Cold header servo motor feeding control method and system

Abstract

The invention discloses a cold header servo motor feeding control method and system, and relates to the field of control systems.The method comprises the steps that a man-machine interface routes a feeding control request to a PLC; the PLC is in communication connection with a driver of a servo motor of the cold header; the PLC analyzes the feeding control request to obtain to-be-executed feeding control logic, and generates a control instruction based on the feeding control logic; the PLC compares the actual parameter with a preset template parameter, and optimizes the actual parameter under the condition that there is a difference, thereby obtaining an equipment optimization parameter; the PLC sends the control instruction and the equipment optimization parameters to a driver of a servo motor of the cold header; the PLC monitors the operation state of the servo motor of the cold header in real time and displays the operation state on the human-computer interface; and the PLC optimizes the control performance of the servo motor of the cold header based on the operation state. The control precision of the cold header control system can be effectively improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of control systems, and in particular to a feed control method and system for a servo motor of a cold heading machine. Background Art

[0002] Cold heading machine is a kind of equipment widely used in the field of metal processing, mainly used to manufacture standard parts such as bolts, nuts, screws, etc. The cold heading process processes metal wire into the desired shape and size through plastic deformation in a cold state. Due to the advantages of high efficiency, energy saving, and high material utilization rate, the cold heading process has been widely used in the automotive, aerospace, construction and other industries. However, with the development of industrial automation and intelligent manufacturing, higher requirements are placed on the control accuracy of cold heading machines. High-precision control can not only improve product quality, but also reduce material waste and production costs.

[0003] In traditional cold heading machine control systems, hydraulic or mechanical transmission methods are usually used to achieve feed control. Although these systems have simple structures and low costs, they have obvious deficiencies in control accuracy and response speed. Specifically, the cold heading machine control system in the prior art has the following problems: 1. Traditional cold heading machine control systems mostly use simple PID control algorithms, which are difficult to cope with complex nonlinear systems and rapidly changing process requirements. During the cold heading forming process, factors such as material deformation and temperature changes will cause dynamic changes in system parameters, and simple PID control is difficult to achieve high-precision control. 2. The parameter adjustment of traditional control systems mostly relies on manual experience, which is difficult to adapt to the requirements of different materials and processes. The parameter optimization process is time-consuming and it is difficult to ensure optimality, which makes it difficult to improve control accuracy.

[0004] In summary, the control accuracy of the cold heading machine control system in the prior art is low, and it is difficult to meet the requirements of modern industrial production for high precision, high efficiency and intelligence. Summary of the invention

[0005] The embodiments of the present application provide a cold heading machine servo motor feed control method and system for effectively improving the control accuracy of the cold heading machine control system.

[0006] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0007] In a first aspect, a cold heading machine servo motor feed control method is provided, which is applied to a feed control system, wherein the feed control system comprises a cold heading machine servo motor, a controller PLC and a human-machine interface, and the method comprises:

[0008] In response to receiving a feed control request sent by a user through the human-machine interface, the human-machine interface routes the feed control request to the controller PLC;

[0009] The controller PLC establishes a communication connection with the driver of the cold heading machine servo motor through industrial Ethernet to identify the type of communication protocol supported by the driver of the cold heading machine servo motor and obtain actual parameters of the cold heading machine servo motor;

[0010] The controller PLC analyzes the feed control request, obtains the feed control logic to be executed, and generates a control instruction based on the feed control logic;

[0011] The controller PLC compares the actual parameters with the preset template parameters, and in the case of differences, optimizes the actual parameters using a parameter optimization strategy to obtain device optimization parameters, wherein the parameter optimization strategy includes a parameter adaptive adjustment strategy based on a motor dynamic model and a machine learning optimization strategy based on historical operation data;

[0012] The controller PLC sends the control instruction and the equipment optimization parameter to the driver of the cold heading machine servo motor through the industrial Ethernet, the control instruction is used to control the speed and position of the cold heading machine servo motor, and the driver is used to configure the parameters of the cold heading machine servo motor according to the equipment optimization parameter;

[0013] The controller PLC monitors the operating status of the cold heading machine servo motor in real time and displays the operating status on the human-machine interface;

[0014] The controller PLC optimizes the control performance of the cold heading machine servo motor based on the operating status.

[0015] In a possible implementation manner of the first aspect, the parameter adaptive adjustment strategy based on the motor dynamic model includes:

[0016] According to the pre-built motor dynamic model and the actual parameters, the first optimal control parameters of the cold heading machine servo motor are calculated in real time, wherein the first optimal control parameters include proportional gain, integral gain and differential gain, the motor dynamic model is constructed based on the template parameters, and the motor dynamic model includes a motor torque equation and a motor motion equation;

[0017] The machine learning optimization strategy based on historical operation data includes:

[0018] Inputting the actual parameters into a pre-trained optimization model to predict a second optimal control parameter, wherein the optimization model is trained based on historical operating data of the cold heading machine servo motor;

[0019] The first optimal control parameter and the second optimal control parameter are integrated to obtain equipment optimization parameters.

[0020] In another possible implementation manner of the first aspect, the integrating the first optimal control parameter and the second optimal control parameter to obtain a device optimization parameter includes:

[0021] Acquire the real-time load torque of the servo motor of the cold heading machine, and calculate the average load torque within a preset time period according to the real-time load torque;

[0022] According to a preset weight coefficient, weighted summation is performed on the first optimal control parameter and the second optimal control parameter to obtain an equipment optimization parameter;

[0023] The weight coefficient is obtained by dynamically adjusting the current load torque of the cold heading machine servo motor and the average load torque using a preset adjustment formula, and the adjustment formula includes:

[0024] w2=1-w1;

[0025] Wherein, w1 is the weight coefficient of the first optimal control parameter, w2 is the weight coefficient of the second optimal control parameter, T L is the current load torque, T La is the average load torque, and α is the adjustment factor.

[0026] In another possible implementation of the first aspect, the controller PLC establishes a communication connection with a driver of the cold heading machine servo motor through industrial Ethernet, including:

[0027] The controller PLC sequentially attempts to establish a communication connection with the driver of the cold heading machine servo motor using different communication protocols, wherein the communication protocols include ModbusTCP, EtherCAT, PROFINET and EtherNet / IP;

[0028] When the controller PLC is successfully connected to the driver of the cold heading machine servo motor, determining the type of communication protocol supported by the driver, and loading a corresponding target communication driver based on the communication protocol type;

[0029] The controller PLC establishes a communication connection with the driver of the cold heading machine servo motor based on the target communication driver and the industrial Ethernet.

[0030] In another possible implementation of the first aspect, the controller PLC establishes a communication connection with a driver of the cold heading machine servo motor based on the target communication driver and the industrial Ethernet, including:

[0031] The controller PLC initializes the communication parameters of the industrial Ethernet through the target communication driver, and the communication parameters include IP address, port number and communication rate;

[0032] The controller PLC sends a handshake signal to the driver;

[0033] If the handshake is successful, the controller PLC establishes a communication connection with the driver.

[0034] In another possible implementation manner of the first aspect, the controller PLC parses the feed control request, obtains the feed control logic to be executed, and generates a control instruction based on the feed control logic, including:

[0035] The controller PLC analyzes the feed control request to obtain the feed control logic to be executed;

[0036] The controller PLC generates a feed speed curve and position control instructions according to the feed control logic;

[0037] The controller PLC determines the optimal speed and optimal position of the cold heading machine servo motor according to the feed speed curve and the position control instruction, and generates a control instruction based on the optimal speed and the optimal position.

[0038] In another possible implementation of the first aspect, the controller PLC determines the optimal speed and optimal position of the cold heading machine servo motor according to the feed speed curve and the position control instruction, and generates a control instruction based on the optimal speed and the optimal position, including:

[0039] The controller PLC analyzes the optimal position and the maximum allowed acceleration according to the position control instruction;

[0040] The controller PLC obtains the current position of the cold heading machine servo motor, and calculates the position error according to the optimal position and the current position, wherein the position error is the difference between the current position and the optimal position;

[0041] The controller PLC calculates the optimal feed speed according to the position error and the maximum acceleration allowed;

[0042] The controller PLC compares the optimal feed speed with the feed speed curve, and if the optimal feed speed exceeds the allowable range of the feed speed curve, adjusts the optimal feed speed to the maximum allowable speed in the allowable range;

[0043] The controller PLC obtains the feed wheel radius of the cold heading machine servo motor;

[0044] The controller PLC calculates the optimal rotation speed according to the optimal feed speed and the feed wheel radius;

[0045] The controller PLC generates a control instruction based on the optimal rotation speed and the optimal position, and the control instruction includes a rotation speed control instruction and a position control instruction.

[0046] In another possible implementation of the first aspect, the control performance of the cold heading machine servo motor includes a feed speed curve, the operating state includes load torque, motor output torque, current data, temperature data and vibration frequency data, and the controller PLC optimizes the control performance of the cold heading machine servo motor based on the operating state, including:

[0047] The controller PLC uses a load fluctuation calculation formula to calculate the load fluctuation of the cold heading machine servo motor according to the load torque and the motor output torque;

[0048] The controller PLC uses a health index calculation formula to calculate the health index of the cold heading machine servo motor according to the current data, the temperature data and the vibration frequency data;

[0049] The controller PLC dynamically adjusts the control instructions of the cold heading machine servo motor based on the health index and the load fluctuation amount to compensate for the load fluctuation and optimize the feed speed curve.

[0050] In another possible implementation of the first aspect, the controller PLC dynamically adjusts the control instructions of the cold heading machine servo motor based on the health index and the load fluctuation amount to compensate for the load fluctuation and optimize the feed speed curve, including:

[0051] The controller PLC adjusts the output torque of the cold heading machine servo motor according to the load fluctuation amount and a preset output torque adjustment formula;

[0052] The controller PLC adjusts the feed speed curve according to the health index and a preset feed speed adjustment formula;

[0053] The control instruction of the cold heading machine servo motor is dynamically adjusted according to the output torque and the feed speed curve.

