Method for autonomously identifying parameters of shield cutterhead driving motor

Abstract

The invention discloses a shield cutterhead driving motor parameter autonomous identification method, and relates to the technical field of motor control, and the method comprises the steps: building a permanent magnet synchronous motor parameter identification mathematical model which comprises a reference model and an adjustable model of a permanent magnet synchronous motor; establishing a fitness function of a vector control system of the permanent magnet synchronous motor based on the reference model and the adjustable model of the permanent magnet synchronous motor; and solving a fitness function of the vector control system of the permanent magnet synchronous motor by using an XDBO algorithm, finding a solution vector which enables the fitness function value to be minimum, obtaining an optimal identification result, and completing motor parameter identification. The invention provides an innovative method for autonomously identifying the parameters of the shield cutterhead driving motor, the parameters can be accurately and effectively identified, the convergence algebra is few, the identification precision is high, the speed is high, the stability is high, the method is suitable for a permanent magnet synchronous motor parameter identification system, the practicability is good, and the method is worthy of popularization.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to a method for autonomously identifying motor parameters. Background Art

[0002] The cutterhead of a shield machine is the core working part of the shield machine. Its main function is to cut soil and crush rocks to form a tunnel. The drive motor of the cutterhead provides it with powerful power to drive the cutterhead to rotate in order to cut and crush the soil. This process directly affects the tunneling efficiency, excavation quality and energy consumption level of the tunnel. Therefore, the performance of the cutterhead drive motor is crucial in the working process of the shield machine. Modern shield machines usually use high-power permanent magnet synchronous motors (PMSMs) driven by high-performance inverters. This type of motor has a wide speed regulation range and good control performance, and can maintain stable operation under high load and complex working conditions. In high-performance speed regulation systems, methods such as vector control or direct torque control are often used. These methods rely on the accurate description of the mathematical model of the motor. The model parameters include resistance, inductance, mutual inductance, moment of inertia, friction coefficient, etc. Through accurate motor parameters, accurate control signals can be calculated to achieve efficient torque control, speed control or position control.

[0003] In order to ensure the accuracy and efficiency of these control systems, the parameters of the motor must be accurately identified. Traditional parameter identification methods include recursive least squares (RLS), model reference adaptive method (MARS), extended Kalman filter (EKF), etc. Although the principle of the least squares method is simple and easy to implement, it may lead to data saturation and is not suitable for actual use. Although the model reference adaptive algorithm is easier to implement, it is necessary to derive the corresponding adaptive law and consider the coupling and parameter accuracy, otherwise it may lead to non-convergence or large errors. The extended Kalman filter algorithm is suitable for high-performance servo systems, but it is greatly affected by the initial value, which reduces the robustness and accuracy of parameter identification.

[0004] Since the PMSM control system has nonlinear time-varying characteristics, it is quite difficult to accurately identify its parameters. Considering factors such as convergence performance, computing power and cost, traditional methods often cannot quickly find the optimal value. In order to solve these problems, in recent years, more and more researchers have begun to introduce intelligent optimization algorithms into the field of motor parameter identification. Compared with traditional methods, these intelligent optimization algorithms can overcome the limitations of traditional methods, provide higher identification accuracy, and have stronger adaptability, especially in complex working conditions. Summary of the invention

[0005] The present invention proposes a method for autonomously identifying parameters of a shield cutter head drive motor, aiming to provide an innovative method for autonomously identifying parameters of a shield cutter head drive motor, so as to solve the technical problem of low identification accuracy of traditional shield cutter head drive motor parameter identification methods.

[0006] In order to achieve the above object, the technical solution of the present invention is achieved as follows:

[0007] A method for autonomously identifying parameters of a shield cutter head drive motor comprises the following steps:

[0008] S1. Establishing a mathematical model for parameter identification of a permanent magnet synchronous motor, including a reference model and an adjustable model of the permanent magnet synchronous motor;

[0009] S2. Based on the reference model and adjustable model of the permanent magnet synchronous motor, a fitness function of the permanent magnet synchronous motor vector control system is established;

[0010] S3. Use the XDBO algorithm to solve the fitness function of the permanent magnet synchronous motor vector control system, find the solution vector that minimizes the fitness function value, obtain the optimal identification result, and complete the motor parameter identification.

