Cold heading machine servo motor feeding control method and system

Abstract

This application discloses a feeding control method and system for a servo motor of a cold heading machine, relating to the field of control systems. The method includes: a human-machine interface (HMI) routes a feeding control request to a PLC; the PLC establishes a communication connection with the driver of the cold heading machine servo motor; the PLC parses the feeding control request to obtain the feeding control logic to be executed, and generates control instructions based on the feeding control logic; the PLC compares the actual parameters with preset template parameters, and optimizes the actual parameters to obtain optimized equipment parameters if differences exist; the PLC sends the control instructions and optimized equipment parameters to the driver of the cold heading machine servo motor; the PLC monitors the operating status of the cold heading machine servo motor in real time and displays the operating status on the HMI; and the PLC optimizes the control performance of the cold heading machine servo motor based on the operating status. This application can effectively improve the control accuracy of the cold heading machine control system.

Description

Technical Field

[0001] This application relates to the field of control systems, and more particularly to a servo motor feeding control method and system for a cold heading machine. Background Technology

[0002] Cold heading machines are widely used in metal processing, primarily for manufacturing standard parts such as bolts, nuts, and screws. The cold heading process uses plastic deformation in a cold state to shape metal wire into the desired form and size. Due to its advantages of high efficiency, energy saving, and high material utilization, cold heading is widely used in industries such as automotive, aerospace, and construction. However, with the development of industrial automation and intelligent manufacturing, higher requirements are being placed on the control precision of cold heading machines. High-precision control not only improves product quality but also reduces material waste and production costs.

[0003] In traditional cold heading machine control systems, hydraulic or mechanical transmission methods are typically used for feeding control. While these systems are simple in structure and low in cost, they have significant shortcomings in terms of control accuracy and response speed. Specifically, existing cold heading machine control systems suffer from the following problems: 1. Traditional cold heading machine control systems often employ simple PID control algorithms, which are ill-suited to complex nonlinear systems and rapidly changing process requirements. During the cold heading process, factors such as material deformation and temperature changes cause dynamic changes in system parameters, making it difficult for simple PID control to achieve high-precision control. 2. Parameter adjustments in traditional control systems rely heavily on manual experience, making it difficult to adapt to the requirements of different materials and processes. The parameter optimization process is time-consuming and struggles to guarantee optimality, resulting in difficulty in improving control accuracy.

[0004] In summary, the existing cold heading machine control system has low control accuracy, making it difficult to meet the requirements of modern industrial production for high precision, high efficiency, and intelligence. Summary of the Invention

[0005] This application provides a servo motor feeding control method and system for a cold heading machine, which effectively improves the control accuracy of the cold heading machine control system.

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

[0007] Firstly, a method for controlling the feeding of a servo motor in a cold heading machine is provided, applied to a feeding control system. The feeding control system includes a servo motor for the cold heading machine, a PLC controller, and a human-machine interface. The method includes:

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

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

[0010] The PLC controller parses the feeding control request, obtains the feeding control logic to be executed, and generates control instructions based on the feeding control logic;

[0011] The PLC controller compares the actual parameters with the preset template parameters, and if there are differences, it uses a parameter optimization strategy to optimize the actual parameters to obtain optimized equipment parameters. The parameter optimization strategy includes a parameter adaptive adjustment strategy based on the motor dynamic model and a machine learning optimization strategy based on historical operating data.

[0012] The PLC controller sends the control commands and the equipment optimization parameters to the driver of the cold heading machine servo motor via the industrial Ethernet. The control commands 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.

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

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

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

[0016] Based on the pre-constructed motor dynamic model and the actual parameters, the first optimal control parameters of the cold heading machine servo motor are calculated in real time. The first optimal control parameters include proportional gain, integral gain and derivative gain. The motor dynamic model is constructed based on the template parameters and includes motor torque equation and motor motion equation.

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

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

[0019] By integrating the first optimal control parameter and the second optimal control parameter, the optimized parameters of the equipment are obtained.

[0020] In another possible implementation of the first aspect, the integration of the first optimal control parameters and the second optimal control parameters to obtain the equipment optimization parameters includes:

[0021] The real-time load torque of the servo motor of the cold heading machine is obtained, and the average load torque within a preset time period is calculated based on the real-time load torque.

[0022] Based on preset weighting coefficients, the first optimal control parameter and the second optimal control parameter are weighted and summed to obtain the equipment optimization parameters;

[0023] The weighting coefficient is dynamically adjusted based on the current load torque and the average load torque of the servo motor of the cold heading machine using a preset adjustment formula, which includes:

[0024] w2 = 1 - w1;

[0025] In the formula, w1 is the weighting coefficient of the first optimal control parameter, w2 is the weighting coefficient of the second optimal control parameter, and T L T is the current load torque. La The average load torque is denoted by α, which is an adjustment factor.

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

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

[0028] When the controller PLC is successfully connected to the driver of the cold heading machine servo motor, the communication protocol type supported by the driver is determined, and the corresponding target communication driver is loaded 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 the 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 and the driver establish a communication connection.

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

[0035] The PLC controller parses the feeding control request to obtain the feeding control logic to be executed;

[0036] The PLC controller generates a feed speed curve and position control commands based on the feed control logic.

[0037] The PLC controller determines the optimal speed and optimal position of the cold heading machine servo motor based on the feed speed curve and the position control command, and generates control commands based on the optimal speed and 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 based on the feed speed curve and the position control command, and generates control commands based on the optimal speed and optimal position, including:

[0039] The PLC controller analyzes the optimal position and the maximum allowable acceleration based on the position control command;

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

[0041] The PLC controller calculates the optimal feed rate based on the position error and the maximum allowable acceleration.

[0042] The PLC controller compares the optimal feed speed with the feed speed curve. If the optimal feed speed exceeds the allowable range of the feed speed curve, the optimal feed speed is adjusted to the maximum allowable speed within the allowable range.

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

[0044] The PLC controller calculates the optimal rotational speed based on the optimal feed speed and the feed wheel radius;

[0045] The PLC controller generates control commands based on the optimal speed and the optimal position. The control commands include speed control commands and position control commands.

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

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

[0048] The PLC controller uses a 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.

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

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

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

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

[0053] The control commands of the cold heading machine servo motor are dynamically adjusted based on the output torque and feed speed curves.

[0054] Secondly, this application provides a feeding control system, comprising:

[0055] The human-machine interface is used to respond to a feeding control request sent by a user through the human-machine interface and to route the feeding control request to the PLC controller.

[0056] Servo motor for cold heading machine;

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

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

[0059] The feed control request is parsed to obtain the feed control logic to be executed, and control instructions are generated 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 the optimized parameters of the equipment. The parameter optimization strategy includes a parameter adaptive adjustment strategy based on the motor dynamic model and a machine learning optimization strategy based on historical operating data.