[0054] In a second aspect, the present application provides a feed control system, comprising:

[0055] A human-machine interface, configured to route a feed control request to a controller PLC in response to receiving a feed control request sent by a user through the human-machine interface;

[0056] Servo motor for cold heading machine;

[0057] A controller PLC is connected to the human-machine interface and to the driver of the cold heading machine servo motor, and is used to perform:

[0058] Establishing a communication connection with the driver of the cold heading machine servo motor through industrial Ethernet to identify the type of communication protocol supported by the driver of the cold heading machine servo motor and to obtain actual parameters of the cold heading machine servo motor, wherein the actual parameters include rated speed, rated torque and encoder resolution, motor inertia and load characteristics;

[0059] Parsing the feed control request to obtain the feed control logic to be executed, and generating a control instruction based on the feed control logic;

[0060] The actual parameters are compared with the preset template parameters, and if there are differences, the actual parameters are optimized using a parameter optimization strategy to obtain device optimization parameters, wherein the parameter optimization strategy includes a parameter adaptive adjustment strategy based on a motor dynamic model and a machine learning optimization strategy based on historical operation data;

[0061] The control instruction and the device optimization parameter are sent to the driver of the cold heading machine servo motor through the industrial Ethernet, wherein the control instruction is used to control the speed and position of the cold heading machine servo motor, and the cold heading machine is used to configure the parameters of the cold heading machine servo motor according to the device optimization parameter;

[0062] Monitor the operating status of the cold heading machine servo motor in real time and display the operating status on the human-machine interface;

[0063] Based on the operating status, the control performance of the cold heading machine servo motor is optimized.

[0064] Through the above technical solutions, firstly, servo motors are used to replace traditional hydraulic or mechanical transmission methods, which greatly improves the accuracy and response speed of feed control. Servo motors have the characteristics of high precision and high dynamic response, and can achieve fast and accurate position and speed control, meeting the requirements of modern industrial production for high-precision processing. Secondly, industrial Ethernet communication technology is introduced to achieve high-speed and reliable communication between the controller PLC and the servo motor driver. By identifying the type of communication protocol supported by the driver, it can flexibly adapt to servo motors of different brands and models, improving the compatibility and scalability of the feed control system. By introducing parameter optimization strategies, including parameter adaptive adjustment strategies based on motor dynamic models and machine learning optimization strategies based on historical operation data, the problem that traditional PID control algorithms are difficult to cope with complex nonlinear systems and rapidly changing process requirements is effectively solved. The parameter adaptive adjustment strategy based on the motor dynamic model can adjust the control parameters in real time to adapt to the dynamic changes of system parameters, such as changes in system characteristics caused by factors such as material deformation and temperature changes. The machine learning optimization strategy based on historical operation data can continuously optimize the control parameters by analyzing a large amount of historical data, achieve continuous improvement of system performance, greatly reduce the workload of manual parameter adjustment, improve the efficiency and accuracy of parameter optimization, and enable the system to quickly adapt to the requirements of different materials and processes. In addition, the controller PLC monitors the operating status of the servo motor in real time and displays the status information on the human-machine interface, so that the operator can understand the operation of the equipment in a timely manner. Based on the monitoring data, the system can also continuously optimize the control performance of the servo motor to achieve continuous improvement of equipment performance. This closed-loop optimization mechanism ensures that the system can always maintain the best working state, improves production efficiency and product quality. In general, this technical solution comprehensively improves the control accuracy, response speed and adaptability of the cold heading machine through advanced control technology and intelligent optimization strategies. By introducing servo motors and advanced control strategies, the problems of low accuracy, slow response and poor adaptability of the traditional cold heading machine control system are effectively solved, and the overall performance and intelligence level of the cold heading machine are significantly improved.

[0065] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic flow chart of a cold heading machine servo motor feed control method provided in an embodiment of the present application;

[0067] Figure 2 A schematic diagram of a communication connection process between a controller PLC and a driver provided in an embodiment of the present application;

[0068] Figure 3 A schematic diagram of the structure of a feed control system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0070] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0071] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0072] Figure 1 The following schematically shows a flow chart of a method for controlling the feeding of a servo motor of a cold heading machine according to an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a cold heading machine servo motor feed control method, which is applied to a feed control system. The feed control system includes a cold heading machine servo motor, a controller PLC and a human-machine interface. The method may include the following steps.

[0073] S110, in response to receiving a feed control request sent by a user through the human-machine interface, the human-machine interface routes the feed control request to the controller PLC;

[0074] S120, the controller PLC establishes a communication connection with the driver of the cold heading machine servo motor through the industrial Ethernet to identify the communication protocol type supported by the driver of the cold heading machine servo motor and obtain the actual parameters of the cold heading machine servo motor;

[0075] S130, the controller PLC analyzes the feed control request, obtains the feed control logic to be executed, and generates a control instruction based on the feed control logic;

[0076] S140, the controller PLC compares the actual parameters with the preset template parameters, and if there are differences, optimizes the actual parameters using a parameter optimization strategy to obtain device optimization parameters, wherein the parameter optimization strategy includes a parameter adaptive adjustment strategy based on a motor dynamic model and a machine learning optimization strategy based on historical operation data;

[0077] S150, the controller PLC sends the control instructions and equipment optimization parameters to the driver of the cold heading machine servo motor through the industrial Ethernet. The control instructions are used to control the speed and position of the cold heading machine servo motor, and the driver is used to configure the parameters of the cold heading machine servo motor according to the equipment optimization parameters;

[0078] S160, controller PLC monitors the operating status of the cold heading machine servo motor in real time and displays the operating status on the human-machine interface;

[0079] S170, controller PLC optimizes the control performance of the cold heading machine servo motor based on the operating status.

[0080] In the servo motor feed control system of the cold heading machine, the user sending a feed control request through the human-machine interface (HMI) is the first step to start the entire control process. The human-machine interface is usually a touch screen or an operation panel, through which the user can enter control instructions, such as setting the feed speed, feed length, or selecting a specific processing mode. When the user completes the input and confirms it, the human-machine interface generates a feed control request, which contains all the parameters and instructions set by the user. In order to ensure that the request can be accurately transmitted to the controller PLC, the human-machine interface will route the request to the PLC through a preset communication protocol (such as Modbus TCP or EtherNet / IP). The human-machine interface converts the parameters entered by the user into a data format that can be recognized by the PLC and sends it to the specified port of the PLC via industrial Ethernet. After receiving the request, the PLC will perform a preliminary data check to ensure the integrity and legality of the request. If the request format is correct and the parameters are within the allowable range, the PLC will enter the next processing flow.

[0081] After receiving the feed control request, the controller PLC needs to establish a communication connection with the driver of the cold heading machine servo motor to obtain the actual parameters of the motor and identify the type of communication protocol supported by the driver. This process first involves the automatic identification of the communication protocol. The PLC will try to use multiple communication protocols (such as Modbus TCP, EtherCAT, PROFINET, and EtherNet / IP) to communicate with the driver through industrial Ethernet. Each protocol has its own specific handshake signal and data packet format. The PLC will send a test signal and wait for the driver's response. If the driver responds successfully, the PLC will record the protocol type and load the corresponding communication driver.

[0082] Next, the PLC will send a parameter reading instruction to the driver through the established communication connection to obtain the actual parameters of the motor, including the current speed, position, torque, temperature, etc. These parameters are real-time data and reflect the current operating status of the motor. In order to ensure the accuracy of the data, the PLC will read and verify the data multiple times to eliminate abnormal values. The actual parameters obtained will be used for subsequent control instruction generation and parameter optimization. Finally, efficient communication between the PLC and the driver is achieved, ensuring the real-time and accuracy of the control system. By automatically identifying the communication protocol, it can adapt to different models of drivers, improving the compatibility and flexibility of the system.

[0083] After obtaining the actual parameters of the cold heading machine servo motor, the controller PLC needs to parse the feed control request sent by the user through the human-machine interface and generate specific control instructions. The parsing process first involves decoding and classifying the request data. The PLC will extract the parameters in the request (such as feed speed, feed length, etc.) and convert them into specific control tasks according to the preset control logic. For example, if the user sets the feed length to 100 mm, the PLC will calculate the distance to be moved based on the current position and target position of the motor, and combine the maximum acceleration and speed limit of the motor to generate a smooth feed speed curve. This curve describes the change of speed over time as the motor moves from the current position to the target position. In order to ensure the accuracy of the control, the PLC will also consider the dynamic characteristics of the motor, such as inertia and load changes, and optimize the speed curve.

[0084] Next, the PLC will generate specific control instructions based on the feed speed curve, including speed control instructions and position control instructions. These instructions are sent to the driver via industrial Ethernet in the form of digital signals, driving the motor to run at the predetermined speed and position. Ultimately, the precise conversion of user instructions to specific control instructions is achieved, ensuring the smoothness and accuracy of motor operation. By generating an optimized speed curve, the system can reduce the start-stop impact of the motor, extend the life of the equipment, and improve processing accuracy.

[0085] Before generating control instructions, the controller PLC will compare the actual parameters obtained with the preset template parameters to determine whether optimization is needed. Template parameters are pre-set ideal parameters that reflect the operating characteristics of the motor in the best state. The comparison process involves the comparison of multiple parameters, including speed, torque, temperature, etc. If there is a difference between the actual parameters and the template parameters, the PLC will start the parameter optimization strategy. The parameter optimization strategy includes two main methods: parameter adaptive adjustment strategy based on the motor dynamic model and machine learning optimization strategy based on historical operation data. The strategy based on the motor dynamic model calculates the optimal control parameters such as proportional gain, integral gain and differential gain in real time through the pre-built motor dynamic model (including the motor torque equation and the motor motion equation). These parameters are used to adjust the control performance of the motor to make it closer to the ideal state. The machine learning optimization strategy based on historical operation data uses a pre-trained optimization model to predict the optimal control parameters based on the historical operation data of the motor. The optimization model is trained with a large amount of historical data and can capture the nonlinear characteristics and complex dynamic behaviors of the motor. The PLC will integrate the optimal control parameters obtained by the two strategies to generate the equipment optimization parameters. During the integration process, the PLC will dynamically adjust the weight coefficient according to the current load torque and average load torque of the motor to ensure the adaptability and stability of the optimization parameters. By dynamically adjusting the control parameters, the system can adapt to different working conditions and load changes, ensuring that the motor is always in the best operating state.

