Method and system for controlling the speed of four main shafts of a large-size ultra-thin photovoltaic silicon wafer multi-wire sawing machine

Abstract

This invention discloses a four-spindle speed control method and system for a large-size ultra-thin photovoltaic silicon wafer multi-wire dicing machine. The method includes: establishing a characteristic model for the speed control of each spindle based on feature modeling theory and the input / output data of each spindle motor; using recursive least squares method to identify the parameters of each spindle characteristic model online, and projecting the identification results to a preset parameter range set; designing a discrete superspiral speed synchronization controller for the four spindles of the multi-wire dicing machine to achieve speed synchronization of each axis, with the synchronization error converging within a finite time; and designing a discrete superspiral speed tracking controller for the four spindles of the multi-wire dicing machine to achieve speed tracking of each axis, with the tracking error converging within a finite time. This invention employs a combination of a variable power sliding surface and a discrete superspiral algorithm, significantly accelerating the convergence speed of synchronization and tracking errors, and effectively suppressing the inherent high-frequency chattering phenomenon of traditional sliding mode control, resulting in a smoother and more stable system response.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of multi-motor synchronous control, and particularly relates to a four-spindle speed control method and system for a large-size ultra-thin photovoltaic silicon wafer multi-wire cutting machine. BACKGROUND

[0002] In recent years, the rapid development of photovoltaic power generation and integrated circuit industry has driven an explosive growth in demand for silicon wafers. To meet the industry's higher standards for silicon wafer processing, such as larger size, thinner thickness, smoother surface, and higher processing efficiency, multi-wire cutting technology has become the core technology in the field of large-size ultra-thin photovoltaic silicon wafer processing due to its unique process advantages. This technology significantly improves processing efficiency and precision by simultaneously cutting a silicon rod with multiple steel wires, gradually replacing traditional external and internal circular cutting methods. In this context, multi-motor synchronous control technology for multi-wire cutting machines has become a key factor in determining the quality of silicon wafer processing, and its performance directly affects the stability of steel wire tension, cutting precision, and equipment reliability.

[0003] Currently, multi-motor synchronous control mainly uses methods such as sliding mode control, adaptive control, backstepping control, and intelligent control. Among them, sliding mode control is widely used in multi-motor synchronous systems due to its simple structure, fast response, and strong robustness. Patent CN116661308B discloses a "multi-motor servo system fixed time control method based on multi-surface sliding mode", patent CN114421835B discloses a "multi-motor control method based on fuzzy deviation coupled backstepping sliding mode strategy", and patent CN115296562B discloses a "multi-motor sliding mode cooperative control method and system based on disturbance compensation". The introduction of the sign function in the above control methods can cause rapid switching of the control quantity, causing system chattering and affecting control performance. Although continuous functions and saturation functions are used in CN111614287B and CN118487575A to replace the sign function to weaken system chattering, the robustness of sliding mode control is also sacrificed.

[0004] The above methods are all based on continuous-time models of multi-motor systems and require discretization in actual engineering applications, which can lead to a decline in control performance and increase the complexity of system debugging. In addition, although sliding mode control performs well in terms of robustness, its inherent chattering problem and slow error convergence speed have not been solved. How to maintain the robustness of sliding mode control while effectively suppressing chattering and further accelerating error convergence speed is still a technical problem that needs to be solved in the field of multi-motor synchronous control. SUMMARY

[0005] Invention purposes: The purpose of the present application is to provide a large-size ultra-thin photovoltaic silicon wafer multi-wire cutting machine four-spindle speed control method which further accelerates the error convergence speed while maintaining the robustness of the sliding mode control and effectively suppresses the chattering; on the other hand, a large-size ultra-thin photovoltaic silicon wafer multi-wire cutting machine four-spindle speed control system is provided.