[0061] The control commands and the equipment optimization parameters are sent to the driver of the cold heading machine servo motor via the industrial Ethernet. The control commands are used to control the speed and position of the cold heading machine servo motor. The cold heading machine is used to configure the parameters of the cold heading machine servo motor according to the equipment optimization parameters.

[0062] The operating status of the servo motor of the cold heading machine is monitored in real time, and the operating status is displayed on the human-machine interface;

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

[0064] The above technical solution firstly replaces traditional hydraulic or mechanical transmission methods with servo motors, significantly improving the accuracy and response speed of feeding control. Servo motors feature high precision and high dynamic response, enabling fast and accurate position and speed control, meeting the high-precision machining requirements of modern industrial production. Secondly, industrial Ethernet communication technology is introduced, achieving high-speed and reliable communication between the PLC controller and the servo motor driver. By identifying the communication protocol types supported by the driver, it can flexibly adapt to different brands and models of servo motors, improving the compatibility and scalability of the feeding control system. Thirdly, by introducing parameter optimization strategies, including adaptive parameter adjustment strategies based on motor dynamic models and machine learning optimization strategies based on historical operating data, the problem of traditional PID control algorithms being unable to cope with complex nonlinear systems and rapidly changing process requirements is effectively solved. The adaptive parameter adjustment strategy based on motor dynamic models can adjust control parameters in real time to adapt to dynamic changes in system parameters, such as changes in system characteristics caused by material deformation, temperature variations, etc. The machine learning optimization strategy based on historical operating data can continuously optimize control parameters by analyzing a large amount of historical data, achieving continuous improvement in system performance. This greatly reduces the workload of manual parameter tuning, improves the efficiency and accuracy of parameter optimization, and enables the system to quickly adapt to the requirements of different materials and processes. Furthermore, the PLC controller monitors the operating status of the servo motor in real time and displays the status information on the human-machine interface, allowing operators to understand the equipment's operating status promptly. Based on the monitoring data, the system can also continuously optimize the control performance of the servo motor, achieving continuous improvement in equipment performance. This closed-loop optimization mechanism ensures that the system always maintains optimal operating conditions, improving production efficiency and product quality. In summary, 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, it effectively solves the problems of low accuracy, slow response, and poor adaptability in traditional cold heading machine control systems, significantly improving the overall performance and intelligence level of the cold heading machine.

[0065] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0066] Figure 1 A flowchart illustrating a servo motor feeding control method for a cold heading machine, provided in an embodiment of this application;

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

[0068] Figure 3 This is a schematic diagram of a feeding control system provided in an embodiment of this application. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0070] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0071] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0072] Figure 1 The illustration shows a schematic flowchart of a servo motor feeding control method for a cold heading machine according to an embodiment of this application. Figure 1 As shown in the figure, this application provides a servo motor feeding control method for a cold heading machine, which is applied to a feeding control system. The feeding control system includes a cold heading machine servo motor, a PLC controller, and a human-machine interface. The method may include the following steps.

[0073] S110. In response to receiving a feeding control request sent by the user through the human-machine interface, the human-machine interface routes the feeding control request to the controller PLC.

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

[0075] S130: The PLC controller parses the feeding control request, obtains the feeding control logic to be executed, and generates control instructions based on the feeding control logic.

[0076] S140 The PLC controller compares the actual parameters with the preset template parameters, and if there are differences, it uses parameter optimization strategies to optimize the actual parameters to obtain optimized equipment parameters. The parameter optimization strategies include a parameter adaptive adjustment strategy based on the motor dynamic model and a machine learning optimization strategy based on historical operating data.

[0077] The S150 and PLC controller send control commands and equipment optimization parameters to the driver of the cold heading machine servo motor via industrial Ethernet. The control commands 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] The S160 controller PLC monitors the operating status of the servo motor of the cold heading machine in real time and displays the operating status on the human-machine interface;

[0079] S170 and PLC controllers optimize the control performance of the servo motors in cold heading machines based on the operating status.

[0080] In the servo motor feeding control system of a cold heading machine, the first step in initiating the entire control process is for the user to send a feeding control request via a Human-Machine Interface (HMI). The HMI is typically a touchscreen or control panel through which the user inputs control commands, such as setting the feeding speed, feeding length, or selecting a specific processing mode. Once the user completes and confirms the input, the HMI generates a feeding control request containing all the parameters and commands set by the user. To ensure the request is accurately transmitted to the PLC, the HMI routes the request to the PLC via a preset communication protocol (such as Modbus TCP or EtherNet / IP). The HMI converts the user-input parameters into a data format recognizable by the PLC and sends it to the designated port of the PLC via an industrial Ethernet network. Upon receiving the request, the PLC performs preliminary data verification to ensure the integrity and validity of the request. If the request format is correct and the parameters are within the allowed range, the PLC proceeds to the next processing step.

[0081] Upon receiving a feed control request, the PLC controller needs to establish a communication connection with the driver of the cold heading machine's servo motor to obtain the motor's actual parameters and identify the communication protocol types supported by the driver. This process first involves the automatic identification of the communication protocol. The PLC will attempt to use various communication protocols (such as Modbus TCP, EtherCAT, PROFINET, and EtherNet / IP) to establish a handshake communication with the driver via Industrial Ethernet. Each protocol has its specific handshake signals 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 sends parameter read commands to the driver via the established communication connection to obtain the motor's actual parameters, including current speed, position, torque, and temperature. These parameters are real-time data, reflecting the motor's current operating status. To ensure data accuracy, the PLC performs multiple reads and data verifications, eliminating outliers. The obtained parameters will be used for subsequent control command generation and parameter optimization. Ultimately, efficient communication between the PLC and the driver is achieved, ensuring the real-time performance and accuracy of the control system. By automatically identifying the communication protocol, it can adapt to different driver models, improving system compatibility and flexibility.

[0083] After obtaining the actual parameters of the servo motor of the cold heading machine, the PLC controller needs to parse the feeding 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 extracts the parameters in the request (such as feeding speed and feeding length) and converts them into specific control tasks according to preset control logic. For example, if the user sets the feeding length to 100 mm, the PLC calculates the distance to be moved based on the motor's current position and target position, and generates a smooth feed speed curve by combining the motor's maximum acceleration and speed limit. This curve describes the change in speed over time as the motor moves from its current position to the target position. To ensure control accuracy, the PLC also considers the motor's dynamic characteristics, such as inertia and load changes, and optimizes the speed curve accordingly.

[0084] Next, the PLC generates specific control commands based on the feed rate curve, including speed control commands and position control commands. These commands 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. This ultimately achieves precise conversion from user commands to specific control commands, ensuring the smoothness and accuracy of motor operation. By generating optimized speed curves, the system can reduce motor start-up and shutdown shocks, extend equipment life, and improve machining accuracy.