[0086] After generating the control instructions and equipment optimization parameters, the controller PLC needs to send these data to the driver of the cold heading machine servo motor through industrial Ethernet. The sending process first involves the encapsulation and transmission of data. The PLC will package the control instructions and optimization parameters into a data packet that conforms to the communication protocol and send it to the designated port of the driver through industrial Ethernet. The data packet contains speed control instructions, position control instructions, and optimized control parameters (such as proportional gain, integral gain, and differential gain). After receiving the data packet, the driver will decode and verify to ensure the integrity and correctness of the data. Next, the driver will configure the motor parameters according to the optimization parameters and adjust the motor control algorithm so that it runs according to the optimized parameters. At the same time, the driver will control the speed and position of the motor according to the control instructions to ensure that the motor runs at the predetermined speed and position, and finally realize the accurate transmission and execution of the control instructions and optimization parameters, ensure the accuracy and stability of the motor operation, and improve the processing accuracy and production efficiency.

[0087] During the operation of the motor, the controller PLC needs to monitor its operating status in real time to ensure the stability and safety of the system. The monitoring process involves real-time acquisition and analysis of multiple parameters, including the motor's speed, position, torque, current, temperature, and vibration frequency. The PLC obtains this data from the driver through industrial Ethernet and processes and analyzes it in real time. For example, the PLC calculates the load fluctuation of the motor, evaluates the health status of the motor, and determines whether there is an abnormality based on the preset threshold. If an abnormality is detected (such as excessive temperature or abnormal vibration frequency), the PLC will immediately issue an alarm and take corresponding protective measures. At the same time, the PLC will display the operating status of the motor on the human-machine interface in real time for the operator to view. The display content includes the current speed, position, load status, health index, etc. of the motor. The operator can understand the operation of the motor in real time through the human-machine interface and make adjustments as needed. The implementation effect of this step is to realize real-time monitoring and visualization of the motor's operating status, and improve the safety and operability of the system. Through real-time monitoring, potential problems can be discovered and handled in a timely manner to avoid equipment damage and production interruptions.

[0088] Based on real-time monitoring of the motor's operating status, the controller PLC will further optimize the motor's control performance. The optimization process involves multiple aspects, including compensation for load fluctuations, adjustment of speed curves, and dynamic optimization of control parameters. First, the PLC will dynamically adjust the control instructions according to the motor's load fluctuation amount and health index to compensate for the impact of load fluctuations on the motor's operation. For example, if a large load fluctuation is detected, the PLC will increase the motor's output torque to ensure the stability of the speed and position. Secondly, the PLC will adjust the feed speed curve according to the motor's health status to avoid the motor running in an abnormal state. For example, if the temperature of the motor is too high, the PLC will reduce the feed speed and reduce the motor's load. Finally, the PLC will dynamically optimize the control parameters based on real-time operating data to ensure that the motor is always in the best operating state. The implementation effect of this step is to achieve real-time optimization of the motor's control performance and improve the stability and processing accuracy of the system. By dynamically adjusting the control instructions and parameters, the system can adapt to complex working environments and changing load conditions to ensure the efficient operation of the motor.

[0089] This embodiment first uses a servo motor to replace the traditional hydraulic or mechanical transmission mode, which greatly improves the accuracy and response speed of feed control. The servo motor has the characteristics of high precision and high dynamic response, and can achieve fast and accurate position and speed control, meeting the requirements of modern industrial production for high-precision processing. Secondly, the industrial Ethernet communication technology is introduced to achieve high-speed and reliable communication between the controller PLC and the servo motor driver. By identifying the type of communication protocol supported by the driver, it can flexibly adapt to servo motors of different brands and models, and improve the compatibility and scalability of the feed control system. By introducing parameter optimization strategies, including parameter adaptive adjustment strategies based on motor dynamic models and machine learning optimization strategies based on historical operation data, the problem that traditional PID control algorithms are difficult to cope with complex nonlinear systems and rapidly changing process requirements is effectively solved. The parameter adaptive adjustment strategy based on the motor dynamic model can adjust the control parameters in real time to adapt to the dynamic changes of system parameters, such as changes in system characteristics caused by factors such as material deformation and temperature changes. The machine learning optimization strategy based on historical operation data can continuously optimize the control parameters by analyzing a large amount of historical data, achieve continuous improvement of system performance, greatly reduce the workload of manual parameter adjustment, improve the efficiency and accuracy of parameter optimization, and enable the system to quickly adapt to the requirements of different materials and processes. In addition, the controller PLC monitors the operating status of the servo motor in real time and displays the status information on the human-machine interface, so that the operator can understand the operation status of the equipment in a timely manner. Based on the monitoring data, the system can also continuously optimize the control performance of the servo motor to achieve continuous improvement of equipment performance. This closed-loop optimization mechanism ensures that the system can always maintain the best working state, improves production efficiency and product quality. In general, this technical solution comprehensively improves the control accuracy, response speed and adaptability of the cold heading machine through advanced control technology and intelligent optimization strategies. By introducing servo motors and advanced control strategies, the problems of low accuracy, slow response and poor adaptability of the traditional cold heading machine control system are effectively solved, and the overall performance and intelligence level of the cold heading machine are significantly improved.

[0090] In one implementation of this embodiment, the parameter adaptive adjustment strategy based on the motor dynamic model includes the following steps:

[0091] S210, calculating in real time the first optimal control parameters of the cold heading machine servo motor according to the pre-built motor dynamic model and actual parameters, wherein the first optimal control parameters include proportional gain, integral gain and differential gain, the motor dynamic model is constructed based on the template parameters, and the motor dynamic model includes the motor torque equation and the motor motion equation;

[0092] S220. Machine learning optimization strategies based on historical operation data include:

[0093] S230, inputting the actual parameters into the pre-trained optimization model to predict the second optimal control parameters, wherein the optimization model is trained based on the historical operation data of the servo motor of the cold heading machine;

[0094] S240 , integrating the first optimal control parameter and the second optimal control parameter to obtain equipment optimization parameters.

[0095] In the control process of the cold heading machine servo motor, the parameter adaptive adjustment strategy based on the motor dynamic model is one of the core steps to achieve high-precision control. The motor dynamic model is a pre-built mathematical model used to describe the physical characteristics and dynamic behavior of the motor. The model includes the motor torque equation and the motor motion equation. The motor torque equation describes the relationship between the motor output torque and the current and speed. The formula can be expressed as:

[0096] T=K t I, where T is the motor torque, Kt is the torque constant, and I is the motor current.

[0097] The motor motion equation describes the relationship between motor speed, torque and load. The formula can be expressed as:

[0098]

[0099] Where J is the motor’s moment of inertia, ω is the motor’s speed, and T L is the load torque.

[0100] Based on the above equations, a complete motor dynamic model can be constructed to simulate the operating characteristics of the motor under different working conditions.

[0101] In actual applications, the controller PLC will collect the actual parameters of the motor in real time, including the current speed, current, torque and load. These parameters will be input into the dynamic model of the motor, and the real-time state of the motor will be obtained through numerical calculation. For example, if the current speed is 1000rpm, the current is 5A, and the load torque is 10N·m, the PLC will substitute these data into the motor torque equation and the motion equation to calculate the dynamic response of the motor. Next, the PLC will adjust the control parameters in real time according to the dynamic response of the motor, including the proportional gain Kp, the integral gain Ki and the differential gain Kd. These parameters are the core parameters of the PID controller and are used to adjust the control performance of the motor. The proportional gain determines the response speed of the controller to the error, the integral gain is used to eliminate the steady-state error, and the differential gain is used to suppress the oscillation of the system. By calculating and adjusting these parameters in real time, the PLC can ensure that the motor can maintain a stable operating state under different working conditions. Finally, the real-time optimization of the motor control parameters is achieved, and the control accuracy and response speed of the system are improved. By dynamically adjusting the control parameters, the system can adapt to different working conditions and load changes to ensure that the motor is always in the best operating state.

[0102] In the control process of the cold heading machine servo motor, the core of the machine learning optimization strategy based on historical operation data is to use a large amount of historical operation data to train the optimization model to predict the optimal control parameters. The historical operation data includes parameters such as the motor's speed, current, torque, temperature, and vibration frequency. These data reflect the operating characteristics of the motor under different working conditions. By analyzing these data, the nonlinear characteristics and complex dynamic behaviors of the motor can be captured, thereby building a machine learning model that can predict the optimal control parameters.

[0103] In practical applications, a large amount of historical operating data needs to be collected first. This data can be collected in real time by sensors and stored in a database. Next, a machine learning algorithm (such as a neural network, support vector machine, or random forest) is used to train this data and build an optimization model. During the training process, the model learns how to predict the optimal control parameters based on the current state of the motor. For example, if the current speed is 1200rpm and the load torque is 15N·m, the model will predict the corresponding proportional gain, integral gain, and differential gain. After the training is completed, the optimization model can be deployed to the controller PLC for real-time prediction of the optimal control parameters, and ultimately the intelligent optimization of the motor control parameters can be achieved. By using historical operating data, it can better adapt to complex working environments and changing load conditions to ensure efficient operation of the motor.

[0104] In the machine learning optimization strategy based on historical operation data, the actual parameters include the current speed, current, torque, temperature, vibration frequency, etc. of the motor, which reflect the real-time operation status of the motor. The pre-trained optimization model is trained with a large amount of historical operation data and can predict the optimal control parameters based on the current state of the motor. In actual applications, the controller PLC will collect the actual parameters of the motor in real time and input them into the optimization model. The model will calculate based on the input parameters and output the predicted optimal control parameters, including proportional gain, integral gain, and differential gain.