[0011] The method for establishing a mathematical model for parameter identification of a permanent magnet synchronous motor is as follows: establishing a stator current equation of the permanent magnet synchronous motor in a dq axis rotating coordinate system, a dynamic model and an adjustable model of a vector control system of the permanent magnet synchronous motor; and establishing a reference model and an adjustable model of the permanent magnet synchronous motor based on the stator current equation of the permanent magnet synchronous motor in a dq axis rotating coordinate system and in combination with the dynamic model and the adjustable model of the vector control system of the permanent magnet synchronous motor.

[0012] The fitness function of the permanent magnet synchronous motor vector control system is: Among them, R s is the stator resistance, L d and L q are the d-axis and q-axis inductances in the dq-axis rotation coordinate system, ψ f is the permanent magnet flux, i d and i q They are the d-axis and q-axis current components in the dq-axis rotating coordinate system, For the adjustable model d-axis current, is the q-axis current of the adjustable model.

[0013] The fitness function of the permanent magnet synchronous motor vector control system solved by the XDBO algorithm includes:

[0014] Use the Sobol sequence to initialize the population and set the XDBO algorithm parameters, which include the optimal solution value range, the maximum number of iterations, the population size, the lower bound of the search space, and the upper bound of the search space.

[0015] Use the XDBO algorithm to update the population position and increase the number of iterations by 1;

[0016] During the iteration process, the fitness function of the permanent magnet synchronous motor vector control system is corrected according to the output errors of the reference model and the adjustable model of the magnetic synchronous motor. The iteration is continued until the output error is minimized, or the iteration is stopped when the set maximum number of iterations is reached. The solution vector that minimizes the fitness function value is output to obtain the optimal identification result.

[0017] The Sobol sequence is used to initialize the population, and the random number R generated by the Sobol sequence n ∈[0,1], the population initial position x n =x min +R n ·(x max -x min ), x min is the minimum value of the optimal resistance range, x max It is the maximum value of the optimal resistance value range.

[0018] When using the XDBO algorithm to update the position, the social learning strategy and the best candidate solution strategy are adopted.

[0019] The probability of a population encountering an obstacle is η. Suppose a natural random number γ∈[0,1], when γ≤η, generate a random number K∈[0,π], and when the random number K is a special value, the position is not updated, otherwise the position is updated; when the random number γ>η, it is an obstacle-free state, and the social learning strategy is used to update the position of the population.

[0020] When the random number K is not a special value, the position is updated as follows:

[0021]

[0022]

[0023] In the formula, x best is the best position of the population after t iterations; t is the current iteration number; represents the average candidate position of other populations at this time, is the result of generation t+1, is the result of generation t, is the result of generation t-1, θ is the angle value of the function tan, x w To solve for the vector, x new Solve for the new generation of vector sets, x roll is the number of vector sets, is the tth solution vector in the set, is the optimal solution vector of the previous generation, is the discriminant formula, x new,ε is the historical optimal solution vector.

[0024] The special value is 0, π.

[0025] The expression for updating the position of the population using the social learning strategy is as follows:

[0026]

[0027] Among them, x rand is the candidate position of a randomly selected species, x w is the candidate position of the species with the worst position after the tth iteration, and △u represents the position x of the eth species after the tth iteration. w With location The Chebyshev distance between them, b is the control parameter, k is the current number of iterations, Distance chebychev Represents the weight parameter obtained by solving the vector information at different positions.

[0028] During population reproduction, the spawning area of ​​the female population is simulated by a boundary selection strategy, which is defined as:

[0029]

[0030] Among them, X * Indicates the current local optimal position, Lb * , Ub * Respectively represent the lower and upper bounds of the spawning area, Lb and Ub represent the lower and upper bounds of the search space, R = 1-t / T max is the inertia weight, T max Indicates the maximum number of iterations;

[0031] After the egg-laying area is determined, the female insects lay eggs in the egg-laying area. Each female insect only produces one egg in each iteration. The egg-laying position is updated using the best candidate solution strategy, and the expression is:

[0032]

[0033] in, represents the direction between the current position and the optimal position, and γ is the weight used to increase the probability of generating the optimal position.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides an innovative method for autonomous identification of shield cutter drive motor parameters; the present invention can not only accurately and effectively identify parameters, but also has few convergence algebras, high identification accuracy, fast speed, and strong stability. It is suitable for permanent magnet synchronous motor parameter identification system, has good practicality, and is worthy of promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 It is a flow chart of the present invention.