[0085] Before generating control commands, the PLC compares the acquired actual parameters with preset template parameters to determine if optimization is needed. Template parameters are pre-defined ideal parameters that reflect the motor's operating characteristics under optimal conditions. The comparison process involves multiple parameters, including speed, torque, and temperature. If discrepancies exist between the actual and template parameters, the PLC initiates a parameter optimization strategy. This strategy includes two main methods: a parameter adaptive adjustment strategy based on the motor's dynamic model and a machine learning optimization strategy based on historical operating data. The motor dynamic model-based strategy calculates optimal control parameters, such as proportional gain, integral gain, and derivative gain, in real time using a pre-built motor dynamic model (including the motor torque equation and motor motion equation). These parameters are used to adjust the motor's control performance to bring it closer to its ideal state. The machine learning optimization strategy based on historical operating data uses a pre-trained optimization model to predict optimal control parameters based on the motor's historical operating data. The optimization model is trained on a large amount of historical data and can capture the motor's nonlinear characteristics and complex dynamic behavior. The PLC integrates the optimal control parameters obtained from both strategies to generate optimized equipment parameters. During integration, the PLC dynamically adjusts the weighting coefficients based on the motor's current load torque and average load torque to ensure the adaptability and stability of the optimized 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 optimal operating condition.

[0086] After generating control commands and optimized equipment parameters, the PLC controller needs to send this data to the driver of the servo motor in the cold heading machine via Industrial Ethernet. The transmission process first involves data encapsulation and transmission. The PLC packages the control commands and optimized parameters into data packets conforming to the communication protocol and sends them to the designated port of the driver via Industrial Ethernet. The data packets contain speed control commands, position control commands, and optimized control parameters (such as proportional gain, integral gain, and derivative gain). Upon receiving the data packets, the driver decodes and verifies them to ensure data integrity and correctness. Next, the driver configures the motor parameters according to the optimized parameters, adjusting the motor's control algorithm to ensure it operates according to the optimized parameters. Simultaneously, the driver controls the motor's speed and position according to the control commands, ensuring the motor operates at the predetermined speed and position. Ultimately, this achieves accurate transmission and execution of control commands and optimized parameters, ensuring the precision and stability of motor operation, and improving processing accuracy and production efficiency.

[0087] During motor operation, the PLC controller needs to monitor its operating status in real time to ensure system stability and safety. The monitoring process involves the real-time acquisition and analysis of multiple parameters, including motor speed, position, torque, current, temperature, and vibration frequency. The PLC acquires this data from the driver via industrial Ethernet and processes and analyzes it in real time. For example, the PLC calculates the motor's load fluctuations, assesses the motor's health status, and determines if any abnormalities exist based on preset thresholds. If an abnormality is detected (such as excessively high temperature or abnormal vibration frequency), the PLC immediately issues an alarm and takes corresponding protective measures. Simultaneously, the PLC displays the motor's operating status in real time on a human-machine interface (HMI) for operator viewing. The display includes the motor's current speed, position, load status, and health index. Operators can understand the motor's operating status in real time through the HMI and make adjustments as needed. This step achieves real-time monitoring and visualization of the motor's operating status, improving system safety and operability. Real-time monitoring allows for the timely detection and handling of potential problems, preventing equipment damage and production interruptions.

[0088] Based on real-time monitoring of the motor's operating status, the PLC controller further optimizes the motor's control performance. This optimization process involves several aspects, including load fluctuation compensation, speed curve adjustment, and dynamic optimization of control parameters. First, the PLC dynamically adjusts control commands based on the motor's load fluctuations and health index to compensate for the impact of load fluctuations on motor operation. For example, if a large load fluctuation is detected, the PLC increases the motor's output torque to ensure speed and position stability. Second, the PLC adjusts the feed speed curve based on the motor's health status to prevent the motor from operating under abnormal conditions. For example, if the motor temperature is too high, the PLC reduces the feed speed to decrease the motor load. Finally, the PLC dynamically optimizes control parameters based on real-time operating data to ensure the motor is always in optimal operating condition. This step achieves real-time optimization of motor control performance, improving system stability and machining accuracy. By dynamically adjusting control commands and parameters, the system can adapt to complex working environments and varying load conditions, ensuring efficient motor operation.

[0089] This embodiment first replaces the traditional hydraulic or mechanical transmission method with a servo motor, significantly improving the accuracy and response speed of the feeding control. Servo motors are characterized by high precision and high dynamic response, enabling fast and accurate position and speed control, meeting the high-precision machining requirements of modern industrial production. Secondly, industrial Ethernet communication technology is introduced, achieving high-speed and reliable communication between the PLC controller and the servo motor driver. By identifying the communication protocol types supported by the driver, it can flexibly adapt to different brands and models of servo motors, improving the compatibility and scalability of the feeding control system. By introducing parameter optimization strategies, including a parameter adaptive adjustment strategy based on the motor dynamic model and a machine learning optimization strategy based on historical operating data, the problem of traditional PID control algorithms being unable 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 control parameters in real time to adapt to dynamic changes in system parameters, such as changes in system characteristics caused by material deformation, temperature changes, etc. The machine learning optimization strategy based on historical operating data can continuously optimize control parameters by analyzing a large amount of historical data, achieving continuous improvement in system performance, greatly reducing the workload of manual parameter tuning, improving the efficiency and accuracy of parameter optimization, and enabling the system to quickly adapt to the requirements of different materials and processes. Furthermore, the PLC controller monitors the servo motor's operating status in real time and displays the information on the human-machine interface, allowing operators to promptly understand the equipment's operating conditions. Based on the monitoring data, the system can continuously optimize the servo motor's control performance, achieving continuous improvement in equipment performance. This closed-loop optimization mechanism ensures the system always maintains optimal operating conditions, improving production efficiency and product quality. In summary, this technical solution comprehensively enhances 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, it effectively solves the problems of low accuracy, slow response, and poor adaptability inherent in traditional cold heading machine control systems, significantly improving the overall performance and intelligence level of the cold heading machine.

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

[0091] S210. Based on the pre-constructed motor dynamic model and actual parameters, calculate the first optimal control parameters of the servo motor of the cold heading machine in real time. The first optimal control parameters include proportional gain, integral gain and derivative gain. The motor dynamic model is constructed based on template parameters and includes motor torque equation and motor motion equation.

[0092] S220, Machine learning optimization strategies based on historical operational data include:

[0093] S230. Input the actual parameters into the pre-trained optimization model to predict the second optimal control parameters. The optimization model is trained based on the historical operating data of the servo motor of the cold heading machine.

[0094] S240. Integrate the first optimal control parameters and the second optimal control parameters to obtain the optimized parameters for the equipment.

[0095] In the control process of the servo motor of a cold heading machine, 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-constructed mathematical model used to describe the physical characteristics and dynamic behavior of the motor. This 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, and 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 equations of motion for an electric motor describe the relationship between motor speed, torque, and load. The formula can be expressed as:

[0098]

[0099] Where J is the moment of inertia of the motor, ω is the motor speed, and T is the rotational speed. L It is the load torque.

