A direct drive piezoelectric spindle and control method

Abstract

The present application discloses a direct-drive piezoelectric spindle and a control method, belonging to the technical field of machine tool spindles, including: an industrial control computer receives the first operation signal collected by a collection device, extracts the dynamic characteristic signal of the electric spindle from the first operation signal, and models the extracted dynamic characteristic signal; adjusts the parameters in the linear conversion process according to the load change of the spindle; sets the instruction structure of the operation parameters of the electric spindle and outputs a control instruction; optimizes the control strategy through a machine learning model. During the implementation of the technical solution of the present application, by extracting the dynamic characteristic signal of the electric spindle and modeling the extracted dynamic characteristic signal, more accurate parameter prediction and control can be achieved, effectively reducing the response delay and insufficient accuracy caused by load changes. At the same time, combined with the dynamic adjustment of the real-time load situation, the electric spindle can maintain high stability and machining accuracy in a complex and changeable machining environment.

Description

Technical Field

[0001] This application relates to the technical field of machine tool spindles, and specifically to a direct drive piezoelectric spindle and a control method therefor. Background Art

[0002] An electric spindle is a power component widely used in numerical control machine tools and high-precision machining equipment. It is usually connected to an electric motor and directly driven by the motor to rotate the spindle without a traditional mechanical transmission device, thereby improving the energy transmission efficiency and response speed.

[0003] With the development of intelligence, the electric spindle is no longer simply connected to the electric motor, but realizes functions such as power control, motion control, feedback and monitoring, and safety protection through a control system. While achieving precise control of the electric spindle, it ensures operation stability and safety. For example, by adjusting the input frequency or current of the electric motor through the control system, precise control of the spindle speed can be achieved. By collecting parameters such as the speed, temperature, and vibration of the electric spindle, and automatically adjusting the control signal according to the feedback data, the machining accuracy and stability can be maintained.

[0004] However, when the existing control systems cope with complex and changeable machining requirements, there are still problems of response delay and insufficient adjustment accuracy, resulting in the inability of the control system to meet the requirements for the reaction ability to input signal changes (such as load changes, command changes, etc.), and the machining stability of the electric spindle cannot be guaranteed. Although the existing PID algorithm can optimize the response speed, there will be overshoot and oscillation phenomena in this method, that is, after reaching the target value, there will still be fluctuations, resulting in the actual result exceeding the target value and then falling back, or there will be multiple up and down fluctuations when approaching the target value. These instabilities will affect the machining quality.

[0005] Therefore, it is necessary to provide a direct drive piezoelectric spindle and a control method therefor to solve the above problems.

[0006] It should be noted that the above information disclosed in this background art section is only used to understand the background art of the concept of this application, and therefore, it may include information that does not constitute the prior art. Summary of the Invention

[0007] Based on the above problems existing in the prior art, the problems to be solved by this application are: to provide a direct drive piezoelectric spindle and a control method therefor, which can apply existing linear processing methods by performing linear conversion on the operation process of the electric spindle, and reduce phenomena such as overshoot and oscillation during the adjustment process.

[0008] The technical solution adopted by this application to solve its technical problems is as follows: A control method for a direct-drive piezoelectric spindle, the direct-drive piezoelectric spindle includes an industrial control computer and a collection device. The industrial control computer is used to receive and analyze the data collected by the collection device, and output a control instruction after data analysis. The collection device includes a digital signal collector and a sensor combination. The sensor combination includes a rotational speed sensor, a current collector, and a torque sensor. The spindle is connected to an actuator, and the actuator is an inverter, which is used to adjust the rotational speed of the spindle according to the received control instruction;

[0009] The control method for the direct-drive piezoelectric spindle includes:

[0010] The industrial control computer receives the first operation signal collected by the collection device, extracts the dynamic characteristic signal of the spindle from the first operation signal, and models the extracted dynamic characteristic signal;

[0011] According to the load change of the spindle, adjust the parameters in the linear conversion process, and generate the corresponding control instruction;

[0012] Set the instruction structure of the spindle operation parameters, and output the control instruction according to the real-time load condition. After receiving the control instruction, the inverter adjusts the rotational speed of the spindle;

[0013] According to the historical data, predict the influence of the spindle load change on the operation parameters through a machine learning model, and optimize the control strategy.