[0105] For example, suppose the current speed is 1500rpm, the current is 6A, the load torque is 20N·m, the temperature is 50℃, and the vibration frequency is 100Hz. These parameters will be input into the optimization model, and the model will predict the corresponding proportional gain, integral gain, and differential gain according to its internal calculation rules. Assume that the model predicts a proportional gain of 2.5, an integral gain of 0.8, and a differential gain of 1.2. These parameters will be used to adjust the motor control algorithm to ensure that the motor can maintain a stable operating state under the current working conditions.

[0106] In the control process of the cold heading machine servo motor, the first optimal control parameter is calculated in real time based on the motor dynamic model, reflecting the dynamic characteristics of the motor. The second optimal control parameter is predicted by the machine learning optimization strategy based on historical operation data, reflecting the historical operation characteristics of the motor. In order to obtain the optimal equipment optimization parameters, these two parameters need to be integrated. The integration process involves the dynamic adjustment of the weight coefficient, which determines the proportion of the two parameters in the final optimization parameters.

[0107] In actual applications, the controller PLC will dynamically adjust the weight coefficient according to the current load torque and average load torque of the motor. For example, assuming the current load torque T L is 18N·m, the average load torque T La The adjustment factor is 15N·m and α is 0.5. According to the following adjustment formula:

[0108]

[0109] It can be calculated that the weight coefficient w1 of the first optimal control parameter is 0.62, and the weight coefficient w2 of the second optimal control parameter is 0.38. Next, the PLC will perform weighted summation of the two parameters according to the weight coefficients to obtain the final equipment optimization parameters. For example, assuming that the first optimal control parameters are Kp = 2.0, Ki = 0.5, Kd = 1.0, and the second optimal control parameters are Kp = 2.5, Ki = 0.8, Kd = 1.2, then the final equipment optimization parameters are Kp = 2.0 × 0.62 + 2.5 × 0.38 = 2.19, Ki = 0.5 × 0.62 + 0.8 × 0.38 = 0.62, Kd = 1.0 × 0.62 + 1.2 × 0.38 = 1.08. These parameters are used to adjust the control algorithm of the motor to ensure that the motor can maintain a stable operating state under the current working conditions. By dynamically adjusting the weight coefficients, it can adapt to different working conditions and load changes to ensure that the motor is always in the best operating state.

[0110] In this implementation, the control performance of the cold heading machine servo motor is significantly improved through the parameter adaptive adjustment strategy based on the motor dynamic model and the machine learning optimization strategy based on historical operation data. The motor dynamic model can reflect the dynamic characteristics of the motor in real time and ensure the real-time optimization of the control parameters; the machine learning optimization strategy uses historical operation data to predict the optimal control parameters, thereby improving the adaptive ability of the system. The combination of the two strategies can quickly respond to external changes and adapt to complex working environments and changing load conditions. By dynamically adjusting the weight coefficient, the advantages of the two strategies can be combined to ensure that the motor is always in the best operating state. In the end, the control accuracy, response speed and stability of the system have been significantly improved, ensuring the efficient operation and processing accuracy of the cold heading machine.

[0111] In one implementation of this embodiment, integrating the first optimal control parameter and the second optimal control parameter to obtain the device optimization parameter includes the following steps:

[0112] S310, obtaining the real-time load torque of the cold heading machine servo motor, and calculating the average load torque within a preset time period according to the real-time load torque;

[0113] S320, performing weighted summation on the first optimal control parameter and the second optimal control parameter according to a preset weight coefficient to obtain an equipment optimization parameter;

[0114] Among them, the weight coefficient is obtained by dynamic adjustment based on the current load torque and average load torque of the cold heading machine servo motor using a preset adjustment formula, and the adjustment formula includes:

[0115] w2=1-w1;

[0116] Where w1 is the weight coefficient of the first optimal control parameter, w2 is the weight coefficient of the second optimal control parameter, T L is the current load torque, T La is the average load torque, and α is the adjustment factor.

[0117] In the control process of the cold heading machine servo motor, the real-time load torque refers to the load torque that the motor is bearing at the current moment, which is usually measured directly by the sensor or driver. In order to have a more comprehensive understanding of the load characteristics of the motor, it is also necessary to calculate the average load torque within the preset time period. The average load torque refers to the average value of the motor load torque within a certain time range, which can reflect the load change trend of the motor. In actual applications, the controller PLC will collect the load torque data of the motor in real time and store it in a sliding window buffer. The size of the sliding window is usually set according to the specific application scenario, for example, it can be set to the data of the past 10 seconds. Each time new load torque data is collected, the PLC will add it to the buffer and remove the oldest data to keep the size of the buffer unchanged. Next, the PLC will calculate the average value of all the data in the buffer to obtain the average load torque. For example, suppose that 10 load torque data are stored in the buffer, namely T1, T2,..., T 10 , then the average load torque T La It can be calculated as:

[0118]

[0119] In this way, the PLC can obtain the load torque information of the motor in real time and calculate its average load torque, providing data support for subsequent weight coefficient adjustment.

[0120] After obtaining the real-time load torque and average load torque of the cold heading machine servo motor, the controller PLC needs to dynamically adjust the weight coefficient according to these data, and perform weighted summation of the first optimal control parameter and the second optimal control parameter to obtain the final equipment optimization parameter. The weight coefficient determines the proportion of the two control parameters in the final optimization parameter. The adjustment factor α is usually set according to the specific application scenario to control the change speed of the weight coefficient. For example, assuming the current load torque T L is 18N·m, the average load torque T La is 15N·m, and the adjustment factor α is 0.5, then the weight coefficient w_1 can be calculated as:

[0121]

[0122] The weight coefficient w2 is w2=1-0.82=0.18. Next, the PLC will perform weighted summation of the first optimal control parameter and the second optimal control parameter according to the weight coefficient to obtain the final equipment optimization parameter. For details, refer to S240 and will not be repeated here. By dynamically adjusting the weight coefficient, the system can adapt to different working conditions and load changes to ensure that the motor is always in the best operating state.

[0123] By integrating the first optimal control parameter and the second optimal control parameter, the control performance of the cold heading machine servo motor is significantly improved in this embodiment. The calculation of real-time load torque and average load torque provides an important basis for the dynamic adjustment of the weight coefficient, so that the system can flexibly adjust the proportion of the control parameter according to the current state of the motor and the load change trend. The first optimal control parameter is based on the motor dynamic model, which can reflect the dynamic characteristics of the motor in real time and ensure the real-time optimization of the control parameter; the second optimal control parameter is based on the machine learning optimization strategy of historical operating data, which can use historical data to predict the optimal control parameter and improve the system's adaptability. The weighted sum of the two control parameters enables the system to combine the advantages of the two strategies to ensure that the motor can maintain a stable operating state under different working conditions.

[0124] In one implementation of this embodiment, the controller PLC establishes a communication connection with the driver of the cold heading machine servo motor via industrial Ethernet, including the following steps:

[0125] S410, the controller PLC uses different communication protocols to try to establish communication connection with the driver of the cold heading machine servo motor in turn, including ModbusTCP, EtherCAT, PROFINET and EtherNet / IP;

[0126] S420, when the controller PLC is successfully connected to the driver of the cold heading machine servo motor, determining the communication protocol type supported by the driver, and loading the corresponding target communication driver based on the communication protocol type;

[0127] S430, the controller PLC, establishes a communication connection with the driver of the cold heading machine servo motor based on the target communication driver and industrial Ethernet.

[0128] Figure 2 The schematic diagram of the communication connection process between the controller PLC and the driver provided in the embodiment of the present application is shown as follows: Figure 2As shown in the figure, in the control system of the cold heading machine servo motor, the communication connection between the controller PLC and the drive is the basis for achieving precise control. Since different models of drives may support different communication protocols, the PLC needs to use multiple communication protocols to try to establish a connection with the drive in turn. Commonly used communication protocols include Modbus TCP, EtherCAT, PROFINET, and EtherNet / IP. Each protocol has its own specific communication mechanism and data format. For example, Modbus TCP is a TCP / IP-based protocol that uses a simple request-response model; EtherCAT is a high-performance real-time Ethernet protocol that supports distributed clocks and high-speed data transmission; PROFINET is an industrial Ethernet protocol that supports real-time communication and device diagnosis; EtherNet / IP is a CIP (Common Industrial Protocol)-based protocol that supports device configuration and data exchange. In actual applications, the controller PLC will try to use these protocols to establish a connection with the drive in a predetermined order. At each attempt, the PLC sends a handshake signal and waits for the drive's response. If the drive responds successfully, the PLC will record the protocol type and stop trying other protocols. For example, suppose the PLC first tries to use the Modbus TCP protocol to send a request to read the register address. If the drive returns the correct response data within the preset time, the connection is successful. If no response is received or the response data is incorrect, the PLC will continue to try to use the EtherCAT protocol, and so on, until it finds the protocol type supported by the drive. The effect of this step is to achieve efficient communication between the PLC and the drive, ensuring the real-time and accuracy of the control system. By automatically identifying the communication protocol, the system can adapt to different models of drives, improving the compatibility and flexibility of the system.

[0129] After successfully establishing a communication connection with the driver of the cold heading machine servo motor, the controller PLC needs to determine the type of communication protocol supported by the driver and load the corresponding target communication driver. The communication protocol type determines the communication mechanism and data format between the PLC and the driver. In actual applications, the PLC will determine the protocol type supported by the driver based on the response data of the handshake signal. For example, if the connection is successfully established using the Modbus TCP protocol, the PLC will record the protocol type and load the communication driver of Modbus TCP. The driver is a software module used to implement the communication functions of a specific protocol, including data encapsulation, transmission, parsing, etc. After loading the driver, the PLC will initialize communication parameters such as IP address, port number, communication rate, etc. to ensure normal communication with the driver. For example, assuming that the driver supports the EtherCAT protocol, the PLC will load the EtherCAT driver and initialize communication parameters such as setting distributed clocks and configuring data frame structures. Finally, accurate communication between the PLC and the driver is achieved, ensuring the accuracy and real-time performance of data transmission. By loading the target communication driver, the system can make full use of the characteristics of the protocol to improve communication efficiency and control performance.