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

[0101] In practical applications, the PLC controller collects real-time parameters of the motor, including current speed, current, torque, and load. These parameters are input into the motor's dynamic model, and the motor's real-time state is obtained through numerical calculations. For example, if the current speed is 1000 rpm, the current is 5A, and the load torque is 10 N·m, the PLC will substitute these data into the motor torque equation and motion equation to calculate the motor's dynamic response. Next, the PLC will adjust the control parameters in real-time based on the motor's dynamic response, including the proportional gain Kp, integral gain Ki, and derivative gain Kd. These parameters are the core parameters of the PID controller, used to adjust the motor's control performance. The proportional gain determines the controller's response speed to errors, the integral gain is used to eliminate steady-state errors, and the derivative gain is used to suppress system oscillations. By calculating and adjusting these parameters in real time, the PLC can ensure that the motor maintains a stable operating state under different operating conditions. Ultimately, real-time optimization of motor control parameters is achieved, improving the system's control accuracy and response speed. By dynamically adjusting the control parameters, the system can adapt to different working conditions and load changes, ensuring that the motor is always in its optimal operating state.

[0102] In the control process of the servo motor of a cold heading machine, the core of the machine learning optimization strategy based on historical operating data is to train the optimization model using a large amount of historical operating data, thereby predicting the optimal control parameters. Historical operating data includes parameters such as motor speed, current, torque, temperature, and vibration frequency, which reflect the motor's operating characteristics under different working conditions. By analyzing this data, the nonlinear characteristics and complex dynamic behavior of the motor can be captured, thus constructing a machine learning model capable of predicting the optimal control parameters.

[0103] In practical applications, the first step is to collect a large amount of historical operating data. This data can be acquired in real time through sensors and stored in a database. Next, machine learning algorithms (such as neural networks, support vector machines, or random forests) are used to train this data and build an optimization model. During training, the model learns how to predict optimal control parameters based on the motor's current state. For example, if the current speed is 1200 rpm and the load torque is 15 N·m, the model will predict the corresponding proportional gain, integral gain, and derivative gain. After training, the optimization model can be deployed to the PLC controller for real-time prediction of optimal control parameters. Ultimately, this enables intelligent optimization of motor control parameters. By utilizing historical operating data, it can better adapt to complex working environments and changing load conditions, ensuring efficient motor operation.

[0104] In machine learning optimization strategies based on historical operating data, actual parameters include the motor's current speed, current, torque, temperature, and vibration frequency, reflecting the motor's real-time operating status. The pre-trained optimization model is obtained through training on a large amount of historical operating data and can predict the optimal control parameters based on the motor's current state. In practical applications, the PLC controller collects the motor's actual parameters in real time and inputs them into the optimization model. The model calculates based on the input parameters and outputs the predicted optimal control parameters, including proportional gain, integral gain, and derivative gain.

[0105] For example, suppose the current speed is 1500 rpm, the current is 6 A, the load torque is 20 N·m, the temperature is 50 °C, and the vibration frequency is 100 Hz. These parameters will be input into the optimization model, which will predict the corresponding proportional gain, integral gain, and derivative gain based on its internal calculation rules. Assume the model predicts a proportional gain of 2.5, an integral gain of 0.8, and a derivative gain of 1.2. These parameters will be used to adjust the motor's control algorithm to ensure the motor maintains stable operation under the current conditions.

[0106] In the control process of the servo motor of the cold heading machine, the first optimal control parameter is calculated in real time based on the motor's dynamic model, reflecting the motor's dynamic characteristics. The second optimal control parameter is predicted based on a machine learning optimization strategy using historical operating data, reflecting the motor's historical operating characteristics. To obtain the optimal equipment parameters, these two parameters need to be integrated. The integration process involves the dynamic adjustment of weighting coefficients, which determine the proportion of the two parameters in the final optimized parameters.

[0107] In practical applications, the PLC controller dynamically adjusts the weighting coefficients based on the motor's current load torque and average load torque. For example, assuming the current load torque T... L The average load torque is 18 N·m. La The value is 15 N·m, and the adjustment factor α is 0.5. The adjustment formula is as follows:

[0108]

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

[0110] This implementation significantly improves the control performance of the servo motor in a cold heading machine by employing a parameter adaptive adjustment strategy based on a motor dynamic model and a machine learning optimization strategy based on historical operating data. The motor dynamic model reflects the motor's dynamic characteristics in real time, ensuring real-time optimization of control parameters; the machine learning optimization strategy utilizes historical operating data to predict optimal control parameters, enhancing the system's adaptability. The combination of these two strategies allows for rapid response to external changes and adaptation to complex working environments and varying load conditions. By dynamically adjusting the weighting coefficients, the advantages of both strategies are combined to ensure the motor is always in its optimal operating state. Ultimately, the system's control accuracy, response speed, and stability are significantly improved, ensuring the efficient operation and machining precision of the cold heading machine.

[0111] In one embodiment of this invention, integrating the first optimal control parameters and the second optimal control parameters to obtain the equipment optimization parameters includes the following steps:

[0112] S310. Obtain 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 based on the real-time load torque.

[0113] S320. According to the preset weighting coefficients, the first optimal control parameter and the second optimal control parameter are weighted and summed to obtain the equipment optimization parameters;

[0114] The weighting coefficient is dynamically adjusted based on the current load torque and average load torque of the servo motor of the cold heading machine using a preset adjustment formula. The adjustment formula includes:

[0115] w2 = 1 - w1;

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

[0117] In the control process of the servo motor of a cold heading machine, real-time load torque refers to the load torque borne by the motor at the current moment, which is usually measured directly by a sensor or driver. To gain a more comprehensive understanding of the motor's load characteristics, it is also necessary to calculate the average load torque over a preset time period. Average load torque refers to the average value of the motor's load torque within a certain time range, reflecting the trend of load changes. In practical applications, the PLC controller collects the motor's load torque data in real time and stores 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 from the past 10 seconds. Each time new load torque data is collected, the PLC adds it to the buffer and removes the oldest data to keep the buffer size constant. Next, the PLC calculates the average value of all data in the buffer to obtain the average load torque. For example, assuming the buffer stores 10 load torque data points, T1, T2, ..., T... 10 Then the average load torque T La It can be calculated as follows:

[0118]

[0119] In this way, the PLC can acquire the motor's load torque information in real time and calculate its average load torque, providing data support for subsequent weighting coefficient adjustments.