[0006] Technical scheme: The photovoltaic silicon wafer multi-wire cutting machine four-spindle speed control method provided by the present application comprises the following steps:

[0007] (1) Based on the characteristic modeling theory, according to the input and output data of each spindle motor, a characteristic model of each spindle speed control is established;

[0008] (2) Recursive least squares method is used to identify the parameters of each spindle characteristic model online, and the identification results are projected to the preset parameter range set;

[0009] (3) A multi-wire cutting machine four-spindle discrete super-helical speed synchronous controller is designed to realize the synchronization of each axis speed, and the synchronization error converges in a limited time;

[0010] (4) A multi-wire cutting machine four-spindle discrete super-helical speed tracking controller is designed to realize the speed tracking of each axis, and the tracking error converges in a limited time.

[0011] The characteristic modeling theory is used to establish the characteristic model of the spindle speed control, which can accurately describe the dynamic characteristics of each spindle motor and provide an accurate model basis for subsequent control; the recursive least squares method is used to identify the model parameters online and project them to the preset range, effectively improving the real-time and robustness of parameter identification and ensuring that the model adapts to different working conditions; the designed four-spindle discrete super-helical speed synchronous controller significantly improves the synchronization accuracy of multi-axis cooperative motion, so that each spindle speed converges to a consistent value in a limited time, greatly reducing the synchronization error; at the same time, the discrete super-helical speed tracking controller further ensures the fast and accurate tracking of each spindle to the target speed, ensuring the stability and consistency of the cutting process. The overall scheme realizes high-precision synchronization and tracking control of the spindle speed in the multi-wire cutting process of ultra-thin photovoltaic silicon wafers, improves the cutting quality and efficiency, and enhances the anti-interference ability and adaptability of the system.

[0012] Preferably, step 1 comprises:

[0013] Based on the characteristic modeling theory, the current command of each spindle motor is taken as the input, and the speed response of each spindle motor is taken as the output, and a discrete characteristic model of each spindle speed control is established:

[0014] ;

[0015] In the formula, indicates the label of each motor; is the speed response of the i th motor; is the current command of the i th motor; , and are three parameters of the i th motor characteristic model.

[0016] By characteristic modeling theory, the discrete spindle speed control characteristic model is established by taking the current command of each spindle motor as input and the speed response as output, which can accurately depict the dynamic characteristics of different motors. The three parameters in the model correspond to the dynamic response characteristics of the system respectively, making the model structure simple and having clear physical meaning, which is convenient for subsequent parameter identification and control optimization. This modeling method effectively reduces the modeling difficulty of complex mechatronic systems while retaining key dynamic information, laying a precise model foundation for subsequent high-precision synchronization and tracking control and improving the adaptability and reliability of the control strategy.

[0017] Preferably, step 2 comprises:

[0018] (2.1) defining the motor data vector as:

[0019] ;

[0020] (2.2) defining the motor parameter vector as:

[0021] ;

[0022] (2.3) using the recursive least squares method with a forgetting factor to identify the characteristic model parameters online:

[0023] ;

[0024] wherein, is the Kalman gain vector of each motor; is the covariance matrix of each motor; is the identity matrix; is the original identification parameter vector of each motor; is the projected identification parameter vector of each motor; is the forgetting factor of each motor, satisfying ; is the projection operator of the identification parameter vector to its parameter range set, that is, when the identification parameter vector exceeds the parameter range set, the value of each identification parameter is equal to the nearest range boundary value; the parameter range set is:

[0025] ;

[0026] wherein, is the sampling time; The maximum value of the set system parameter change rate.

[0027] The feature model parameters are identified on-line and in real time by the recursive least square method with a forgetting factor, which can dynamically track the system parameter changes and adapt to different working conditions. The forgetting factor is introduced to reduce the weight of the historical data, so that the model pays more attention to the latest dynamics and improves the sensitivity of parameter updating. The parameter estimation accuracy is optimized by Kalman gain and covariance matrix, which ensures the rapid convergence and stability of the identification process. At the same time, the identification results are constrained in the preset reasonable range by the parameter projection operator, which avoids parameter drift or abnormal fluctuation and enhances the robustness of the system. The design of parameter range set further limits the parameter change rate, prevents control instability caused by sudden changes, and ensures the reliability of the identification process and the overall performance of the control system.