[0014] In the implementation process of the technical solution of this application, by extracting the dynamic characteristic signal of the spindle and modeling the extracted dynamic characteristic signal, more accurate parameter prediction and control can be achieved, effectively reducing the response delay and insufficient accuracy caused by load change. At the same time, combined with the dynamic adjustment of the real-time load condition, the spindle maintains high stability and machining accuracy in a complex and changeable machining environment; in addition, through the introduction of the machine learning model, the control system can continuously learn and optimize, further reducing overshoot and oscillation phenomena, and improving machining efficiency.

[0015] Further, the dynamic characteristic signal includes moment of inertia, angular velocity, input torque, and frictional torque.

[0016] Further, the method for modeling the extracted dynamic characteristic signal includes:

[0017] Establish a dynamic response model, take the dynamic signal as the input of the dynamic response model, use a differential equation to model the dynamic characteristics of the spindle, and perform linear conversion;

[0018] According to the established differential equation, and obtain the initial angular velocity of the spindle, to get the change amount of the angular velocity with time;

[0019] Construct a linearized state feedback model, and use the input torque corresponding to the current angular velocity of the main shaft as the input of the linearized state feedback model to achieve linear conversion.

[0020] Furthermore, the process of dynamic characteristic modeling is realized by the differential equation method. The established differential equation is: J(dw / dt) = Ti - Tf, where t is the running time of the main shaft, J(dw / dt) represents the angular acceleration of the main shaft, dw / dt refers to the derivative of the angular velocity with respect to time, representing the angular velocity change rate. After multiplying by the moment of inertia J, the rotational speed corresponding to the torque required to change the main shaft is obtained.

[0021] Furthermore, Ti - Tf represents torque balance, that is, the difference between the applied input torque and the friction torque determines the angular acceleration of the main shaft. If Ti is greater than Tf, it means the main shaft is in an accelerating state. If Ti is less than Tf, it means the main shaft is in a decelerating state.

[0022] Furthermore, the calculation method for the change amount of the angular velocity over time is: w(t) = w(0) + tΔT / J, where t represents time, ΔT represents the net torque, ΔT = Ti - Tf, and ΔT / J represents the angular acceleration of the main shaft.

[0023] Furthermore, adjusting the parameters in the linear conversion process includes: first, preset a set of gain values, which are obtained through simulation; establish the mapping relationship between the gain and the load, and perform linear response using the transfer function; extract key load points using the interpolation method according to the load distribution; conduct simulation verification and further adjust the gain during the verification process.

[0024] Furthermore, the interpolation method is linear interpolation or polynomial interpolation.

[0025] Furthermore, set the gain and the angular velocity of the motorized spindle as the instruction types of the operating parameters of the motorized spindle.

[0026] A direct drive piezoelectric motorized spindle includes an industrial control computer, a collection device, and a control system. The control system includes: a dynamic feature extraction module, which is used for the industrial control computer to receive the first operating signal collected by the collection device, extract the dynamic features of the motorized spindle from the first operating signal, and model the extracted dynamic features;

[0027] A linear parameter adjustment module, which is used to adjust the parameters in the linear conversion process according to the load change of the main shaft and generate corresponding control instructions;

[0028] A control instruction output module, which is used to set the instruction structure of the operating parameters of the motorized spindle and output control instructions according to the real-time load condition;

[0029] A control strategy optimization module, which is used to predict the influence of the load change of the motorized spindle on the operating parameters according to historical data through a machine learning model, and optimize the control strategy.

[0030] The beneficial effects of this application are as follows: A direct-drive piezoelectric motorized spindle and control method provided by this application can extract the dynamic characteristic signals of the motorized spindle and model the extracted dynamic characteristic signals, so as to achieve more accurate parameter prediction and control, effectively reduce the response delay and insufficient accuracy caused by load changes. At the same time, combined with the dynamic adjustment of the real-time load situation, the motorized spindle can maintain high stability and machining accuracy in a complex and changeable machining environment; in addition, through the introduction of a machine learning model, the control system can continuously learn and optimize, further reducing overshoot and oscillation phenomena and improving machining efficiency.

[0031] In addition to the purposes, features and advantages described above, this application has other purposes, features and advantages. The following will refer to the drawings to further elaborate on this application in detail. Description of the Drawings

[0032] The schematic diagrams of the drawings forming a part of this application are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application.

[0033] In the drawings:

[0034] Figure 1 is a schematic diagram of the overall flow of a control method for a direct-drive piezoelectric motorized spindle in this application;

[0035] Figure 2 is a schematic diagram of the module composition of a control system for a direct-drive piezoelectric motorized spindle in this application. Detailed Embodiments

[0036] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine with the embodiments to elaborate on this application in detail.