[0120] After obtaining the real-time and average load torque of the servo motor in the cold heading machine, the PLC controller needs to dynamically adjust the weighting coefficients based on this data and perform a weighted sum of the first and second optimal control parameters to obtain the final optimized equipment parameters. The weighting coefficients determine the proportion of the two control parameters in the final optimized parameters. The adjustment factor α is usually set according to the specific application scenario to control the rate of change of the weighting coefficients. For example, assuming the current load torque T... L The average load torque is 18 N·m. La Given a value of 15 N·m and an adjustment factor α of 0.5, the weighting coefficient w_1 can be calculated as follows:

[0121]

[0122] The weighting coefficient w2 is w2 = 1 - 0.82 = 0.18. Next, the PLC will perform a weighted summation of the first and second optimal control parameters based on the weighting coefficient to obtain the final optimized equipment parameters, as detailed in S240, which will not be repeated here. By dynamically adjusting the weighting coefficient, the system can adapt to different working conditions and load changes, ensuring that the motor is always in the optimal operating state.

[0123] This implementation significantly improves the control performance of the servo motor in the cold heading machine by integrating the first and second optimal control parameters. The calculation of real-time load torque and average load torque provides a crucial basis for the dynamic adjustment of weighting coefficients, enabling the system to flexibly adjust the weighting of control parameters based on the motor's current state and load variation trends. The first optimal control parameter, based on the motor's dynamic model, reflects the motor's dynamic characteristics in real time, ensuring real-time optimization of control parameters. The second optimal control parameter, based on a machine learning optimization strategy using historical operating data, predicts optimal control parameters using historical data, improving the system's adaptability. The weighted summation of the two control parameters allows the system to combine the advantages of both strategies, ensuring the motor maintains a stable operating state under different working conditions.

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

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

[0126] S420. If the controller PLC is successfully connected to the driver of the cold heading machine servo motor, determine the communication protocol type supported by the driver and load the corresponding target communication driver based on the communication protocol type.

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

[0128] Figure 2 This paper illustrates a schematic diagram of the communication connection process between the controller PLC and the driver provided in an embodiment of this application. Figure 2As shown, in the control system of the servo motor of a cold heading machine, the communication connection between the PLC controller and the driver is the foundation for achieving precise control. Since different driver models may support different communication protocols, the PLC needs to attempt to establish a connection with the driver using multiple communication protocols sequentially. Commonly used communication protocols include Modbus TCP, EtherCAT, PROFINET, and EtherNet / IP. Each protocol has its 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 clocking and high-speed data transmission; PROFINET is an industrial Ethernet protocol that supports real-time communication and device diagnostics; and EtherNet / IP is a CIP (Common Industrial Protocol)-based protocol that supports device configuration and data exchange. In practical applications, the PLC will attempt to establish a connection with the driver using these protocols in a predetermined order. Each time it attempts, the PLC sends a handshake signal and waits for the driver's response. If the driver responds successfully, the PLC records the protocol type and stops trying other protocols. For example, suppose the PLC first attempts to use the Modbus TCP protocol to send a request to read a register address. If the driver returns correct response data within a preset time, the connection is successful. If no response is received or the response data is incorrect, the PLC will continue to attempt to use the EtherCAT protocol, and so on, until a protocol type supported by the driver is found. This step achieves efficient communication between the PLC and the driver, ensuring the real-time performance and accuracy of the control system. By automatically identifying the communication protocol, the system can adapt to different driver models, improving system compatibility and flexibility.

[0129] After successfully establishing a communication connection with the driver of the servo motor in the cold heading machine, the PLC controller needs to determine the communication protocol type 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 practical applications, the PLC determines the protocol type supported by the driver based on the response data of the handshake signal. For example, if a connection is successfully established using the Modbus TCP protocol, the PLC will record the protocol type and load the Modbus TCP communication driver. The driver is a software module used to implement the communication functions of a specific protocol, including data encapsulation, transmission, and parsing. After loading the driver, the PLC initializes communication parameters, such as IP address, port number, and communication rate, to ensure normal communication with the driver. For example, assuming the driver supports the EtherCAT protocol, the PLC will load the EtherCAT driver and initialize communication parameters, such as setting the distributed clock and configuring the data frame structure. Ultimately, precise 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 fully utilize the characteristics of the protocol, improving communication efficiency and control performance.

[0130] After determining the communication protocol types supported by the driver and loading the target communication driver, the PLC controller needs to establish a stable communication connection with the driver of the cold heading machine servo motor based on the target communication driver and Industrial Ethernet. First, the PLC initializes the Industrial Ethernet communication parameters through the target communication driver, including IP address, port number, and communication speed. For example, if using the PROFINET protocol, the PLC will set the IP address to 192.168.1.1, the port number to 34964, and the communication speed to 100Mbps. Next, the PLC sends a handshake signal to the driver 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 driver returns the correct response data within a preset time, the handshake is successful, and the communication link is established. For example, assuming the EtherNet / IP protocol is used, the PLC will send a CIP connection request, and the driver will return a CIP connection confirmation, indicating a successful handshake. Once the communication link is established, the PLC can exchange data with the driver via Industrial Ethernet, including sending control commands and receiving status information. This step ensures stable communication between the PLC and the driver, guaranteeing the real-time performance and reliability of the control system. By establishing a stable communication link, the system can quickly respond to external changes, improving control accuracy and production efficiency.

[0131] This implementation method attempts to establish communication connections with the servo motor driver of the cold heading machine sequentially using different communication protocols. The PLC controller can automatically identify the protocol types supported by the driver and load the corresponding target communication driver. This process ensures efficient communication between the PLC and the driver, improving system compatibility and flexibility. After determining the communication protocol type and loading the driver, the PLC can initialize 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. Ultimately, the control accuracy, response speed, and stability of the feeding control system are significantly improved, ensuring the efficient operation and processing accuracy of the cold heading machine.

[0132] In one embodiment of this invention, 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 industrial Ethernet communication parameters through the target communication driver. The communication parameters include IP address, port number, and communication rate.

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

[0135] S530. If the handshake is successful, the controller PLC and the driver establish a communication connection.

[0136] In the control system of the servo motor in a cold heading machine, the communication connection between the PLC controller and the driver is fundamental to achieving precise control. To ensure the stability and efficiency of communication, the PLC needs to initialize the industrial Ethernet communication parameters through the target communication driver. Communication parameters include IP address, port number, and communication rate, which determine the configuration and performance of the communication link. The IP address is a unique identifier for the device in the network, ensuring accurate data transmission to the target device. The port number distinguishes different communication services, ensuring data is received and processed by the correct application. The communication rate determines the data transmission speed, typically measured in Mbps (megabits per second). In practical applications, the PLC sets appropriate communication parameters based on the driver configuration and network environment. For example, assuming the driver's IP address is 192.168.1.2, port number is 502, and communication rate is 100Mbps, the PLC will configure these parameters in the target communication driver. The initialization process involves several steps. First, the PLC checks the validity of the IP address to ensure it conforms to network specifications. Next, the PLC configures the port number, ensuring it matches the driver's port number. Finally, the PLC sets the communication rate to ensure it matches the network device's speed. For example, if the network switch supports 100Mbps, the PLC will set the communication rate to 100Mbps to ensure stable data transmission. This step achieves precise configuration of communication parameters, ensuring the stability and efficiency of the communication link. By initializing communication parameters, the system can quickly establish a communication connection, improving the control system's response speed and reliability.