[0130] After determining the type of communication protocol supported by the drive and loading the target communication driver, the controller PLC needs to establish a stable communication connection with the drive of the cold heading machine servo motor based on the target communication driver and industrial Ethernet. First, the PLC will initialize the communication parameters of the industrial Ethernet through the target communication driver, including the IP address, port number, communication rate, etc. For example, if the PROFINET protocol is used, the PLC will set the IP address to 192.168.1.1, the port number to 34964, and the communication rate to 100Mbps. Next, the PLC will send a handshake signal to the drive to confirm the availability of the communication link. The handshake signal is usually a simple data packet used to test the connectivity and response speed of the communication link. If the drive returns the correct response data within the preset time, it means that the handshake is successful and the communication link is established. For example, assuming that the EtherNet / IP protocol is used, the PLC will send a CIP connection request, and the drive will return a CIP connection confirmation, indicating that the handshake is successful. Once the communication link is established, the PLC can exchange data with the drive through industrial Ethernet, including sending control instructions, receiving status information, etc. The effect of this step is to achieve stable communication between PLC and driver, ensuring the real-time and reliability of the control system. By establishing a stable communication link, the system can quickly respond to external changes and improve control accuracy and production efficiency.

[0131] This embodiment attempts to establish a communication connection with the driver of the cold heading machine servo motor in turn by using different communication protocols. The controller PLC can automatically identify the protocol type supported by the driver and load the corresponding target communication driver. This process ensures efficient communication between the PLC and the driver and improves the compatibility and flexibility of the system. After determining the communication protocol type and loading the driver, the PLC can initialize the communication parameters, establish a stable communication link, and ensure the accuracy and real-time performance of data transmission. By establishing a communication connection based on the target communication driver and industrial Ethernet, the characteristics of the protocol can be fully utilized to improve communication efficiency and control performance. Finally, the control accuracy, response speed and stability of the feed control system have been significantly improved, ensuring the efficient operation and processing accuracy of the cold heading machine.

[0132] In one implementation of this embodiment, the controller PLC establishes a communication connection with the driver of the cold heading machine servo motor based on the target communication driver and industrial Ethernet, including the following steps:

[0133] S510, the controller PLC initializes the communication parameters of the industrial Ethernet through the target communication driver, the communication parameters including the IP address, the port number and the communication rate;

[0134] S520, the controller PLC sends a handshake signal to the driver;

[0135] S530: If the handshake is successful, the controller PLC establishes a communication connection with the driver.

[0136] In the control system of the cold heading machine servo motor, the communication connection between the controller PLC and the drive is the basis for achieving precise control. In order to ensure the stability and efficiency of communication, the PLC needs to initialize the communication parameters of the industrial Ethernet through the target communication driver. The communication parameters include IP address, port number and communication rate, which determine the configuration and performance of the communication link. The IP address is the unique identifier of the device in the network, which is used to ensure that the data can be accurately transmitted to the target device. The port number is used to distinguish different communication services to ensure that the data can be received and processed by the correct application. The communication rate determines the speed of data transmission, usually in Mbps (megabits per second). In actual applications, the controller PLC will set the appropriate communication parameters according to the configuration of the drive and the network environment. For example, assuming that the IP address of the drive is 192.168.1.2, the port number is 502, and the communication rate is 100Mbps, the PLC will configure these parameters into the target communication driver. The initialization process involves multiple steps. First, the PLC will check the validity of the IP address to ensure that it meets the network specifications. Next, the PLC will configure the port number to ensure that it is consistent with the port number of the drive. Finally, the PLC sets the communication rate to ensure that it matches the rate of the network device. For example, if the network switch supports 100Mbps, the PLC will set the communication rate to 100Mbps to ensure the stability of data transmission. The effect of this step is to achieve accurate configuration of communication parameters and ensure the stability and efficiency of the communication link. By initializing the communication parameters, the system can quickly establish a communication connection and improve the response speed and reliability of the control system.

[0137] After initializing the communication parameters of the industrial Ethernet, the controller PLC needs to send a handshake signal to the driver of the cold heading machine servo motor to confirm the availability of the communication link. The handshake signal is a simple data packet used to test the connectivity and response speed of the communication link. The content and format of the handshake signal depend on the communication protocol used. For example, if the Modbus TCP protocol is used, the handshake signal can be a request to read the register address; if the EtherCAT protocol is used, the handshake signal can be a distributed clock synchronization request. In actual applications, the controller PLC will generate the corresponding handshake signal according to the configuration of the target communication driver and send it to the driver through the industrial Ethernet. For example, assuming that the ModbusTCP protocol is used, the PLC will generate a request to read the register address 0x0001, and the data packet format is: transaction identifier (2 bytes), protocol identifier (2 bytes), length field (2 bytes), unit identifier (1 byte), function code (1 byte), register address (2 bytes). The PLC will send this data packet to the IP address and port number of the driver and wait for the driver's response. If the driver returns the correct response data within the preset time, it means that the handshake is successful. For example, the format of the data packet returned by the driver is: transaction identifier (2 bytes), protocol identifier (2 bytes), length field (2 bytes), unit identifier (1 byte), function code (1 byte), byte count (1 byte), register data (2 bytes). The effect of this step is to achieve the connectivity test of the communication link and ensure the accuracy and real-time performance of data transmission. By sending a handshake signal, the system can quickly confirm the availability of the communication link and improve the response speed and reliability of the control system.

[0138] After successfully sending the handshake signal and receiving the correct response from the driver, the communication connection between the controller PLC and the driver of the cold heading machine servo motor is formally established. The establishment of the communication connection means that the PLC can exchange data with the driver through industrial Ethernet, including sending control instructions, receiving status information, etc. In practical applications, the establishment of a communication connection involves multiple steps. First, the PLC confirms the availability of the communication link based on the response data of the handshake signal. For example, if the Modbus TCP protocol is used, the PLC checks the transaction identifier, protocol identifier, function code, and register data in the returned data packet to ensure that it is consistent with the request sent. Next, the PLC initializes the communication buffer for storing the sent and received data. The communication buffer is a temporary storage area used to ensure the continuity and integrity of data transmission. For example, the PLC sets the size of the send buffer to 512 bytes and the size of the receive buffer to 1024 bytes. Finally, the PLC starts the communication thread for real-time processing of data sending and receiving. The communication thread is an independent execution unit used to ensure real-time and efficient data transmission. For example, the PLC creates a high-priority communication thread to handle the sending of control instructions and the receiving of status information.

[0139] This embodiment initializes the communication parameters of the industrial Ethernet, sends a handshake signal and establishes a communication connection, so that the controller PLC can achieve stable and efficient communication with the driver of the cold heading machine servo motor. The initialization of the communication parameters ensures the configuration and performance of the communication link, the sending and response of the handshake signal confirms the availability of the communication link, and the establishment of the communication connection realizes the real-time exchange of data, which can effectively improve the control accuracy and production efficiency. By accurately configuring the communication parameters, quickly confirming the availability of the communication link, and processing the sending and receiving of data in real time, it can quickly respond to external changes and adapt to complex working environments and changing load conditions.

[0140] In one implementation of this embodiment, the controller PLC parses the feed control request, obtains the feed control logic to be executed, and generates a control instruction based on the feed control logic, including the following steps:

[0141] S610, the controller PLC analyzes the feed control request and obtains the feed control logic to be executed;

[0142] S620, the controller PLC generates a feed speed curve and position control instructions according to the feed control logic;

[0143] S630, the controller PLC determines the optimal speed and optimal position of the cold heading machine servo motor according to the feed speed curve and the position control instruction, and generates a control instruction based on the optimal speed and optimal position.

[0144] In the control system of the cold heading machine servo motor, parsing the feed control request is the first step to generate control instructions. The feed control request is sent by the user through the human-machine interface (HMI) and contains the feed parameters set by the user, such as feed length, feed speed, processing mode, etc. The controller PLC needs to parse these parameters and convert them into specific feed control logic. The parsing process first involves the conversion of data format. Feed control requests are usually transmitted in a specific data format (such as JSON or XML), and the PLC needs to parse it into an internal data structure. For example, assuming that the JSON format of the feed control request is {"length":100,"speed":500,"mode":"continuous"}, the PLC will parse it into a data structure with a length of 100 mm, a speed of 500 mm / s, and a mode of continuous feeding. Next, the PLC will generate the feed control logic based on the parsed parameters. The feed control logic is a sequence of instructions that describes how the motor runs, including stages such as starting, accelerating, constant speed, decelerating, and stopping. For example, if the user sets the feed length to 100 mm and the feed speed to 500 mm / s, the PLC will generate a feed control logic that includes acceleration, constant speed, and deceleration stages. The acceleration stage accelerates from 0 to 500 mm / s, the constant speed stage maintains a speed of 500 mm / s, and the deceleration stage decelerates from 500 mm / s to 0. By parsing the feed control request, the system can quickly respond to the user's operating intentions and improve the flexibility and adaptability of the control system.

[0145] After parsing the feed control request and obtaining the feed control logic, the controller PLC generates a feed speed curve and position control instructions according to the feed control logic. The feed speed curve describes the change of the motor speed over time during the feeding process, and the position control instruction describes the target position that the motor needs to reach. The process of generating a feed speed curve involves multiple steps. First, the PLC calculates the time point and speed value of each stage according to the acceleration, uniform speed and deceleration stages in the feed control logic. For example, assuming that the feed length is 100 mm, the feed speed is 500 mm / s, the acceleration time is 0.2 seconds, and the deceleration time is 0.2 seconds, the PLC will generate a feed speed curve containing acceleration, uniform speed and deceleration stages. The acceleration stage accelerates from 0 to 500 mm / s, the uniform speed stage maintains a speed of 500 mm / s, and the deceleration stage decelerates from 500 mm / s to 0. Next, the PLC generates a position control instruction according to the feed speed curve. The position control instruction describes the target position that the motor needs to reach at each time point. For example, assuming that the motor is in the acceleration phase at a time point of 0.1 seconds and a speed of 250 mm / s, the PLC will calculate that the motor's target position at 0.1 seconds is 12.5 mm. By generating feed speed curves and position control instructions, the system can quickly respond to external changes and improve control accuracy and production efficiency.