[0037] In order to enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0038] Embodiment 1: The present application provides a direct-drive piezoelectric spindle and a control method. The direct-drive piezoelectric spindle includes an industrial control computer and a collection device. The industrial control computer is used to receive and analyze the data collected by the collection device, and output a control instruction after data analysis. The collection device includes a digital signal collector and a sensor combination. The sensor combination includes a rotational speed sensor, a current collector, and a torque sensor. The spindle is connected to an actuator, which is an inverter. The inverter is used to adjust the rotational speed of the spindle according to the received control instruction. The specific working principles and usage methods of each device can refer to the prior art and will not be described in detail in this embodiment;

[0039] The control method of the direct-drive piezoelectric spindle is applied to the operation process of the direct-drive piezoelectric spindle. Through processes such as data collection, data analysis, and signal adjustment and output, the operation process of the direct-drive piezoelectric spindle is adjusted, and problems such as response delay and insufficient adjustment accuracy are reduced during this process. As Figure 1 shown, the control method includes the following steps:

[0040] Step 10: The industrial control computer receives the first operation signal collected by the collection device, extracts the dynamic characteristics of the spindle from the first operation signal, and models the extracted dynamic characteristics.

[0041] In order to control the operation process of the direct-drive piezoelectric spindle and reduce the data usage, unnecessary data is eliminated. After the industrial control computer receives the first operation signal collected by the collection device, the dynamic characteristics of the spindle are extracted from it. The first operation signal includes the spindle speed signal collected by the rotational speed sensor and the torque signal collected by the torque sensor. The dynamic characteristics include factors such as rotational speed, torque, and friction. Among them, the collection device can be multiple digital signal collectors, various sensors, or other collection systems, such as rotational speed sensors, current collectors, and torque sensors, etc. There is no limitation in this embodiment as long as the operation signal of the spindle can be collected. The rotational speed sensor is used to detect the rotational speed information of the spindle in real time to obtain the speed signal. The current collector is used to collect the current data of the spindle, and then calculate the load change of the spindle according to the current data. The torque sensor is used to obtain the torque information of the spindle during operation to obtain signals such as the input torque and friction torque of the spindle;

[0042] Among them, there are various characteristic signals in the first operating signal. According to the characteristic type, they can be divided into dynamic characteristic signals and static characteristic signals. Among them, the dynamic characteristic signals include moment of inertia, angular velocity, input torque, friction torque, etc. Among them, the moment of inertia represents the resistance of the main shaft to rotation, the angular velocity is the speed of the main shaft rotation, with the unit of radians per second, the input torque is the torque applied to the main shaft to drive the main shaft to rotate, and the friction torque is the torque generated by the frictional force, which will generate a resistance to the rotation of the main shaft. In this embodiment, the above four dynamic signals are used as modeling parameters, and then motion modeling is carried out through differential equations. Specifically, the method for modeling the extracted dynamic characteristic signals includes:

[0043] Step 101: Establish a dynamic response model, and use the dynamic signal as the input of the dynamic response model. Use differential equations to model the dynamic characteristics of the motorized spindle and perform linear transformation;

[0044] In order to obtain the accurate dynamic characteristics of the motorized spindle for subsequent analysis, dynamic characteristic modeling is required. The process of dynamic characteristic modeling is realized by using the differential equation method because the differential equation method can precisely describe the motion law of the motorized spindle. Specifically, taking the dynamic signals as the moment of inertia J, angular velocity w, input torque Ti, and friction torque Tf as examples, the established differential equation is:

[0045] J(dw / dt) = Ti - Tf. This equation describes the dynamic behavior of the motorized spindle during operation. Among them, t is the operating time of the main shaft, J(dw / dt) represents the angular acceleration of the main shaft, dw / dt refers to the derivative of the angular velocity with respect to time, representing the angular velocity change rate. After multiplying by the moment of inertia J, the corresponding rotational speed under the torque required to change the main shaft is obtained;

[0046] Ti - Tf represents torque balance, that is, the difference between the applied input torque and the friction torque determines the angular acceleration of the main shaft. If Ti is greater than Tf, it means the main shaft is in an accelerating state. On the contrary, if Ti is less than Tf, it means the main shaft is in a decelerating state. By establishing a differential equation, it is more convenient to perform dynamic characteristic analysis of the main shaft and torque balance, so as to precisely control and optimize the motorized spindle; in addition, this model can also be used to predict the dynamic response of the main shaft under different working conditions, which is convenient for the maintenance of the motorized spindle;