[0037] Figure 2 It is a simulation block diagram of the present invention.

[0038] Figure 3 Initialize the mapping profile for the Sobol sequence of the present invention, where Figure 3 -(a) is the random mapping particle distribution diagram, Figure 3 -(b) is the particle distribution diagram of Sobol sequence mapping.

[0039] Figure 4 The effect diagram of optimizing four international test functions for XDBO algorithm, PSO algorithm and COA algorithm.

[0040] Figure 5 This is a matlab / simulink simulation identification effect diagram of the present invention; wherein Figure 5 -(a) is the diagram of the stator resistance simulation process; Figure 5 -(b) is the d-axis inductance simulation process diagram; Figure 5 -(c) is the q-axis inductance simulation process diagram; Figure 5 -(d) is the magnetic flux simulation process diagram.

[0041] Figure 6 This is a diagram showing the effect of the platform experiment after the data is collected by the present invention; Figure 6 -(a) is the diagram of the stator resistance simulation process; Figure 6 -(b) is the d-axis inductance simulation process diagram; Figure 6 -(c) is the q-axis inductance simulation process diagram; Figure 6 -(d) is the magnetic flux simulation process diagram. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] A method for autonomous identification of shield cutterhead drive motor parameters, such as Figure 1 As shown in the figure, to realize the parameter identification of permanent magnet synchronous motor, firstly, a reference model, an adjustable model and a fitness function are established based on the mathematical model of permanent magnet synchronous motor vector control, and the parameter identification is realized by comparing the output value of the reference model of the permanent magnet synchronous motor with the output value of the actual model. Then, the algorithm parameters are set, the population is initialized using the Sobol sequence, and a new population position update strategy is designed, that is, the social learning strategy and the best candidate solution strategy are used to optimize the population position update, and the fitness function is corrected according to the output error of the reference model and the adjustable model. The iteration is continued until the output error is minimized, or the iteration is stopped when the set maximum number of iterations is reached, and the optimal parameter identification result is obtained. Figure 2 It is a simulation block diagram of the present invention, which embodies the feasibility of the present invention.

[0044] The specific steps of the present invention are as follows:

[0045] S1. Establish a mathematical model for permanent magnet synchronous motor parameter identification, including a reference model and an adjustable model of the permanent magnet synchronous motor.

[0046] Furthermore, the method for establishing a mathematical model for parameter identification of a permanent magnet synchronous motor is as follows: establishing a stator current equation of the permanent magnet synchronous motor in a dq-axis rotating coordinate system, a dynamic model and an adjustable model of a vector control system of the permanent magnet synchronous motor; based on the stator current equation of the permanent magnet synchronous motor in a dq-axis rotating coordinate system and combined with the dynamic model and adjustable model of the vector control system of the permanent magnet synchronous motor, a reference model and an adjustable model of the permanent magnet synchronous motor are established.

[0047] Specifically, the permanent magnet synchronous motor is regarded as an ideal motor, the core saturation is ignored, the spatial magnetic field is considered to be sinusoidal, and factors such as core hysteresis loss and eddy current loss are not considered. d =0 control method, the permanent magnet synchronous motor is modeled, and the stator current equation of the permanent magnet synchronous motor in the dq axis rotating coordinate system is:

[0048]

[0049] Among them, u d and u q are the d-axis and q-axis voltage components in the dq-axis rotating coordinate system, respectively. d and i q are the d-axis and q-axis current components in the dq-axis rotating coordinate system, respectively, d and L q are the d-axis and q-axis inductances in the dq-axis rotation coordinate system, R s is the stator resistance, ψ f is the permanent magnet flux, ω e is the rotor electrical angular velocity, and s is the differential operator.