[0028] Preferably, step 3 comprises:

[0029] (3.1) The four main shaft motors are divided into two groups, the first group including the first and third motors, and the second group including the second and fourth motors;

[0030] (3.2) The in-group synchronization error is defined as: ; in the formula, is the in-group synchronization error of the first group; is the in-group synchronization error of the second group;

[0031] (3.3) The inter-group synchronization error is defined as: ; in the formula, is the inter-group synchronization error;

[0032] (3.4) The four main shaft discrete super-helix speed synchronization controller of the multi-wire cutting machine is designed as:

[0033]

[0034]

[0035]

[0036]

[0037] ; in the formula, is the synchronization control input of the i-th motor; is a sign function; is a sampling time; is an integral part; all are control parameters.

[0038] ​​​​​By grouping the four main shaft motors and defining the synchronization error within and between groups respectively, hierarchical collaborative optimization is realized, effectively reducing the complexity of multi-axis synchronous control. The synchronization controller designed based on the discrete super-spiral algorithm can quickly suppress synchronization error and ensure high-precision convergence of intra-group and inter-group speed within a limited time, significantly improving the consistency of multi-axis collaborative motion. The nonlinear terms (such as the sign function) in the controller enhance the robustness of the system to disturbances and parameter uncertainties, while the discretization design makes it more suitable for digital control environment. This scheme ensures strong anti-interference ability while avoiding high-frequency chattering problem, making the multi-wire cutting process more stable and reliable, thereby improving the cutting precision and surface quality of ultra-thin silicon wafer.

[0039] Preferably, the integral quantity and control parameters satisfy:

[0040] ; ; ; .

[0041] By reasonable gain distribution, fast convergence and stable control of synchronization error are realized, where each parameter adjusts the convergence speed, anti-interference ability and dynamic response characteristics of the system respectively, ensuring high-precision coordination of intra-group and inter-group synchronization; by optimizing parameter combination, the controller effectively suppresses overshoot and chattering while ensuring strong robustness, enabling the multi-spindle system to maintain smooth and stable synchronization performance under complex working conditions; the synergistic effect of parameters further enhances the dynamic adjustment ability of the system, enabling the cutting process to maintain excellent synchronization precision under load changes or external disturbances, thereby ensuring the uniformity and yield of ultra-thin silicon wafer cutting.

[0042] Preferably, step 4 comprises:

[0043] (4.1) defining the tracking error of each motor as:

[0044] ; in the formula, is the tracking error of the i-th motor; is the speed response of the i-th motor; is the expected speed;

[0045] (4.2) defining the system tracking error as:

[0046] ; in the formula, is the system tracking error;

[0047] (4.3) constructing a variable power sliding mode surface as:

[0048] ; in the formula, is the sliding mode surface; ; , are control parameters; is sampling time;

[0049] (4.4) The four-motor discrete hyper-spiral speed tracking controller of the multi-wire saw is designed as:

[0050] ; in the formula, is the tracking control input of the i th motor; ; , , are control parameters;

[0051] (4.5) The four-motor discrete hyper-spiral speed controller of the multi-wire saw is finally designed as:

[0052] ; in the formula, is the control input of the i th motor.

[0053] By defining the tracking error of each motor and the comprehensive tracking error of the system, accurate monitoring and control of the target speed are achieved. The innovative variable power sliding mode surface design enhances the adaptive adjustment ability of the system in the dynamic process, so that the tracking error can quickly converge and remain stable. The tracking controller designed based on the discrete hyper-spiral algorithm, combined with multi-parameter collaborative optimization, effectively suppresses the chattering phenomenon of traditional sliding mode control, while improving the response speed and anti-interference of the system. Finally, through the fusion output of synchronous control and tracking control, the stable tracking performance of the multi-motor system under high-speed precision cutting conditions is realized, ensuring that each motor can quickly respond to changes in speed instructions and maintain excellent dynamic consistency, thereby significantly improving the cutting quality and production efficiency of ultra-thin silicon wafers.