[0047] Step 102: According to the established differential equation, and obtain the initial angular velocity of the motorized spindle to get the change amount of the angular velocity over time;

[0048] After establishing the differential equation, since the differential equation contains the time component t, it is only necessary to solve the differential equation. Combining the initial angular velocity w(0) of the motorized spindle, the change in the angular velocity of the spindle over time w(t) can be obtained. Specifically, w(t) = w(0) + tΔT / J, where t represents time, ΔT represents the net torque, that is, Ti - Tf, and ΔT / J represents the angular acceleration of the spindle. After integrating with respect to time, the variation law of the spindle angular velocity over time can be obtained, and then the change in the angular velocity of the spindle at different moments can be accurately obtained, providing a basis for subsequent speed regulation;

[0049] Step 103: Construct a linearized state feedback model and use the input torque corresponding to the current angular velocity of the spindle as the input of the linearized state feedback model to achieve linear conversion;

[0050] In practical applications, the dynamic data of the spindle and the dynamic response model established are non-linear. The non-linear dynamic response model will increase the complexity and uncertainty of the system during control. Therefore, it is crucial to convert the non-linear system to a linear system according to the known conditions. After converting to a linear model, common linear control techniques in the existing technology can be applied, thereby improving the control accuracy and reducing problems such as control overshoot and oscillation;

[0051] In this embodiment, by constructing a linearized state feedback model and using the input torque corresponding to the current angular velocity of the spindle as the input of the linearized state feedback model, since the variation law of the spindle angular velocity over time and the relationship between the change in the spindle angular velocity and the input torque and the frictional torque have been obtained in the previous steps, when the spindle is running normally, the input torque corresponding to the current angular velocity of the spindle can be obtained by using the conversion between formulas and input into the linearized state feedback model, so that the dynamic behavior of the entire spindle control system can be linearly processed based on this input torque. In this way, through linearization, not only the control algorithm is simplified, but also the computational burden during the control process is reduced, the response speed and stability of the control system are improved, high-precision control of the spindle is achieved, and linear control techniques in the existing technology can be applied;

[0052] In addition, the model also takes into account various disturbances that may occur during actual operation. By adjusting the input torque in real time, the influence brought by these disturbances can be effectively compensated. This process enhances the robustness of the control system, ensures that the motorized spindle can maintain excellent performance under different working conditions, and improves the machining accuracy and production efficiency;

[0053] Among them, the selection of the linearization model can refer to the prior art. For example, state feedback can be selected, and by setting the corresponding feedback gain, the spindle control system can be converted into a standard linear system form, which facilitates the further realization of fine regulation of the spindle speed through the optimization and adjustment of the linearization model, ensuring the high precision and high stability required during the machining process.

[0054] Step 20: Adjust the parameters in the linear conversion process according to the load change of the spindle, and generate corresponding control instructions;

[0055] During the operation of the spindle, it will have different operating states, thus generating different load changes. For example, in the case of idling, the load of the spindle is small, while during heavy-duty machining, the load increases significantly. When the load changes, the linear conversion in the foregoing process will also be affected. Because the linear conversion process needs to obtain the input torque at the current spindle speed and combine the corresponding feedback gain to achieve linear conversion, and when the load changes, the input torque will also change simultaneously. Therefore, it is necessary to dynamically adjust the input torque and feedback gain according to the load change of the spindle to ensure the accuracy of the linear conversion to meet the control requirements under different working conditions;

[0056] In this embodiment, the motorized spindle is connected to an actuator, and the actuator is an inverter, which is used to adjust the speed of the motorized spindle according to the received control instructions;

[0057] When adjusting the parameters in the linear conversion process, it is first necessary to collect the real-time load data of the spindle. Through real-time data analysis, the input torque and feedback gain are accurately adjusted to maintain the accuracy of the linear conversion, forming continuous optimization of the control system, ensuring high-efficiency and stable control performance under various working conditions, so that the control system can adapt to the load change of the spindle in real time and maintain the accuracy of the linear conversion. Specifically, adjusting the parameters in the linear conversion process includes:

[0058] First, preset a set of gain values, which are obtained through simulation; in the foregoing linear conversion process, a set of gain values need to be set first, and this set of gain values is used as the preset value and adjusted according to the load change in the subsequent process;