[0146] After generating the feed speed curve and position control instructions, the controller PLC needs to determine the optimal speed and optimal position of the cold heading machine servo motor based on these data and generate the final control instructions. The optimal speed and optimal position are the target values ​​that the motor needs to achieve during the feeding process to ensure that the motor can run at the predetermined speed and position. The process of determining the optimal speed involves multiple steps.

[0147] First, the PLC calculates the target speed of the motor at each time point based on the feed speed curve. For example, assuming the feed speed curve has a speed of 250 mm / s at 0.1 seconds, the PLC calculates the target speed of the motor to be 250 mm / s. Next, the PLC converts the target speed to speed based on the feed wheel radius of the motor. For example, assuming the feed wheel radius is 50 mm, the PLC calculates the target speed of the motor to be:

[0148]

[0149] The process of determining the optimal position is similar. The PLC calculates the target position of the motor at each time point based on the position control instruction. For example, assuming that the target position of the position control instruction at 0.1 seconds is 12.5 mm, the PLC will calculate the target position of the motor to be 12.5 mm. Finally, the PLC generates a control instruction based on the optimal speed and optimal position. The control instructions include speed control instructions and position control instructions, which are used to control the speed and position of the motor. For example, the PLC generates a control instruction containing a target speed of 0.796 rpm and a target position of 12.5 mm, and sends it to the drive via industrial Ethernet. Ultimately, precise control of the motor operation is achieved, ensuring the smoothness and accuracy of the feeding process. By generating control instructions for the optimal speed and optimal position, the system can respond quickly to external changes and improve control accuracy and production efficiency.

[0150] This embodiment can realize high-precision control of the servo motor of the cold heading machine by parsing the feed control request, generating the feed speed curve and position control instructions, determining the optimal speed and optimal position and generating control instructions. The controller PLC can realize high-precision control of the servo motor of the cold heading machine. Parsing the feed control request ensures the accurate conversion of the user instruction to the specific control logic, generating the feed speed curve and position control instructions realizes the smoothness and accuracy of the motor operation, and determining the optimal speed and optimal position and generating control instructions ensures that the motor can run at the predetermined speed and position, effectively improving the response speed and reliability of the control system, and ensuring the efficient operation and processing accuracy of the cold heading machine. By accurately controlling the speed and position of the motor, the feed control system can quickly respond to external changes and adapt to complex working environments and variable load conditions.

[0151] In one implementation of this embodiment, the controller PLC determines the optimal speed and optimal position of the cold heading machine servo motor according to the feed speed curve and the position control instruction, and generates a control instruction based on the optimal speed and optimal position, including the following steps:

[0152] S710, the controller PLC analyzes the optimal position and the maximum allowed acceleration according to the position control instruction;

[0153] S720, the controller PLC obtains the current position of the servo motor of the cold heading machine, and calculates the position error according to the optimal position and the current position, wherein the position error is the difference between the current position and the optimal position;

[0154] S730, the controller PLC calculates the optimal feed speed according to the position error and the maximum allowed acceleration;

[0155] S740, the controller PLC compares the optimal feed speed with the feed speed curve, and if the optimal feed speed exceeds the allowable range of the feed speed curve, adjusts the optimal feed speed to the maximum allowable speed in the allowable range;

[0156] S750, the controller PLC obtains the feed wheel radius of the cold heading machine servo motor;

[0157] S760, the controller PLC calculates the optimal speed according to the optimal feed speed and feed wheel radius;

[0158] S770. The controller PLC generates a control instruction based on the optimal speed and the optimal position. The control instruction includes a speed control instruction and a position control instruction.

[0159] In the control system of the cold heading machine servo motor, the position control instruction is the key parameter that describes the target position that the motor needs to reach. The controller PLC needs to parse the optimal position and the maximum allowed acceleration according to the position control instruction to ensure that the motor can run according to the predetermined path and speed. The optimal position is the target position that the motor needs to reach during the feeding process, which is usually set by the user through the human-machine interface. For example, assuming that the user sets the feeding length to 100 mm, the PLC will parse the optimal position as 100 mm. The maximum allowed acceleration is the maximum acceleration value that the motor can withstand during acceleration and deceleration, which is usually determined by the physical characteristics and load conditions of the motor. For example, assuming that the maximum acceleration of the motor is 5000 mm / s2, the PLC will parse the maximum allowed acceleration as 5000 mm / s2. The parsing process involves data format conversion and parameter extraction. Position control instructions are usually transmitted in a specific data format (such as JSON or XML), and the PLC needs to parse it into an internal data structure. For example, if the JSON format of the position control instruction is {"position":100,"max_acceleration":5000}, the PLC will parse it as the optimal position of 100 mm and the maximum allowed acceleration of 5000 mm / s2.

[0160] After parsing the optimal position, the controller PLC needs to obtain the current position of the cold heading machine servo motor and calculate the position error. The current position is the real-time position of the motor during the feeding process, which is usually measured by an encoder or sensor. For example, assuming that the current position of the motor is 50 mm, the PLC will obtain the current position as 50 mm. The position error is the difference between the current position and the optimal position, which is used to evaluate whether the motor needs to adjust its operating status. For example, assuming that the optimal position is 100 mm and the current position is 50 mm, the PLC will calculate the position error as 50 mm. The PLC will obtain the current position data from the drive through industrial Ethernet and compare it with the optimal position. For example, assuming that the current position data is 50 mm and the optimal position is 100 mm, the PLC will calculate the position error as 50 mm, realizing real-time monitoring and error evaluation of the motor position, ensuring the accuracy and stability of the motor operation.

[0161] After calculating the position error, the controller PLC needs to calculate the optimal feed rate based on the position error and the maximum allowed acceleration. The optimal feed rate is the target speed that the motor needs to reach during the feeding process to ensure that the motor can run according to the predetermined path and speed. The process of calculating the optimal feed rate involves multiple steps. First, the PLC calculates the target speed of the motor based on the position error and the maximum allowed acceleration. For example, assuming the position error is 50 mm and the maximum allowed acceleration is 5000 mm / s2, the PLC will calculate the target speed of the motor as:

[0162]

[0163] Next, the PLC will adjust the target speed according to the physical characteristics of the motor and the load conditions. For example, if the maximum speed of the motor is 1000 mm / s, the PLC will adjust the target speed to 707.11 mm / s, achieving precise control of the motor speed and ensuring the smoothness and accuracy of the feeding process. By calculating the optimal feed speed, the system can quickly respond to external changes and improve control accuracy and production efficiency.

[0164] After calculating the optimal feed speed, the controller PLC needs to compare it with the feed speed curve to ensure that the optimal feed speed is within the allowable range. The feed speed curve describes the change in motor speed over time during the feeding process, and is usually set by the user through the human-machine interface. For example, assuming that the allowable range of the feed speed curve is 0 to 1000 mm / s, the PLC will compare the optimal feed speed of 707.11 mm / s with the feed speed curve. If the optimal feed speed exceeds the allowable range, the PLC will adjust it to the maximum allowable speed in the allowable range. For example, assuming that the optimal feed speed is 1200 mm / s, which is beyond the allowable range, the PLC will adjust it to 1000 mm / s, achieving precise control of the motor speed and ensuring the smoothness and accuracy of the feeding process.

[0165] After adjusting the optimal feed speed, the controller PLC needs to obtain the feed wheel radius of the cold heading machine servo motor to convert the optimal feed speed into the optimal speed. The feed wheel radius is the physical size of the motor feed wheel and is usually provided by the equipment manufacturer. For example, assuming the feed wheel radius is 50 mm, the PLC will obtain the feed wheel radius as 50 mm. The PLC can obtain the feed wheel radius data from the drive via industrial Ethernet and store it in the internal memory. For example, assuming the feed wheel radius is 50 mm, the PLC will store it as 50 mm.

[0166] After obtaining the feed wheel radius, the controller PLC needs to calculate the optimal speed based on the optimal feed speed and feed wheel radius. The optimal speed is the target speed that the motor needs to reach during the feeding process to ensure that the motor can run at the predetermined speed and position. The process of calculating the optimal speed involves multiple steps. First, the PLC calculates the target speed of the motor based on the optimal feed speed and feed wheel radius. For example, assuming the optimal feed speed is 707.11 mm / s and the feed wheel radius is 50 mm, the PLC will calculate the target speed of the motor as:

[0167]

[0168] Next, the PLC will adjust the target speed according to the physical characteristics of the motor and the load conditions. For example, if the maximum speed of the motor is 5 rpm, the PLC will adjust the target speed to 2.25 rpm, achieving precise control of the motor speed and ensuring the smoothness and accuracy of the feeding process.

[0169] After calculating the optimal speed and optimal position, the controller PLC needs to generate control instructions based on these data. The control instructions include speed control instructions and position control instructions, which are used to control the speed and position of the motor. The process of generating control instructions involves multiple steps. First, the PLC generates a speed control instruction based on the optimal speed. For example, assuming that the optimal speed is 2.25 rpm, the PLC generates a speed control instruction containing a target speed of 2.25 rpm. Next, the PLC generates a position control instruction based on the optimal position. For example, assuming that the optimal position is 100 mm, the PLC generates a position control instruction containing a target position of 100 mm. Finally, the PLC sends the speed control instruction and position control instruction to the drive via industrial Ethernet. For example, the PLC generates a control instruction containing a target speed of 2.25 rpm and a target position of 100 mm, and sends it to the drive via industrial Ethernet. Ultimately, precise control of the motor operation is achieved, ensuring the smoothness and accuracy of the feeding process.

