel matrix is established for the TPMLM average thrust latent variable and the TPMLM thrust fluctuation fitting characteristic latent variable data set, and the two sub-Hankel matrices are combined into a Hankel matrix bundle, as follows: S1: Set f(t) to be continuous in the interval [a, b], generate N equally spaced nodes in the interval [0, t], with a step size of L, and the first-order derivative of f(t) is expressed as follows using backward difference: Among them, f(-1) is not continuous in the interval and is 0; t j =t0+jL,t j is the time value of the jth equidistant node in the interval [0, t]. Since the nodes are equidistant, the time interval between each node is fixed, that is, the step size L; t0 is the first node in the interval [0, t]; S2: For the Nth node backward difference derivative expression of the mean thrust of one component of the TPMLM parameter phasor: Where, ▽ is the backward difference of the function f(t) at the node t0, which is used to approximate the derivative of the point; the backward difference is the difference between the function value from the current point t1 to the previous point t0, divided by the step size L; f0 is the value of the function f(t) at the first node t0, f1 is the value of the function f(t) at the second node t1, f N is the function f(t) at the last node t N The value of S3: The backward difference of the Nth node is expressed in the form of a vector matrix as follows: Where M is the vector matrix of f′(t), T N is the function value vector matrix obtained at equidistant nodes, A 1 N is the first-order backward difference vector matrix; S4: The backward difference vector matrix Q(L) of the n-th order derivative of f(t) obtained by the above transformation is expressed as follows: in, m=0,1,2,...,n; S5: Given the backward difference vector matrix Q(L) of the Nth node from S4, find the backward difference approximation matrix at the Nth node: Among them, α is the order value of the backward difference vector matrix Q(L), L -α The αth power of the reciprocal of the step length L represented by ; S6: Extract the phasor matrix vector Q from the S5 backward difference approximation matrix h0 : S7: Q in S6 h0 The matrix is rotated 90° counterclockwise to obtain the fitting characteristic Hankel matrix J of the average thrust of TPMLM F ; S8: Repeat the above operation to obtain the fitting characteristic Hankel matrix J of TPMLM thrust fluctuation K .

4. The method for optimizing thrust characteristics of a cylindrical permanent magnet linear motor based on a matrix algorithm according to claim 3, characterized in that: The specific process of solving the determinant value of the Hankel matrix bundle is: Step 1) Fitting characteristic Hankel matrix J of TPMLM average thrust F , solve for J F The determinant value, the fitting characteristic Hankel matrix J of the TPMLM average thrust F for: Step 2) Obtain the fitting characteristic Hankel matrix J of the TPMLM average thrust F The determinant of , multiply the diagonal elements of the lower triangular matrix, where the diagonal elements are: Get the determinant value: Step 3) Since each term contains L -α , extract it and calculate the product of the remaining parts: Step 4) Repeat the above steps to obtain the fitting characteristic Hankel matrix J of TPMLM thrust fluctuation K The determinant value of ; Step 5) The fitting characteristic Hankel matrix J of the average thrust of TPMLM is obtained F Multiply the determinant value of by the theoretical formula calculated value of TPMLM average thrust F, and we get |J F |·F=F new ; Step 6) The fitting characteristic Hankel matrix J of TPMLM thrust fluctuation is obtained K Multiply the determinant value of by the theoretical formula calculated value of TPMLM thrust fluctuation K to get |J K |·K=K new .

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