[0054] Preferably, the power parameters and are designed as:

[0055] ;

[0056] ; in the formula, , are control parameters.

[0057] By dynamically adjusting the power parameters and, the convergence characteristics of the sliding mode surface can adapt to the changes in the tracking error. When the error is large, a higher power is used to speed up the convergence, and when it approaches the steady state, the power is reduced to improve the accuracy, achieving an optimal balance between fast response and high precision. This ensures that the multi-motor system can achieve high dynamic and high-precision speed tracking control during the cutting process of ultra-thin silicon wafers.

[0058] The second aspect of the present application discloses a four-spindle speed control system of a photovoltaic silicon wafer multi-wire sawing machine, which comprises:

[0059] A characteristic modeling module: based on the characteristic modeling theory, a characteristic model of the speed control of each spindle is established according to the input and output data of each spindle motor;

[0060] A parameter identification module: recursive least squares method is used to identify the parameters of each spindle characteristic model online, and the identification results are projected to a preset parameter range set;

[0061] A speed synchronization control module: containing a discrete super-helix speed synchronization controller, which is used to realize the speed synchronization control of the four spindles, and ensure that the synchronization error converges within a limited time;

[0062] A speed tracking control module: containing a discrete super-helix speed tracking controller, which is used to realize the speed tracking control of each spindle, and ensure that the tracking error converges within a limited time;

[0063] A comprehensive control module: used to integrate the outputs of the speed synchronization control and the speed tracking control, and generate the final control instructions of each spindle motor.

[0064] The third aspect of the present application further provides a computer device, which comprises a memory and a processor, and the memory stores a computer program capable of being loaded and executed by the processor to implement the four-spindle speed control method of the large-size ultra-thin photovoltaic silicon wafer multi-wire sawing machine.

[0065] The fourth aspect of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by the processor to implement the four-spindle speed control method of the large-size ultra-thin photovoltaic silicon wafer multi-wire sawing machine.

[0066] Beneficial effects: compared with the prior art, the present application has the following remarkable advantages: 1. By designing a discrete super-spiral speed synchronization and tracking controller, while maintaining the inherent robustness of the sliding mode control, the method of combining variable power sliding surface and discrete super-spiral algorithm is adopted to significantly accelerate the convergence speed of synchronization error and tracking error, and effectively suppress the inherent high-frequency chattering phenomenon of traditional sliding mode control, so that the system response is more smooth and stable; 2. By establishing the discrete characteristic model of the main shaft motor, and using the recursive least squares method with forgetting factor for online parameter identification, the system can track the motor parameter changes in real time, and endows the controller with excellent adaptive ability, ensuring that the optimal control performance can be maintained under different working conditions; 3. The innovative grouping synchronization control strategy cooperates with the discrete super-spiral algorithm to realize high-precision speed synchronization control among multiple main shafts, significantly improving the coordination performance of each main shaft motor in the cutting process, and ensuring the quality consistency of ultra-thin silicon wafer cutting; 4. By constructing a sliding surface with adaptive power characteristics and a discrete super-spiral control law, the system automatically adjusts the convergence rate at different error stages, ensuring fast response at the initial stage and precise convergence when approaching the equilibrium point, and optimizing the dynamic response characteristics of the system as a whole. BRIEF DESCRIPTION OF DRAWINGS

[0067] Figure 1 The method flowchart of the present application is shown in the figure.

[0068] Figure 2 The control block diagram of the method of the present application is shown in the figure.

[0069] Figure 3 The curve graph of the speed response of each motor of the present application is shown in the figure.

[0070] Figure 4 The curve graph of the tracking error of each motor of the present application is shown in the figure.

[0071] Figure 5 The curve graph of the synchronization error within and between groups of the present application is shown in the figure.

[0072] Figure 6 The curve graph of the motor model parameter identification of the present application is shown in the figure.