[0050] The dynamic model of the permanent magnet synchronous motor vector control system is:

[0051]

[0052] Where p is the parameter vector, x is the internal variable of the permanent magnet synchronous motor vector control system, y is the output of the permanent magnet synchronous motor vector control system, u is the input of the permanent magnet synchronous motor vector control system, f and g are the dynamic functions of the permanent magnet synchronous motor vector control system, It is the time derivative of the internal variable x of the permanent magnet synchronous motor vector control system.

[0053] An adjustable model of the permanent magnet synchronous motor vector control system is established as shown in the following formula:

[0054]

[0055] in, is the time derivative of the internal variable x of the permanent magnet synchronous motor vector control system in the adjustable model, is the internal variable of the permanent magnet synchronous motor vector control system in the adjustable model, is the parameter vector in the adjustable model, u is the input of the permanent magnet synchronous motor vector control system in the adjustable model, is the output of the permanent magnet synchronous motor vector control system in the adjustable model.

[0056] The reference model of permanent magnet synchronous motor is:

[0057]

[0058] The adjustable model of permanent magnet synchronous motor is:

[0059]

[0060] in, For the adjustable model d-axis current, is the adjustable model q-axis current, is the resistance to be identified in the adjustable model, is the q-axis inductance to be identified in the adjustable model, is the flux linkage of the permanent magnet to be identified in the adjustable model, is the d-axis inductance to be identified in the adjustable model.

[0061] S2. Based on the reference model and adjustable model of the permanent magnet synchronous motor, a fitness function of the permanent magnet synchronous motor vector control system is established.

[0062] The fitness function is a weighted quadratic function:

[0063]

[0064] In the formula, is the fitness function containing the identification parameters, W is a positive definite matrix;

[0065] According to the fitness function, the stator resistance R is calculated s , d-axis inductance L in dq-axis rotating coordinate system d , q-axis inductance L in dq-axis rotating coordinate system q 、Permanent magnet flux ψ f The fitness of the four parameters; after comparison, the particle with the best position is selected to participate in the next operation, and this cycle continues until the fitness function approaches 0, and the stator resistance R is obtained. s , d-axis inductance L in dq-axis rotating coordinate system d , q-axis inductance L in dq-axis rotating coordinate system q 、Permanent magnet flux ψ f The four parameter values ​​are determined as the true values ​​of the permanent magnet synchronous motor vector control system, and the fitness function of the permanent magnet synchronous motor vector control system is defined as:

[0066]

[0067] S3. Use the XDBO (X Dung beetle optimizer) algorithm to solve the fitness function of the permanent magnet synchronous motor vector control system, find the solution vector that minimizes the fitness function value, obtain the optimal identification result, and complete the motor parameter identification.

[0068] The XDBO algorithm is obtained by changing the way of initializing the population of the DBO (Dung beetle optimizer) algorithm and introducing a social learning strategy and a best candidate solution strategy.

[0069] The fitness function of the permanent magnet synchronous motor vector control system solved by the XDBO algorithm includes:

[0070] The population is initialized using the Sobol sequence and the parameters of the XDBO algorithm are set. The XDBO algorithm parameters include the optimal solution value range, the maximum number of iterations, the population size, the lower bound of the search space, and the upper bound of the search space.

[0071] Use the XDBO algorithm to update the population position and increase the number of iterations by 1;

[0072] During the iteration process, the fitness function of the permanent magnet synchronous motor vector control system is corrected according to the output errors of the reference model and the adjustable model of the magnetic synchronous motor. The iteration is continued until the output error is minimized, or the iteration is stopped when the set maximum number of iterations is reached. The solution vector that minimizes the fitness function value is output to obtain the optimal identification result.

[0073] like Figure 3 As shown in Figure 2, the Sobol sequence mapping particle distribution is more uniform, which is conducive to the algorithm exploring uniformity in the process of searching for the optimal value.

[0074] Assume that the optimal solution range is [x min ,x max ], the random number R generated by the Sobol sequence n ∈[0,1], the initial position of the population is:

[0075] x n =x min +R n ·(x max -x min ) (8)

[0076] Assume that the probability of the population encountering an obstacle is η = 0.5, and set the natural random number γ∈[0,1]. When γ≤η, generate a random number K∈[0,π]. No location update is performed when , otherwise the location update is as follows:

[0077]

[0078] In the formula, x best is the best position of the population after t iterations; t is the current iteration number; represents the average candidate position of other populations at this time, is the result of generation t+1, is the result of generation t, is the result of generation t-1, θ is the angle value of the function tan, x w To solve for the vector, x new Solve for the new generation of vector sets, x roll is the number of vector sets, is the tth solution vector in the set, is the optimal solution vector of the previous generation, is the discriminant formula, x new,ε is the historical optimal solution vector.