[0059] A mapping relationship between gain and load is established, and a transfer function is used for linear response. After a set of gain values ​​are preset, since the load will change continuously, the gain in the process of establishing the linear relationship will change accordingly, otherwise the control system may deviate from the preset linear model. Therefore, it is necessary to establish a mapping relationship between gain and load, and use a transfer function for linear response. The transfer function is a method used in linear power systems. The mapping relationship is obtained by performing Laplace transform on the input and output. In this embodiment, the load is the input and the gain is the output, so as to ensure that the control system can quickly and accurately adjust the parameters when facing the diversity of the spindle load. The use principle and calculation formula of the transfer function can refer to the existing technology and will not be described in detail in this embodiment.

[0060] According to the distribution of the load, the interpolation method is used to extract the key load points; by extracting these key load points, the linear conversion parameters are optimized, because in actual applications, the distribution of load values ​​is not necessarily uniform. In the case of uneven distribution, the calculated gain will fluctuate greatly, and after being converted into a control signal, oscillation is likely to occur. Therefore, the interpolation method is used to extract the key load points, thereby calculating the gain under the key load points and reducing the generation of oscillations. The interpolation method can be implemented by linear interpolation or polynomial interpolation. For details, please refer to the prior art;

[0061] Perform simulation verification and further adjust the gain during the verification process; after obtaining the gain value, simulation verification is still required to observe the impact of the gain value on the subsequent linear change process, and determine whether overshoot, oscillation and other problems occur. If so, fine-tune the gain to meet the requirements and ensure that the system can maintain good performance under various working conditions.

[0062] Step 30: Set the command structure of the electric spindle operating parameters and output control commands according to the real-time load conditions;

[0063] After completing the foregoing steps, it is also necessary to set the instruction structure of the electric spindle operating parameters. The instruction structure is a structure of control instructions for machine recognition. The instruction structure usually includes parameters such as instruction type, parameter name, parameter value, check value, end symbol, etc. After setting the instruction structure, the computer or industrial control computer can directly recognize it without conversion, improving the data processing efficiency. In this embodiment, the instruction type of setting the gain and the angular velocity of the electric spindle as the electric spindle operating parameters is used. Since the final control is the rotational speed of the electric spindle, other operating parameters do not need to be considered, and the gain and angular velocity are dynamically adjusted according to the real-time load feedback, and the corresponding control instructions are output, so as to accurately control the rotational speed of the electric spindle. And because linear conversion has been done and the gain selection in the linear conversion process, the probability of situations such as oscillation and overshoot will be reduced, further improving the operating stability of the electric spindle;

[0064] Step 40: According to historical data, predict the impact of the electric spindle load change on the operating parameters through a machine learning model, and optimize the control strategy. Through in-depth analysis of historical data, the machine learning model can learn the relationship between load changes and operating parameters, and achieve intelligent control of the spindle. Under the guidance of prediction, the control system will be able to more intelligently adapt to complex and changeable working conditions, ensuring the high efficiency and stability of the electric spindle operation. Specifically, the use of a machine learning model to predict the impact of data changes can refer to the prior art and will not be elaborated in this embodiment.

[0065] Embodiment 2: As Figure 2 shown, the present application proposes a direct drive piezoelectric electric spindle, including an industrial control computer, a collection device, and a control system. The control system is used to implement the control method in Embodiment 1. The control system includes:

[0066] The control system includes:

[0067] A dynamic feature extraction module, which is used for the industrial control computer to receive the first operation signal collected by the collection device, extract the dynamic features of the electric spindle from the first operation signal, and model the extracted dynamic features;

[0068] A linear parameter adjustment module, which is used to adjust the parameters in the linear conversion process according to the load change of the spindle and generate corresponding control instructions;

[0069] A control instruction output module, which is used to set the instruction structure of the electric spindle operating parameters and output control instructions according to the real-time load situation;

[0070] A control strategy optimization module, which is used to predict the impact of the electric spindle load change on the operating parameters through a machine learning model according to historical data and optimize the control strategy.