[0137] After initializing the industrial Ethernet communication parameters, the PLC controller 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 using the Modbus TCP protocol, the handshake signal might be a request to read a register address; if using the EtherCAT protocol, the handshake signal might be a distributed clock synchronization request. In practical applications, the PLC controller generates the corresponding handshake signal based on the configuration of the target communication driver and sends it to the driver via the industrial Ethernet. For example, assuming the Modbus TCP protocol is used, the PLC will generate a request to read register address 0x0001, with the data packet format as follows: transaction identifier (2 bytes), protocol identifier (2 bytes), length field (2 bytes), unit identifier (1 byte), function code (1 byte), and register address (2 bytes). The PLC will send this data packet to the driver's IP address and port number and wait for the driver's response. If the driver returns the correct response data within a preset time, the handshake is successful. For example, the data packet format 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), and register data (2 bytes). This step effectively tests the connectivity of the communication link, ensuring 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, improving the response speed and reliability of the control system.

[0138] After successfully sending a handshake signal and receiving a correct response from the driver, the communication connection between the PLC controller and the driver of the cold heading machine servo motor is formally established. The establishment of this connection means that the PLC can exchange data with the driver via industrial Ethernet, including sending control commands and receiving status information. In practical applications, establishing a communication connection involves several steps. First, the PLC confirms the availability of the communication link based on the response data of the handshake signal. For example, if using the Modbus TCP protocol, the PLC checks the transaction identifier, protocol identifier, function code, and register data in the returned data packet to ensure they match the sent request. Next, the PLC initializes the communication buffer to store 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 receive buffer to 1024 bytes. Finally, the PLC starts a communication thread for real-time processing of data transmission and reception. The communication thread is an independent execution unit used to ensure the real-time and efficient transmission of data. For example, the PLC creates a high-priority communication thread to handle the sending of control commands and the receiving of status information.

[0139] This implementation method enables stable and efficient communication between the PLC controller and the driver of the servo motor in the cold heading machine by initializing the communication parameters of the industrial Ethernet, sending handshake signals, and establishing a communication connection. Initializing the communication parameters ensures the configuration and performance of the communication link, sending and responding to the handshake signals confirms the availability of the communication link, and establishing the communication connection enables real-time data exchange, ultimately improving control accuracy and production efficiency. By accurately configuring communication parameters, quickly confirming the availability of the communication link, and processing data transmission and reception in real time, it can quickly respond to external changes and adapt to complex working environments and varying load conditions.

[0140] In one embodiment of this invention, the PLC parses the feed control request to obtain the feed control logic to be executed, and generates control instructions based on the feed control logic, including the following steps:

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

[0142] S620, the PLC controller generates the feed speed curve and position control instructions based on the feed control logic;

[0143] The S630 and PLC controller determine the optimal speed and position of the servo motor of the cold heading machine based on the feed speed curve and position control instructions, and generate control instructions based on the optimal speed and position.

[0144] In the control system of the servo motor of a cold heading machine, parsing the feed control request is the first step in generating control instructions. The feed control request is sent by the user through a Human-Machine Interface (HMI) and contains user-defined feed parameters, such as feed length, feed speed, and processing mode. The PLC controller needs to parse these parameters and convert them into specific feed control logic. The parsing process first involves data format conversion. Feed control requests are usually transmitted in a specific data format (such as JSON or XML), which the PLC needs to parse into an internal data structure. For example, assuming 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 continuous feed mode. Next, the PLC generates the feed control logic based on the parsed parameters. The feed control logic is a sequence of instructions describing how the motor operates, including stages such as start-up, acceleration, constant speed, deceleration, 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 phases. The acceleration phase accelerates from 0 to 500 mm / s, the constant speed phase maintains the speed at 500 mm / s, and the deceleration phase decelerates from 500 mm / s to 0. By parsing the feed control request, the system can quickly respond to the user's operational intentions, improving the flexibility and adaptability of the control system.

[0145] After parsing the feed control request and obtaining the feed control logic, the PLC generates a feed speed curve and position control instructions based on the feed control logic. The feed speed curve describes the change of the motor speed over time during the feeding process, while the position control instructions describe the target position the motor needs to reach. Generating the feed speed curve involves several steps. First, the PLC calculates the time point and speed value for each stage based on the acceleration, constant speed, and deceleration stages in the feed control logic. For example, assuming a feed length of 100 mm, a feed speed of 500 mm / s, an acceleration time of 0.2 seconds, and a deceleration time of 0.2 seconds, the PLC will generate a feed speed curve including 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. Next, the PLC generates position control instructions based on the feed speed curve. The position control instructions describe the target position the motor needs to reach at each time point. For example, assuming the motor's acceleration phase occurs at 0.1 seconds and its speed is 250 mm / s, the PLC will calculate the target position of the motor at 0.1 seconds as 12.5 mm. By generating feed rate curves and position control commands, the system can quickly respond to external changes, improving control accuracy and production efficiency.

[0146] After generating the feed rate curve and position control commands, the PLC controller needs to determine the optimal speed and position of the servo motor in the cold heading machine based on this data, and then generate the final control commands. The optimal speed and position are the target values ​​that the motor needs to achieve during the feeding process to ensure that the motor can operate at the predetermined speed and position. 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 rate curve. For example, assuming the feed rate curve shows a speed of 250 mm / s at 0.1 seconds, the PLC calculates the target motor speed to be 250 mm / s. Next, the PLC converts the target speed into motor speed based on the feed wheel radius. For example, assuming the feed wheel radius is 50 mm, the PLC calculates the target motor speed as follows:

[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 command. For example, assuming the target position of the position control command 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 control commands based on the optimal speed and optimal position. These control commands include speed control commands and position control commands, used to control the motor's speed and position. For example, the PLC will generate a control command containing a target speed of 0.796 rpm and a target position of 12.5 mm, and send it to the driver via industrial Ethernet. This ultimately achieves precise control of the motor's operation, ensuring the smoothness and accuracy of the feeding process. By generating control commands for optimal speed and optimal position, the system can quickly respond to external changes, improving control accuracy and production efficiency.

[0150] This implementation method, by parsing the feed control request, generating the feed speed curve and position control command, determining the optimal speed and position, and generating control commands, enables the PLC controller to achieve high-precision control of the servo motor of the cold heading machine. Parsing the feed control request ensures the accurate conversion of user instructions into specific control logic; generating the feed speed curve and position control command ensures the smoothness and accuracy of motor operation; and determining the optimal speed and position and generating control commands 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 precisely controlling the motor speed and position, the feed control system can quickly respond to external changes and adapt to complex working environments and varying load conditions.

[0151] In one embodiment of this invention, the PLC determines the optimal speed and optimal position of the servo motor of the cold heading machine based on the feed speed curve and position control commands, and generates control commands based on the optimal speed and optimal position, including the following steps:

[0152] S710 and PLC controllers analyze the optimal position and maximum allowable acceleration based on the position control instructions.