[0079] Then when the random number γ>η, it is an obstacle-free state, and the population position iteration model for updating the social learning strategy is:

[0080]

[0081] Among them, x rand is the candidate position of a randomly selected species, x w is the candidate position of the species with the worst position after the tth iteration, and △u represents the position x of the eth species after the tth iteration. w With location The Chebyshev distance between them, b is the control parameter, k is the current number of iterations, Distance chebychev Represents the weight parameter obtained by solving the vector information at different positions.

[0082] In the process of population reproduction, in order to provide a safe environment for species to lay eggs and reproduce, a boundary selection strategy is proposed to simulate the spawning area of ​​the female population, which is defined as:

[0083]

[0084] Among them, X * Indicates the current local optimal position, Lb * ,Ub * Respectively represent the lower and upper bounds of the spawning area, Lb and Ub represent the lower and upper bounds of the search space, R = 1-t / T max is the inertia weight, T max Indicates the maximum number of iterations.

[0085] After the egg-laying area is determined, the female insect will choose this area to lay eggs. For the XDBO algorithm, each female insect only produces one egg in each iteration, which is one solution. As can be seen from formula (14), the boundary of the egg-laying area is dynamically changing and is determined by the inertia weight R. Therefore, the egg-laying position is also dynamic during the iteration process:

[0086] B i (t+1)=X * +b1(B i (t)-Lb * )+b2(B i (t)-Ub * ) (15)

[0087] In the formula, B i (t) is the position of the i-th spawning ball at the t-th iteration, b1 and b2 are two independent random vectors of size 1×D, and D represents the dimension of the optimization problem. From formula (15), it can be seen that the optimal spawning position is limited by the values ​​of the two random numbers b1 and b2, that is, the probability of generating the optimal position and the worst position is the same. Therefore, the best candidate solution strategy is used to improve the position update formula of formula (15) to:

[0088]

[0089] in, Indicates the direction between the current position and the optimal position, and γ is the weight used to increase the probability of generating the optimal position. As the number of iterations increases, if it is close to the optimal solution, the search ability decreases, and if it is far away from the optimal solution, the search ability increases.

[0090] In order to verify the effectiveness of the XDBO algorithm, the XDBO algorithm is compared with the PSO and COA algorithms. The number of iterations is set to 500 and the population size is 50. The international optimization test function test in Table 1 is performed.

[0091] Table 1 Examples of internationally accepted continuous function extreme value optimization

[0092]

[0093] The error and iteration curves of different algorithms for different test functions are as follows: Figure 4 As shown in the figure, it can be clearly seen that the XDBO algorithm is superior to the COA and PSO algorithms in terms of fitness convergence. In addition, the XDBO algorithm also shows a significant advantage in the number of iterations. At about the fifth iteration, it has quickly converged to make the function error zero, showing the superiority of the XDBO algorithm in terms of convergence speed. Figure 4 (c) shows that the PSO algorithm converges slowly and has poor stability. Figure 4 In (d), the convergence speed of the COA algorithm is improved compared with that of the PSO. Specifically, the COA algorithm begins to stabilize after at least 200 iterations. The PSO algorithm converges to 0 at about 500 generations, and the error is large. In contrast, the XDBO algorithm proposed in the present invention can achieve rapid error convergence to zero within the 100th generation, with almost no fluctuation.

[0094] In summary, the XDBO algorithm proposed in the present invention has faster convergence speed, stronger stability and higher accuracy compared with the COA and PSO algorithms; and it is effective and feasible to apply it to the parameter identification of permanent magnet synchronous motor systems.

[0095] To further verify the effectiveness of the XDBO algorithm, a permanent magnet synchronous motor parameter identification platform was built, and the permanent magnet synchronous motor operating data was collected when the permanent magnet synchronous motor was running stably. The operating data of the permanent magnet synchronous motor in the control chip was saved and uploaded to the host computer, and the motor parameter identification was completed. Through 10 independent repeated experiments, the average results were obtained to obtain the motor identification parameters.