[0073] Figure 7 The control input curve graph of each motor of the present application is shown in the figure. DETAILED DESCRIPTION

[0074] The technical solutions of the present application will be further described below with reference to the accompanying drawings.

[0075] The present application provides a four-main-shaft speed control method for large-size ultra-thin photovoltaic silicon wafer multi-wire cutting machine, as shown in the figure. Figure 1 The method comprises the following steps:

[0076] Step 1, according to the characteristic modeling theory, based on the input and output of each axis motor, the characteristic model of each axis for speed control is established; Step 1 is specifically:

[0077] According to the characteristic modeling theory, the characteristic model of each axis for speed control is established with the motor current command of each axis as input and the motor speed response of each axis as output:

[0078] ;

[0079] Wherein, denotes the motor label; is the speed response of the i-th motor; is the current command of the i-th motor; 、 and are three parameters of the i-th motor characteristic model.

[0080] Step 2, the recursive least squares method is used to identify the parameters of each axis characteristic model online, and the identified values are projected to the preset parameter range set; Step 2 is specifically:

[0081] The motor data vector is defined as:

[0082] ;

[0083] The motor parameter vector is defined as:

[0084] ;

[0085] The recursive least squares method with forgetting factor is used to identify the characteristic model parameters online:

[0086] ;

[0087] Wherein, is the Kalman gain vector of each motor; is the covariance matrix of each motor; is the unit matrix; is the original identification parameter vector of each motor; is the projected identification parameter vector of each motor; is the forgetting factor of each motor, satisfying ; is the projection operator of the identification parameter vector to its parameter range set, that is, when the identification parameter vector exceeds the parameter range set, the value of each identification parameter is equal to the nearest range boundary value; The parameter range set is:

[0088] ;

[0089] Wherein, is the sampling time; is the maximum value of the set system parameter change rate.

[0090] Step 3, design a multi-wire saw four-spindle discrete super-spiral speed synchronization control method to realize the synchronization of each spindle speed, and the synchronization error converges in a limited time; as shown in the figure, step 3 is specifically: Figure 2

[0091] The four motors are divided into two groups, the first group includes the first, third motor, and the second group includes the second, fourth motor; define the group synchronization error as:

[0092] ; ;

[0093] Wherein, is the first group of group synchronization error; is the second group of group synchronization error;

[0094] Define the inter-group synchronization error as:

[0095] ;

[0096] Wherein, is the inter-group synchronization error;

[0097] The multi-wire saw four-spindle discrete super-spiral speed synchronization controller is designed as:

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] Wherein, is the synchronization control input of the i-th motor; is the sign function; is the sampling time; ; ; ; All are control parameters.

[0103] Step 4, design a multi-wire saw four-spindle discrete super-spiral speed tracking control method to realize the speed tracking of each spindle, and the tracking error converges in a limited time; step 4 is specifically:

[0104] Define the tracking error of each motor as:

[0105] ​

[0106] where, is the tracking error of the ith motor; is the speed response of the ith motor; is the desired speed;

[0107] Define the system tracking error as:

[0108] ;

[0109] where, is the system tracking error;

[0110] Construct a variable power sliding mode surface as:

[0111] ;

[0112] where, is the sliding mode surface; ; , is the control parameter; is the sampling time; the power and are designed as:

[0113] ;

[0114] ;

[0115] where, , is the control parameter.

[0116] Design a four-motor discrete super-spiral speed tracking controller for a multi-wire cutting machine as:

[0117] ;

[0118] where, is the tracking control input of the ith motor; ; , , are all control parameters.

[0119] Finally, design a four-motor discrete super-spiral speed controller for a multi-wire cutting machine as:

[0120] ;

[0121] where, is the control input of the ith motor.

[0122] The proposed multi-wire cutting machine spindle speed control method for the ultra-thin photovoltaic silicon wafer is simulated in a virtual environment below, and the feasibility of the control method is verified.