[0153] The S720 controller PLC obtains the current position of the servo motor of the cold heading machine and calculates the position error based on the optimal position and the current position. The position error is the difference between the current position and the optimal position.

[0154] The S730 controller PLC calculates the optimal feed rate based on the position error and the maximum allowable acceleration.

[0155] The S740 controller PLC compares the optimal feed rate with the feed rate curve. If the optimal feed rate exceeds the allowable range of the feed rate curve, it adjusts the optimal feed rate to the maximum allowable speed within the allowable range.

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

[0157] The S760 controller PLC calculates the optimal speed based on the optimal feed rate and feed wheel radius.

[0158] The S770 controller PLC generates control instructions based on the optimal speed and optimal position. These control instructions include speed control instructions and position control instructions.

[0159] In the control system of the servo motor of a cold heading machine, the position control command is a key parameter describing the target position the motor needs to achieve. The PLC controller needs to parse the position control command to determine the optimal position and the maximum allowable acceleration to ensure that the motor runs along the predetermined path and speed. The optimal position is the target position the motor needs to reach during the feeding process, usually set by the user through the human-machine interface. For example, assuming the user sets the feeding length to 100 mm, the PLC will parse the optimal position as 100 mm. The maximum allowable acceleration is the maximum acceleration value the motor can withstand during acceleration and deceleration, usually determined by the motor's physical characteristics and load conditions. For example, assuming the motor's maximum acceleration is 5000 mm / s², the PLC will parse the maximum allowable acceleration as 5000 mm / s². The parsing process involves data format conversion and parameter extraction. Position control commands are usually transmitted in a specific data format (such as JSON or XML), which the PLC needs to parse into its internal data structure. For example, assuming 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 allowable acceleration of 5000 mm / s².

[0160] After determining the optimal position, the PLC controller needs to acquire the current position of the servo motor of the cold heading machine and calculate the position error. The current position is the motor's real-time position during the feeding process, typically measured by an encoder or sensor. For example, assuming the motor's current position is 50 mm, the PLC will acquire the current position as 50 mm. The position error is the difference between the current position and the optimal position, used to assess whether the motor's operating state needs adjustment. For example, assuming 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 acquires the current position data from the driver via industrial Ethernet and compares it with the optimal position. For example, assuming the current position data is 50 mm and the optimal position is 100 mm, the PLC will calculate the position error as 50 mm, achieving real-time monitoring and error assessment of the motor position, ensuring the accuracy and stability of motor operation.

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

[0162]

[0163] Next, the PLC adjusts the target speed based on the motor's physical characteristics and load conditions. For example, assuming the motor's maximum speed 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, improving control accuracy and production efficiency.

[0164] After calculating the optimal feed rate, the PLC controller compares it with the feed rate curve to ensure that the optimal feed rate is within the allowable range. The feed rate curve describes the change in motor speed over time during the feeding process and is typically set by the user through a human-machine interface (HMI). For example, assuming the allowable range of the feed rate curve is 0 to 1000 mm / s, the PLC will compare the optimal feed rate of 707.11 mm / s with the feed rate curve. If the optimal feed rate exceeds the allowable range, the PLC will adjust it to the maximum allowable speed within that range. For example, assuming the optimal feed rate is 1200 mm / s, exceeding 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 PLC controller needs to obtain the feed wheel radius of the cold heading machine servo motor to convert the optimal feed speed into the optimal rotational speed. The feed wheel radius is the physical size of the motor's feed wheel, typically provided by the equipment manufacturer. For example, assuming the feed wheel radius is 50 mm, the PLC will obtain a feed wheel radius of 50 mm. The PLC can obtain the feed wheel radius data from the driver via industrial Ethernet and store it in its 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 PLC controller needs to calculate the optimal rotational speed based on the optimal feed speed and feed wheel radius. The optimal rotational speed is the target speed the motor needs to achieve during the feeding process to ensure that the motor can operate at the predetermined speed and position. Calculating the optimal rotational speed involves several steps. First, the PLC calculates the target rotational 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 rotational speed of the motor as follows:

[0167]

[0168] Next, the PLC will adjust the target speed based on the motor's physical characteristics and load conditions. For example, assuming the motor's maximum speed 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 position, the PLC controller needs to generate control instructions based on this data. These control instructions include speed control instructions and position control instructions, used to control the motor's speed and position. The process of generating control instructions involves several steps. First, the PLC generates a speed control instruction based on the optimal speed. For example, assuming the optimal speed is 2.25 rpm, the PLC will generate a speed control instruction containing the target speed of 2.25 rpm. Next, the PLC generates a position control instruction based on the optimal position. For example, assuming the optimal position is 100 mm, the PLC will generate a position control instruction containing the target position of 100 mm. Finally, the PLC sends the speed control instructions and position control instructions to the driver via an industrial Ethernet network. For example, the PLC might generate a control instruction containing the target speed of 2.25 rpm and the target position of 100 mm and send it to the driver via an industrial Ethernet network. This ultimately achieves precise control of the motor's operation, ensuring the smoothness and accuracy of the feeding process.

[0170] This implementation method achieves high-precision control of the servo motor of the cold heading machine by analyzing the optimal position and maximum allowable acceleration, 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 commands. Analyzing the optimal position and maximum allowable acceleration ensures the smoothness and accuracy of motor operation; obtaining the current position and calculating the position error enables real-time monitoring and error assessment of the motor position; calculating and adjusting the optimal feed speed ensures precise control of the motor speed; obtaining the feed wheel radius and calculating the optimal speed enables precise control of the motor speed; and generating control commands ensures that the motor operates at the predetermined speed and position. This process improves the response speed and reliability of the control system, ensuring the efficient operation and machining accuracy of the cold heading machine. By precisely controlling the motor's speed and position, the system can quickly respond to external changes and adapt to complex working environments and varying load conditions.

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

[0172] The S810 controller PLC uses a load fluctuation calculation formula to calculate the load fluctuation of the servo motor of the cold heading machine based on the load torque and the motor output torque.

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

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

[0175] In the control system of the servo motor of a cold heading machine, load fluctuation is an important indicator reflecting changes in motor load. Load fluctuation refers to the magnitude of change in load torque during motor operation, typically calculated as the difference between the load torque and the motor output torque. Load torque is the external load torque borne by the motor, usually measured directly by sensors or drivers. Motor output torque is the actual torque output by the motor, typically calculated from the motor's current and speed. In practical applications, the PLC controller collects load torque and motor output torque data in real time and inputs them into the load fluctuation calculation formula. The load fluctuation calculation formula can typically be expressed as:

[0176] ΔT=|T L -T M | where ΔT is the load fluctuation, T L It is the load torque, T M This refers to the motor output torque. For example, assuming the load torque is 20 N·m and the motor output torque is 18 N·m, the PLC will calculate the load fluctuation as 2 N·m. The PLC obtains the load torque and motor output torque data from the driver via industrial Ethernet and substitutes them into the load fluctuation calculation formula. For example, assuming the load torque is 20 N·m and the motor output torque is 18 N·m, the PLC will calculate the load fluctuation as 2 N·m. By calculating the load fluctuation, the system can quickly respond to external changes, improving control accuracy and production efficiency.