[0096] Under the operating conditions of the PMSM parameters in Table 2, Table 2 is as follows:

[0097] Table 2 PMSM parameters

[0098]

[0099] First, use Matlab / simulink simulation software to simulate and verify the XDBO algorithm. The output errors of the reference model and the adjustable model are corrected according to the fitness function until they are minimized. If the error meets the accuracy requirement, the iteration ends. Otherwise, the iteration will continue until the maximum number of iterations is reached, thereby obtaining the optimal identification result. The identification simulation results based on the XDBO algorithm are shown in Figure 2. Figure 5 shown.

[0100] Secondly, the data collected by the experimental platform is uploaded to the host computer, and then the identification equation is formed to complete the identification. After 10 independent repeated experiments, the average results are obtained to obtain the motor identification parameters. The experimental identification results based on the XDBO algorithm are as follows: Figure 6 As shown. Figure 5 and Figure 6 As shown in the figure, the XDBO algorithm also has good results in identifying the electrical parameters of PMSM in the experiment. Although the experimental results have slightly larger errors than the simulation results, the overall error is still within the controllable range, and the overall results are almost consistent with the simulation results. Both experimental and simulation results can prove the effectiveness of the algorithm proposed in the present invention in identifying the electrical parameters of PMSM.

[0101] By statistically analyzing 50 sets of data, the error results are shown in Table 3:

[0102] Table 3 Comparison of three PMSM parameter identification algorithms

[0103]

[0104] As can be seen from Table 3, the average error of the XDBO algorithm proposed in this invention for identifying motor parameters is around 1%, and the identification results are accurate and meet the motor control requirements. Compared with the COA and PSO algorithms, the identification results of the XDBO algorithm are significantly better than the other two algorithms. It can be seen that the parameter identification of the XDBO algorithm proposed in this article has the characteristics of high accuracy, less convergence algebra, fast speed, strong stability, etc., and its application prospect in permanent magnet synchronous motor parameter identification is better than COA and PSO algorithms.

[0105] The present invention proposes a method for autonomously identifying the parameters of a shield cutter drive motor. First, a permanent magnet synchronous motor reference model, an adjustable model and a fitness function are established based on the mathematical model of permanent magnet synchronous motor vector control. Secondly, a Sobol sequence initialization population strategy is proposed to increase population diversity and thus improve the quality of the solution. A new population position update strategy is designed to improve local search capabilities and improve algorithm convergence accuracy. Finally, the simulation and experimental platform verify that the present invention can accurately and effectively identify parameters. The present invention proposes a method for autonomously identifying the parameters of a shield cutter drive motor, which can effectively realize intelligent optimization of parameters. The simulation verifies that the method can not only accurately and effectively identify parameters, but also has significant improvements in convergence algebra, identification accuracy, speed and stability. It is suitable for permanent magnet synchronous motor parameter identification systems, has good practicality, and is worthy of promotion.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for autonomously identifying parameters of a shield cutterhead drive motor, characterized in that: The following steps are involved: S1. Establishing a mathematical model for parameter identification of a permanent magnet synchronous motor, including a reference model and an adjustable model of the permanent magnet synchronous motor; S2. Based on the reference model and adjustable model of the permanent magnet synchronous motor, a fitness function of the permanent magnet synchronous motor vector control system is established; S3. Use the XDBO algorithm to solve the fitness function of the permanent magnet synchronous motor vector control system, find the solution vector that minimizes the fitness function value, obtain the optimal identification result, and complete the motor parameter identification.

2. The method for autonomously identifying parameters of a shield cutterhead drive motor according to claim 1 is characterized in that: The method for establishing a mathematical model for parameter identification of a permanent magnet synchronous motor is as follows: establishing a stator current equation of the permanent magnet synchronous motor in a dq axis rotating coordinate system, a dynamic model and an adjustable model of a vector control system of the permanent magnet synchronous motor; and establishing a reference model and an adjustable model of the permanent magnet synchronous motor based on the stator current equation of the permanent magnet synchronous motor in a dq axis rotating coordinate system and in combination with the dynamic model and the adjustable model of the vector control system of the permanent magnet synchronous motor.