[0123] Motor parameters: motor inertia is , torque coefficient is , induced electromotive force coefficient is , resistance is , inductance is ;

[0124] Control parameters: , , , , , , , , , , , , ;

[0125] Figure 3 The motor speed response curves are shown, and the motor speed response reciprocates between 1000 rpm and-1000 rpm.

[0126] Figure 4 The motor tracking error curves are shown, and the motor tracking error is very small and has no obvious chattering.

[0127] Figure 5 The in-group and inter-group synchronization error curves are shown, and the in-group and inter-group synchronization error is kept in a very small range and has no obvious chattering.

[0128] Figure 6 The motor model parameter identification curves are shown, and the motor characteristic model parameter identification results converge quickly and remain stable.

[0129] Figure 7 The motor control input curves are shown, which are obtained by superimposing the tracking control input and the synchronization control input, and ensure the system tracking and synchronization performance.

[0130] The experimental results show that the four-spindle discrete super-helix speed control method for the large-size ultra-thin photovoltaic silicon wafer multi-wire cutting machine can make the motor speed response closely follow the expected speed, the tracking accuracy is high, the speed between the motors keeps high consistency, and the synchronization accuracy is high.

[0131] Based on the similar inventive concept, the embodiment of the application also provides a photovoltaic silicon wafer multi-wire cutting machine four-spindle speed control system corresponding to the photovoltaic silicon wafer multi-wire cutting machine four-spindle speed control method, comprising:

[0132] Characteristic modeling module: based on characteristic modeling theory, input and output data of each spindle motor are used to establish a characteristic model of each spindle speed control;

[0133] Parameter identification module: recursive least squares method is used to identify the parameters of each spindle characteristic model online, and the identification results are projected to the preset parameter range set;

[0134] Speed synchronization control module: containing a discrete superhelix speed synchronization controller, used to realize the speed synchronization control of four spindles, and ensure that the synchronization error converges in a finite time;

[0135] Speed tracking control module: containing a discrete superhelix speed tracking controller, used to realize the speed tracking control of each spindle, and ensure that the tracking error converges in a finite time;

[0136] Comprehensive control module: used to integrate the outputs of speed synchronization control and speed tracking control, and generate the final control instructions of each spindle motor.

[0137] The application further discloses an electronic device.

[0138] Specifically, the electronic device can be a computer device such as a desktop computer, a notebook computer, a palm computer and a cloud server. The computer device can include but is not limited to a processor and a memory. The processor and the memory can be connected through a bus or other means. The processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, graphics processing units (GPU), embedded neural network processing units (NPU) or other dedicated deep learning coprocessors, discrete gates or transistor logic devices, discrete hardware components, etc. chips, or combinations of the above chips.

[0139] The memory, as a non-transitory computer readable storage medium, can be used to store non-transitory software programs, non-transitory computer executable programs and modules. The processor performs various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory. The memory can include a program storage area and a data storage area, wherein the program storage area can store application programs required by the control unit and at least one function; and the data storage area can store data created by the processor and the like. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0140] The application further discloses a computer readable storage medium.

[0141] Specifically, the computer readable storage medium is used to store a computer program, and the computer program is executed by the processor to realize the method in the above method embodiments.

[0142] Those skilled in the art can understand that all or part of the processes in the above method embodiments of the application can be completed by a computer program instructing related hardware, and the program can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc. The storage medium can also include a combination of the above types of memories.