[0071] The above are only the preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various modifications and variations can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A control method for a direct-drive hydrostatic electric spindle, characterized in that: The direct-drive static pressure electric spindle includes an industrial computer and a collection device, the industrial computer is used to receive and analyze data collected from the collection device, and output control instructions after data analysis, the collection device includes a digital signal acquisition instrument and a sensor combination, the sensor combination includes a speed sensor, a current acquisition instrument and a torque sensor, the electric spindle is connected to an actuator, the actuator is a frequency converter, and the frequency converter is used to adjust the speed of the electric spindle according to the received control instructions; The control method of the direct-drive hydrostatic electric spindle comprises: The industrial computer receives a first operation signal collected by the collection device, wherein the first operation signal includes a spindle speed signal collected by a speed sensor and a torque signal collected by a torque sensor, extracts a dynamic characteristic signal of the spindle from the first operation signal, and models the extracted dynamic characteristic signal; The current acquisition instrument generates a load change signal of the main shaft, and the industrial computer adjusts the parameters in the linear conversion process according to the load change signal of the main shaft, and generates corresponding control instructions; The industrial computer sets the instruction structure of the electric spindle operating parameters and outputs control instructions according to the real-time load conditions. After receiving the control instructions, the frequency converter adjusts the speed of the electric spindle. Based on historical data, the industrial computer uses a machine learning model to predict the impact of the electric spindle load changes on the operating parameters and optimize the control strategy.

2. The control method of a direct-drive hydrostatic electric spindle according to claim 1, characterized in that: The dynamic characteristic signals include moment of inertia, angular velocity, input torque and friction torque.

3. The control method of a direct-drive hydrostatic electric spindle according to claim 2, characterized in that: Methods for modeling the extracted dynamic feature signals include: A dynamic response model is established, and the dynamic signal is used as the input of the dynamic response model. The dynamic characteristics of the electric spindle are modeled using differential equations and linearly transformed. According to the established differential equation, the initial angular velocity of the electric spindle is obtained to obtain the change of the angular velocity with time; A linearized state feedback model is constructed, and the input torque corresponding to the current angular velocity of the spindle is used as the input of the linearized state feedback model to achieve linear conversion.

4. The control method of a direct-drive hydrostatic electric spindle according to claim 3, characterized in that: The process of dynamic characteristic modeling is implemented by differential equation method. The established differential equation is: J (dw / dt) = Ti-Tf, where t is the running time of the spindle, J (dw / dt) represents the angular acceleration of the spindle, and dw / dt refers to the derivative of the angular velocity with respect to time, representing the rate of change of the angular velocity. After multiplying it with the moment of inertia J, the corresponding speed under the need to change the spindle torque is obtained; The Ti-Tf represents torque balance, that is, the difference between the applied input torque and the friction torque determines the angular acceleration of the spindle. If Ti is greater than Tf, it means that the spindle is in an accelerating state, and if Ti is less than Tf, it means that the spindle is in a decelerating state.

5. The control method of a direct-drive hydrostatic electric spindle according to claim 3, characterized in that: The calculation method of the change of the angular velocity over time is: w(t)=w(0)+tΔT / J, where t represents time, ΔT represents net torque, ΔT=Ti-Tf, and ΔT / J represents the angular acceleration of the main shaft.

6. The control method of a direct-drive hydrostatic electric spindle according to claim 1, characterized in that: Adjusting the parameters in the linear conversion process includes: first presetting a set of gain values, which are obtained through simulation; establishing a mapping relationship between gain and load, and using transfer function for linear response; according to the distribution of load, using interpolation method to extract key load points; performing simulation verification, and further adjusting the gain during the verification process.

7. The control method of a direct-drive hydrostatic electric spindle according to claim 6, characterized in that: The interpolation method is linear interpolation or polynomial interpolation.

8. The control method of a direct-drive hydrostatic electric spindle according to claim 1, characterized in that: The command type for setting the gain and the angular velocity of the electric spindle as the operating parameters of the electric spindle.

9. A direct drive hydrostatic electric spindle, characterized in that: It includes an industrial computer, a collection device and a control system, and the control system is used to implement the control method according to any one of claims 1 to 8, and the control system includes: A dynamic feature extraction module is used for the industrial computer to receive the first operation signal collected by the collection device, extract the dynamic features of the electric spindle from the first operation signal, and model the extracted dynamic features; The linear parameter adjustment module is used to adjust the parameters in the linear conversion process according to the load change of the spindle and generate corresponding control instructions; The control command output module is used to set the command structure of the electric spindle operating parameters and output the control command according to the real-time load conditions; The control strategy optimization module is used to predict the impact of the electric spindle load change on the operating parameters through a machine learning model based on historical data and optimize the control strategy.

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