[0170] This embodiment can realize high-precision control of the servo motor of the cold heading machine by analyzing the optimal position and the maximum acceleration allowed, obtaining the current position and calculating the position error, calculating the optimal feed speed, adjusting the optimal feed speed, obtaining the feed wheel radius, calculating the optimal speed and generating control instructions. The controller PLC can realize high-precision control of the servo motor of the cold heading machine. The analysis of the optimal position and the maximum acceleration allowed ensures the smoothness and accuracy of the motor operation, the acquisition of the current position and the calculation of the position error realizes the real-time monitoring and error evaluation of the motor position, the calculation of the optimal feed speed and the adjustment of the optimal feed speed ensure the precise control of the motor speed, the acquisition of the feed wheel radius and the calculation of the optimal speed realize the precise control of the motor speed, and the generation of control instructions ensures that the motor can run at the predetermined speed and position. This process improves the response speed and reliability of the control system and ensures the efficient operation and processing accuracy of the cold heading machine. By accurately controlling the speed and position of the motor, the system can quickly respond to external changes and adapt to complex working environments and variable load conditions.

[0171] In one implementation of this embodiment, the control performance of the cold heading machine servo motor includes a feed speed curve, the operating state includes load torque, motor output torque, current data, temperature data and vibration frequency data, and the controller PLC optimizes the control performance of the cold heading machine servo motor based on the operating state, including the following steps:

[0172] S810, the controller PLC uses the load fluctuation calculation formula to calculate the load fluctuation of the cold heading machine servo motor according to the load torque and the motor output torque;

[0173] S820, the controller PLC uses the health index calculation formula to calculate the health index of the cold heading machine servo motor based on the current data, temperature data and vibration frequency data;

[0174] S830, the controller PLC dynamically adjusts the control instructions of the cold heading machine servo motor based on the health index and load fluctuation to compensate for load fluctuations and optimize the feed speed curve.

[0175] In the control system of the cold heading machine servo motor, load fluctuation is an important indicator reflecting the change of motor load. Load fluctuation refers to the change amplitude of the load torque of the motor during operation, which is usually calculated by the difference between the load torque and the motor output torque. Load torque is the external load torque borne by the motor, which is usually measured directly by a sensor or driver. The motor output torque is the torque actually output by the motor, which is usually calculated by the current and speed of the motor. In actual applications, the controller PLC will collect load torque and motor output torque data in real time and input them into the load fluctuation calculation formula. The load fluctuation calculation formula can usually be expressed as:

[0176] ΔT=|T L -T M |, where ΔT is the load fluctuation, T L is the load torque, T M is the motor output torque. For example, assuming the load torque is 20N·m and the motor output torque is 18N·m, the PLC will calculate the load fluctuation to be 2N·m. The PLC will obtain the load torque and motor output torque data from the driver through Industrial Ethernet and substitute them into the load fluctuation calculation formula. For example, assuming the load torque is 20N·m and the motor output torque is 18N·m, the PLC will calculate the load fluctuation to be 2N·m. By calculating the load fluctuation, the system can respond quickly to external changes and improve control accuracy and production efficiency.

[0177] In the control system of the cold heading machine servo motor, the health index is an important indicator that reflects the operating status of the motor. The health index refers to the health status of the motor during operation, which is usually calculated through current data, temperature data and vibration frequency data. The current data is the current value when the motor is running, usually measured by a current sensor. The temperature data is the temperature value when the motor is running, usually measured by a temperature sensor. The vibration frequency data is the vibration frequency when the motor is running, usually measured by a vibration sensor. In actual applications, the controller PLC will collect current data, temperature data and vibration frequency data in real time, and input them into the health index calculation formula. The health index calculation formula can usually be expressed as:

[0178] H = w1·I+w2·T+w3·V;

[0179] Where H is the health index, I is the current data, T is the temperature data, V is the vibration frequency data, and w1, w2, and w3 are weight coefficients. For example, assuming the current data is 10A, the temperature data is 50°C, and the vibration frequency data is 100Hz, the weight coefficients are 0.5, 0.3, and 0.2 respectively. The PLC will calculate the health index as 0.5×10+0.3×50+0.2×100=5+15+20=40. The PLC will obtain the current data, temperature data, and vibration frequency data from the sensor through the industrial Ethernet and substitute them into the health index calculation formula.

[0180] After calculating the health index and load fluctuation, the controller PLC needs to dynamically adjust the control instructions of the cold heading machine servo motor based on these data to compensate for load fluctuations and optimize the feed speed curve. The process of dynamically adjusting the control instructions involves multiple steps.

[0181] First, the PLC adjusts the output torque of the motor according to the load fluctuation. For example, assuming the load fluctuation is 2N·m, the PLC will increase the output torque of the motor by 2N·m to compensate for the load fluctuation. Next, the PLC adjusts the feed speed curve according to the health index. For example, assuming the health index is 40, the PLC will reduce the slope of the feed speed curve to reduce the load on the motor. Finally, the PLC generates a new control instruction based on the adjusted output torque and feed speed curve. For example, assuming the adjusted output torque is 20N·m and the slope of the feed speed curve is 0.5, the PLC will generate a control instruction containing the target output torque of 20N·m and the target feed speed curve slope of 0.5, and send it to the drive via industrial Ethernet. Finally, the dynamic adjustment of the motor control instruction is realized, ensuring the stability and reliability of the motor operation. By dynamically adjusting the control instruction, the system can respond quickly to external changes and improve control accuracy and production efficiency.

[0182] In this embodiment, the controller PLC can achieve high-precision control of the cold heading machine servo motor by calculating the load fluctuation amount and health index and dynamically adjusting the control instructions. Calculating the load fluctuation amount ensures real-time monitoring and evaluation of the motor load fluctuation, calculating the health index realizes real-time monitoring and evaluation of the motor health status, and dynamically adjusting the control instructions ensures the stability and reliability of the motor operation. This process improves the response speed and reliability of the control system and ensures the efficient operation and processing accuracy of the cold heading machine. By accurately controlling the output torque and feed speed curve of the motor, the system can quickly respond to external changes and adapt to complex working environments and changing load conditions.

[0183] In one implementation of this embodiment, the controller PLC dynamically adjusts the control instructions of the cold heading machine servo motor based on the health index and the load fluctuation to compensate for the load fluctuation and optimize the feed speed curve, including the following steps:

[0184] S910, the controller PLC adjusts the output torque of the cold heading machine servo motor according to the load fluctuation and the preset output torque adjustment formula;

[0185] S920, the controller PLC adjusts the feed speed curve according to the health index and a preset feed speed adjustment formula;

[0186] S930, dynamically adjust the control command of the cold heading machine servo motor according to the output torque and feed speed curve.

[0187] In the control system of the cold heading machine servo motor, the load fluctuation is an important indicator reflecting the change of the motor load. In order to compensate for the load fluctuation, the controller PLC needs to dynamically adjust the output torque of the motor according to the load fluctuation and the preset output torque adjustment formula. The output torque is the actual output torque of the motor, which is usually calculated by the current and speed of the motor. The preset output torque adjustment formula can usually be expressed as T M ′=T M +k·ΔT, where T M ' is the adjusted output torque, T Mis the current output torque, ΔT is the load fluctuation, and k is the adjustment coefficient. For example, assuming that the current output torque is 18N·m, the load fluctuation is 2N·m, and the adjustment coefficient is 1, the PLC will calculate the adjusted output torque to be 20N·m. The PLC will obtain the current output torque and load fluctuation data from the drive through Industrial Ethernet and substitute it into the output torque adjustment formula. For example, assuming that the current output torque is 18N·m, the load fluctuation is 2N·m, and the adjustment coefficient is 1, the PLC will calculate the adjusted output torque to be 20N·m, realizing the dynamic adjustment of the motor output torque and ensuring the stability and reliability of the motor operation. By adjusting the output torque, the system can respond quickly to external changes and improve control accuracy and production efficiency.

[0188] In the control system of the cold heading machine servo motor, the health index is an important indicator reflecting the operating status of the motor. In order to optimize the feed speed curve, the controller PLC needs to dynamically adjust the feed speed curve according to the health index and the preset feed speed adjustment formula. The preset feed speed adjustment formula can usually be expressed as v′=v·(1-α·H), where v' is the adjusted feed speed, v is the current feed speed, H is the health index, and α is the adjustment coefficient. For example, assuming that the current feed speed is 500 mm / s, the health index is 40, and the adjustment coefficient is 0.01, the PLC will calculate the adjusted feed speed to be 480 mm / s.

[0189] After adjusting the output torque and feed speed curves, the controller PLC needs to dynamically adjust the control instructions of the cold heading machine servo motor based on these data. The control instructions include speed control instructions and position control instructions, which are used to control the speed and position of the motor. The process of dynamically adjusting the control instructions involves multiple steps. First, the PLC generates a new speed control instruction based on the adjusted output torque. For example, assuming that the adjusted output torque is 20N·m, the PLC generates a speed control instruction containing the target output torque of 20N·m. Next, the PLC generates a new position control instruction based on the adjusted feed speed curve. For example, assuming that the adjusted feed speed is 480 mm / s, the PLC generates a position control instruction containing the target feed speed of 480 mm / s. Finally, the PLC sends the new speed control instruction and position control instruction to the drive via industrial Ethernet. For example, the PLC generates a control instruction containing the target output torque of 20N·m and the target feed speed of 480 mm / s and sends it to the drive via industrial Ethernet.

[0190] In this embodiment, the controller PLC can achieve high-precision control of the servo motor of the cold heading machine by adjusting the output torque, adjusting the feed speed curve and dynamically adjusting the control instructions. Adjusting the output torque ensures real-time compensation of motor load fluctuations, adjusting the feed speed curve achieves real-time optimization of the motor operating state, and dynamically adjusting the control instructions ensures the stability and reliability of the motor operation. This process improves the response speed and reliability of the control system and ensures the efficient operation and processing accuracy of the cold heading machine. By accurately controlling the output torque and feed speed curve of the motor, the system can quickly respond to external changes and adapt to complex working environments and changing load conditions.