Claims

1. A method for controlling the speed of four main shafts of a multi-wire saw for slicing photovoltaic silicon wafers, characterized in that, The method comprises the following steps: (1) based on the characteristic modeling theory, according to the input and output data of each spindle motor, a characteristic model of each spindle speed control is established; (2) the recursive least squares method is used to identify the parameters of each spindle characteristic model online, and the identification results are projected to the preset parameter range set; (3) a four-spindle discrete super-helical speed synchronization controller of the multi-wire cutting machine is designed to realize the speed synchronization of each axis, and the synchronization error converges in a limited time; (4) a four-spindle discrete super-helical speed tracking controller of the multi-wire cutting machine is designed to realize the speed tracking of each axis, and the tracking error converges in a limited time; The step (1) comprises: Based on the characteristic modeling theory, the current command of each spindle motor is taken as the input, and the speed response of each spindle motor is taken as the output, and a discrete characteristic model of each spindle speed control is established: where, denotes the motor index; is the speed response of the ith motor; is the current command of the ith motor; , and are three parameters of the ith motor characteristic model; The step (2) comprises: (2.1) the motor data vector is defined as: (2.2) defining each motor parameter vector as: (2.3) Online identification of the characteristic model parameters using a recursive least squares method with a forgetting factor: ; where, is the Kalman gain vector of each motor; is the covariance matrix of each motor; is the identity matrix; is the original identification parameter vector of each motor; is the projected identification parameter vector of each motor; is the forgetting factor of each motor, satisfying ; is the projection operator of the identification parameter vector to its parameter range set, i.e. when the identification parameter vector exceeds the parameter range set, the value of each identification parameter is equal to the nearest range boundary value; the parameter range set is: ; wherein is the sampling time; is the set maximum rate of change of system parameters; The step (3) comprises: (3.1) the four spindle motors are divided into two groups, the first group includes the first and third motors, and the second group includes the second and fourth motors; (3.2) the group synchronization error is defined as: ; ; wherein is a first set of inter-group synchronization errors; is a second set of inter-group synchronization errors; (3.3) the group synchronization error is defined as: ; wherein is the inter-group synchronization error; (3.4) the four-spindle discrete super-helical speed synchronization controller of the multi-wire cutting machine is designed as: ; ; ; ; wherein is a synchronous control input for the i-th motor; is a sign function; is a sampling time; is an integral part; , , , , are control parameters; The step (4) comprises: (4.1) the motor tracking error is defined as: ; where, is the tracking error for the i-th motor; is the speed response for the i-th motor; is the desired speed; (4.2) the system tracking error is defined as: ; wherein is the system tracking error; (4.3) a variable power sliding mode surface is constructed as: ; wherein is a sliding surface; ; , is a control parameter; is a sampling time; and is a power parameter; (4.4) the four-spindle discrete super-helical speed tracking controller of the multi-wire cutting machine is designed as: ; wherein is a tracking control input for the i-th motor; ; , , are control parameters; (4.5) the four-spindle discrete super-helical speed controller of the multi-wire cutting machine is finally designed as: ; where, is the control input to the i-th motor.

2. The speed control method according to claim 1, characterized by, The integral quantity and the control parameter satisfy: ； ； ； 、 、 、 、 。 3. The speed control method according to claim 1, characterized by, the power parameter and are designed as: ; ; wherein , is a control parameter.

4. A four-spindle speed control system for a multi-wire saw for slicing photovoltaic silicon wafers for performing the method according to any one of claims 1 to 3, characterized in that comprise: a characteristic modeling module: based on the characteristic modeling theory, according to the input and output data of each spindle motor, a characteristic model of each spindle speed control is established; a parameter identification module: the recursive least squares method is used to identify the parameters of each spindle characteristic model online, and the identification results are projected to the preset parameter range set; a speed synchronization control module: containing a discrete super-helical speed synchronization controller, used for realizing the speed synchronization control of the four spindles, ensuring that the synchronization error converges in a limited time; a speed tracking control module: containing a discrete super-helical speed tracking controller, used for realizing the speed tracking control of each spindle, ensuring that the tracking error converges in a limited time; a comprehensive control module: used for integrating the outputs of the speed synchronization control and the speed tracking control to generate the final control command of each spindle motor.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the four-spindle speed control method of the multi-wire cutting machine for photovoltaic silicon wafers according to any one of claims 1-3.

6. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized by The processor executes the program to realize the four-spindle speed control method of the multi-wire cutting machine for photovoltaic silicon wafers according to any one of claims 1-3.

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