3. The method for autonomously identifying parameters of a shield cutter head drive motor according to claim 2, characterized in that: The fitness function of the permanent magnet synchronous motor vector control system is: Among them, R s is the stator resistance, L d and L q are the d-axis and q-axis inductances in the dq-axis rotation coordinate system, ψ f is the permanent magnet flux, i d and i q They are the d-axis and q-axis current components in the dq-axis rotating coordinate system, For the adjustable model d-axis current, is the q-axis current of the adjustable model.

4. The method for autonomously identifying parameters of a shield cutter head drive motor according to claim 3 is characterized in that: The fitness function of the permanent magnet synchronous motor vector control system solved by the XDBO algorithm includes: Use the Sobol sequence to initialize the population and set the XDBO algorithm parameters, which include the optimal solution value range, the maximum number of iterations, the population size, the lower bound of the search space, and the upper bound of the search space. Use the XDBO algorithm to update the population position and increase the number of iterations by 1; During the iteration process, the fitness function of the permanent magnet synchronous motor vector control system is corrected according to the output errors of the reference model and the adjustable model of the magnetic synchronous motor. The iteration is continued until the output error is minimized, or the iteration is stopped when the set maximum number of iterations is reached. The solution vector that minimizes the fitness function value is output to obtain the optimal identification result.

5. The method for autonomously identifying parameters of a shield cutter head drive motor according to claim 4 is characterized in that: The Sobol sequence is used to initialize the population, and the random number R generated by the Sobol sequence n ∈[0,1], the population initial position x n =x min +R n ·(x max -x min ), x min is the minimum value of the optimal resistance range, x max It is the maximum value of the optimal resistance value range.

6. The method for autonomously identifying parameters of a shield cutterhead drive motor according to claim 4 or 5, characterized in that: When using the XDBO algorithm to update the position, the social learning strategy and the best candidate solution strategy are adopted.

7. The method for autonomously identifying parameters of a shield cutter head drive motor according to claim 6, characterized in that: The probability of a population encountering an obstacle is η. Suppose a natural random number γ∈[0,1], when γ≤η, generate a random number K∈[0,π], and when the random number K is a special value, the position is not updated, otherwise the position is updated; when the random number γ>η, it is an obstacle-free state, and the social learning strategy is used to update the position of the population.

8. The method for autonomously identifying parameters of a shield cutterhead drive motor according to claim 7, characterized in that: When the random number K is not a special value, the position is updated as follows: In the formula, x best is the best position of the population after t iterations; t is the current iteration number; x represents the average candidate position of other populations at this time, is the result of generation t+1, is the result of generation t, is the result of generation t-1, θ is the angle value of the function tan, x w To solve for the vector, x new Solve for the new generation of vector sets, x roll is the number of vector sets, is the tth solution vector in the set, is the optimal solution vector of the previous generation, is the discriminant formula, x new,ε is the historical optimal solution vector.

9. The method for autonomously identifying parameters of a shield cutterhead drive motor according to claim 8, characterized in that: The expression for updating the position of the population using the social learning strategy is as follows: Among them, x rand is the candidate position of a randomly selected species, x w is the candidate position of the species with the worst position after the tth iteration, and △u represents the position x of the eth species after the tth iteration. w With location The Chebyshev distance between them, b is the control parameter, k is the current number of iterations, Distance chebychev Represents the weight parameter obtained by solving the vector information at different positions.

10. The method for autonomously identifying parameters of a shield cutterhead drive motor according to claim 9, characterized in that: During population reproduction, the spawning area of ​​the female population is simulated by a boundary selection strategy, which is defined as: Among them, X * Indicates the current local optimal position, Lb * ,Ub * Respectively represent the lower and upper bounds of the spawning area, Lb and Ub represent the lower and upper bounds of the search space, R = 1-t / T max is the inertia weight, T max Indicates the maximum number of iterations; After the egg-laying area is determined, the female insects lay eggs in the egg-laying area. Each female insect only produces one egg in each iteration. The egg-laying position is updated using the best candidate solution strategy, and the expression is: in, represents the direction between the current position and the optimal position, and γ is the weight used to increase the probability of generating the optimal position.

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