[0191] The present application also provides a feed control system, such as Figure 3 As shown, including:

[0192] A human-machine interface, configured to route a feed control request to a controller PLC in response to receiving a feed control request sent by a user through the human-machine interface;

[0193] Servo motor for cold heading machine;

[0194] A controller PLC is connected to the human-machine interface and to the driver of the cold heading machine servo motor, and is used to perform:

[0195] Establishing a communication connection with the driver of the cold heading machine servo motor through industrial Ethernet to identify the type of communication protocol supported by the driver of the cold heading machine servo motor and to obtain actual parameters of the cold heading machine servo motor, wherein the actual parameters include rated speed, rated torque and encoder resolution, motor inertia and load characteristics;

[0196] Parsing the feed control request to obtain the feed control logic to be executed, and generating a control instruction based on the feed control logic;

[0197] The actual parameters are compared with the preset template parameters, and if there are differences, the actual parameters are optimized using a parameter optimization strategy to obtain device optimization parameters, wherein the parameter optimization strategy includes a parameter adaptive adjustment strategy based on a motor dynamic model and a machine learning optimization strategy based on historical operation data;

[0198] The control instruction and the device optimization parameter are sent to the driver of the cold heading machine servo motor through the industrial Ethernet, wherein the control instruction is used to control the speed and position of the cold heading machine servo motor, and the cold heading machine is used to configure the parameters of the cold heading machine servo motor according to the device optimization parameter;

[0199] Monitor the operating status of the cold heading machine servo motor in real time and display the operating status on the human-machine interface;

[0200] Based on the operating status, the control performance of the cold heading machine servo motor is optimized.

[0201] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0202] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of the processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0203] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0204] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0205] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0206] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0207] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0208] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0209] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A cold heading machine servo motor feeding control method, characterized in that: Applied to a feed control system, the feed control system includes a cold heading machine servo motor, a controller PLC and a human-machine interface, the method includes: In response to receiving a feed control request sent by a user through the human-machine interface, the human-machine interface routes the feed control request to the controller PLC; The controller PLC establishes a communication connection with the driver of the cold heading machine servo motor through industrial Ethernet to identify the type of communication protocol supported by the driver of the cold heading machine servo motor and obtain actual parameters of the cold heading machine servo motor; The controller PLC analyzes the feed control request, obtains the feed control logic to be executed, and generates a control instruction based on the feed control logic; The controller PLC compares the actual parameters with the preset template parameters, and in the case of differences, optimizes the actual parameters using a parameter optimization strategy to obtain device optimization parameters, wherein the parameter optimization strategy includes a parameter adaptive adjustment strategy based on a motor dynamic model and a machine learning optimization strategy based on historical operation data; The controller PLC sends the control instruction and the equipment optimization parameter to the driver of the cold heading machine servo motor through the industrial Ethernet, the control instruction is used to control the speed and position of the cold heading machine servo motor, and the driver is used to configure the parameters of the cold heading machine servo motor according to the equipment optimization parameter; The controller PLC monitors the operating status of the cold heading machine servo motor in real time and displays the operating status on the human-machine interface; The controller PLC optimizes the control performance of the cold heading machine servo motor based on the operating status.

2. The method according to claim 1, characterized in that The parameter adaptive adjustment strategy based on the motor dynamic model includes: According to the pre-built motor dynamic model and the actual parameters, the first optimal control parameters of the cold heading machine servo motor are calculated in real time, wherein the first optimal control parameters include proportional gain, integral gain and differential gain, the motor dynamic model is constructed based on the template parameters, and the motor dynamic model includes a motor torque equation and a motor motion equation; The machine learning optimization strategy based on historical operation data includes: Inputting the actual parameters into a pre-trained optimization model to predict a second optimal control parameter, wherein the optimization model is trained based on historical operating data of the cold heading machine servo motor; The first optimal control parameter and the second optimal control parameter are integrated to obtain equipment optimization parameters.

3. The method according to claim 2, characterized in that The step of integrating the first optimal control parameter and the second optimal control parameter to obtain a device optimization parameter includes: Acquire the real-time load torque of the servo motor of the cold heading machine, and calculate the average load torque within a preset time period according to the real-time load torque; According to a preset weight coefficient, weighted summation is performed on the first optimal control parameter and the second optimal control parameter to obtain an equipment optimization parameter; The weight coefficient is obtained by dynamically adjusting the current load torque of the cold heading machine servo motor and the average load torque using a preset adjustment formula, and the adjustment formula includes: Wherein, w1 is the weight coefficient of the first optimal control parameter, w2 is the weight coefficient of the second optimal control parameter, T L is the current load torque, T La is the average load torque, and α is the adjustment factor.

4. The method according to claim 1, characterized in that The controller PLC establishes a communication connection with the driver of the cold heading machine servo motor through industrial Ethernet, including: The controller PLC sequentially attempts to establish a communication connection with the driver of the cold heading machine servo motor using different communication protocols, wherein the communication protocols include ModbusTCP, EtherCAT, PROFINET and EtherNet / IP; When the controller PLC is successfully connected to the driver of the cold heading machine servo motor, determining the type of communication protocol supported by the driver, and loading a corresponding target communication driver based on the communication protocol type; The controller PLC establishes a communication connection with the driver of the cold heading machine servo motor based on the target communication driver and the industrial Ethernet.

5. The method according to claim 4, characterized in that The controller PLC establishes a communication connection with the driver of the cold heading machine servo motor based on the target communication driver and the industrial Ethernet, including: The controller PLC initializes the communication parameters of the industrial Ethernet through the target communication driver, and the communication parameters include IP address, port number and communication rate; The controller PLC sends a handshake signal to the driver; If the handshake is successful, the controller PLC establishes a communication connection with the driver.

6. The method according to claim 1, characterized in that The controller PLC analyzes the feed control request to obtain the feed control logic to be executed, and generates a control instruction based on the feed control logic, including: The controller PLC analyzes the feed control request to obtain the feed control logic to be executed; The controller PLC generates a feed speed curve and position control instructions according to the feed control logic; The controller PLC determines the optimal speed and optimal position of the cold heading machine servo motor according to the feed speed curve and the position control instruction, and generates a control instruction based on the optimal speed and the optimal position.

7. The method according to claim 6, characterized in that The controller PLC determines the optimal speed and optimal position of the cold heading machine servo motor according to the feed speed curve and the position control instruction, and generates a control instruction based on the optimal speed and the optimal position, including: The controller PLC analyzes the optimal position and the maximum allowed acceleration according to the position control instruction; The controller PLC obtains the current position of the cold heading machine servo motor, and calculates the position error according to the optimal position and the current position, wherein the position error is the difference between the current position and the optimal position; The controller PLC calculates the optimal feed speed according to the position error and the maximum acceleration allowed; The controller PLC compares the optimal feed speed with the feed speed curve, and if the optimal feed speed exceeds the allowable range of the feed speed curve, adjusts the optimal feed speed to the maximum allowable speed in the allowable range; The controller PLC obtains the feed wheel radius of the cold heading machine servo motor; The controller PLC calculates the optimal rotation speed according to the optimal feed speed and the feed wheel radius; The controller PLC generates a control instruction based on the optimal rotation speed and the optimal position, and the control instruction includes a rotation speed control instruction and a position control instruction.

8. The method according to claim 7, characterized in that The control performance of the cold heading machine servo motor includes a feed speed curve, the operating state includes load torque, motor output torque, current data, temperature data and vibration frequency data, and the controller PLC optimizes the control performance of the cold heading machine servo motor based on the operating state, including: The controller PLC uses a load fluctuation calculation formula to calculate the load fluctuation of the cold heading machine servo motor according to the load torque and the motor output torque; The controller PLC uses a health index calculation formula to calculate the health index of the cold heading machine servo motor according to the current data, the temperature data and the vibration frequency data; The controller PLC dynamically adjusts the control instructions of the cold heading machine servo motor based on the health index and the load fluctuation amount to compensate for the load fluctuation and optimize the feed speed curve.

9. The method according to claim 8, characterized in that The controller PLC dynamically adjusts the control instructions of the cold heading machine servo motor based on the health index and the load fluctuation amount to compensate for the load fluctuation and optimize the feed speed curve, including: The controller PLC adjusts the output torque of the cold heading machine servo motor according to the load fluctuation amount and a preset output torque adjustment formula; The controller PLC adjusts the feed speed curve according to the health index and a preset feed speed adjustment formula; The control instruction of the cold heading machine servo motor is dynamically adjusted according to the output torque and the feed speed curve.

10. A feed control system, characterized in that: include: A human-machine interface, configured to route a feed control request to a controller PLC in response to receiving a feed control request sent by a user through the human-machine interface; Servo motor for cold heading machine; A controller PLC is connected to the human-machine interface and to the driver of the cold heading machine servo motor, and is used to perform: Establishing a communication connection with the driver of the cold heading machine servo motor through industrial Ethernet to identify the type of communication protocol supported by the driver of the cold heading machine servo motor and to obtain actual parameters of the cold heading machine servo motor, wherein the actual parameters include rated speed, rated torque and encoder resolution, motor inertia and load characteristics; Parsing the feed control request to obtain the feed control logic to be executed, and generating a control instruction based on the feed control logic; The actual parameters are compared with the preset template parameters, and if there are differences, the actual parameters are optimized using a parameter optimization strategy to obtain device optimization parameters, wherein the parameter optimization strategy includes a parameter adaptive adjustment strategy based on a motor dynamic model and a machine learning optimization strategy based on historical operation data; The control instruction and the device optimization parameter are sent to the driver of the cold heading machine servo motor through the industrial Ethernet, wherein the control instruction is used to control the speed and position of the cold heading machine servo motor, and the cold heading machine is used to configure the parameters of the cold heading machine servo motor according to the device optimization parameter; Monitor the operating status of the cold heading machine servo motor in real time and display the operating status on the human-machine interface; Based on the operating status, the control performance of the cold heading machine servo motor is optimized.

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