[0177] In the control system of the servo motor of a cold heading machine, the health index is an important indicator reflecting the motor's operating status. The health index refers to the motor's health status during operation and is typically calculated using current data, temperature data, and vibration frequency data. Current data refers to the motor's operating current value, usually measured by a current sensor. Temperature data refers to the motor's operating temperature value, usually measured by a temperature sensor. Vibration frequency data refers to the motor's operating vibration frequency, usually measured by a vibration sensor. In practical applications, the PLC controller collects current, temperature, and vibration frequency data in real time and inputs them into the health index calculation formula. The health index calculation formula can typically 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 weighting coefficients. For example, assuming the current data is 10A, the temperature data is 50℃, and the vibration frequency data is 100Hz, with weighting coefficients of 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 acquires the current, temperature, and vibration frequency data from the sensors via industrial Ethernet and substitutes them into the health index calculation formula.

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

[0181] First, the PLC adjusts the motor's output torque based on load fluctuations. For example, assuming a load fluctuation of 2 N·m, the PLC increases the motor's output torque by 2 N·m to compensate for the load fluctuation. Next, the PLC adjusts the feed rate curve based on a health index. For example, assuming a health index of 40, the PLC decreases the slope of the feed rate curve to reduce the motor load. Finally, the PLC generates new control commands based on the adjusted output torque and feed rate curves. For example, assuming an adjusted output torque of 20 N·m and a feed rate curve slope of 0.5, the PLC generates a control command containing the target output torque of 20 N·m and the target feed rate curve slope of 0.5, and sends it to the driver via industrial Ethernet. This ultimately achieves dynamic adjustment of the motor control commands, ensuring the stability and reliability of motor operation. By dynamically adjusting control commands, the system can quickly respond to external changes, improving control accuracy and production efficiency.

[0182] This implementation method achieves high-precision control of the servo motor of the cold heading machine by calculating load fluctuations and health indices, and dynamically adjusting control commands. Calculating load fluctuations ensures real-time monitoring and evaluation of motor load fluctuations, calculating the health index enables real-time monitoring and evaluation of the motor's health status, and dynamically adjusting control commands ensures the stability and reliability of motor operation. This process improves the response speed and reliability of the control system, ensuring the efficient operation and machining accuracy of the cold heading machine. By precisely controlling the motor's output torque and feed speed curves, the system can quickly respond to external changes and adapt to complex working environments and varying load conditions.

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

[0184] S910, the PLC controller 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 and PLC controllers adjust the feed rate curve according to the health index and the preset feed rate adjustment formula.

[0186] S930: Dynamically adjusts the control commands of the servo motor of the cold heading machine according to the output torque and feed speed curves.

[0187] In the control system of the servo motor in a cold heading machine, load fluctuation is a crucial indicator reflecting changes in motor load. To compensate for load fluctuations, the PLC controller needs to dynamically adjust the motor's output torque based on the load fluctuation and a preset output torque adjustment formula. Output torque is the actual torque output by the motor, typically calculated from the motor's current and speed. 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 MHere, ΔT is the current output torque, ΔT is the load fluctuation, and k is the adjustment coefficient. For example, assuming the current output torque is 18 N·m, the load fluctuation is 2 N·m, and the adjustment coefficient is 1, the PLC will calculate the adjusted output torque as 20 N·m. The PLC obtains the current output torque and load fluctuation data from the driver via industrial Ethernet and substitutes them into the output torque adjustment formula. For example, assuming the current output torque is 18 N·m, the load fluctuation is 2 N·m, and the adjustment coefficient is 1, the PLC will calculate the adjusted output torque as 20 N·m, realizing dynamic adjustment of the motor output torque and ensuring the stability and reliability of motor operation. By adjusting the output torque, the system can quickly respond to external changes, improving control accuracy and production efficiency.

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

[0189] After adjusting the output torque and feed rate curves, the PLC controller needs to dynamically adjust the control commands for the cold heading machine servo motor based on this data. These control commands include speed control commands and position control commands, used to control the motor's speed and position. The process of dynamically adjusting the control commands involves several steps. First, the PLC generates a new speed control command based on the adjusted output torque. For example, assuming the adjusted output torque is 20 N·m, the PLC will generate a speed control command containing the target output torque of 20 N·m. Next, the PLC generates a new position control command based on the adjusted feed rate curve. For example, assuming the adjusted feed rate is 480 mm / s, the PLC will generate a position control command containing the target feed rate of 480 mm / s. Finally, the PLC sends the new speed control command and position control command to the driver via an industrial Ethernet network. For example, the PLC might generate a control command containing the target output torque of 20 N·m and the target feed rate of 480 mm / s and send it to the driver via an industrial Ethernet network.

[0190] This implementation method enables the PLC controller to 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 commands. Adjusting the output torque ensures real-time compensation for motor load fluctuations, adjusting the feed speed curve achieves real-time optimization of the motor's operating state, and dynamically adjusting the control commands ensures the stability and reliability of the motor's operation. This process improves the response speed and reliability of the control system, ensuring the efficient operation and machining accuracy of the cold heading machine. By precisely controlling the motor's output torque and feed speed curve, the system can quickly respond to external changes and adapt to complex working environments and varying load conditions.

[0191] This application also provides a feeding control system, such as... Figure 3 As shown, it includes:

[0192] The human-machine interface is used to respond to a feeding control request sent by a user through the human-machine interface and to route the feeding control request to the PLC controller.

[0193] Servo motor for cold heading machine;

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

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

[0196] The feed control request is parsed to obtain the feed control logic to be executed, and control instructions are generated 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 the optimized parameters of the equipment. The parameter optimization strategy includes a parameter adaptive adjustment strategy based on the motor dynamic model and a machine learning optimization strategy based on historical operating data.

[0198] The control commands and the equipment optimization parameters are sent to the driver of the cold heading machine servo motor via the industrial Ethernet. The control commands are used to control the speed and position of the cold heading machine servo motor. The cold heading machine is used to configure the parameters of the cold heading machine servo motor according to the equipment optimization parameters.

[0199] The operating status of the servo motor of the cold heading machine is monitored in real time, and the operating status is displayed on the human-machine interface;

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

[0201] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0202] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0203] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0204] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified 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] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0207] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0208] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0209] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for controlling the feed of a cold header servo motor, characterized in that, The application is applied to a feed control system, the feed control system comprises a cold header servo motor, a controller PLC and a human-machine interface, and the method comprises the following steps: 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 header servo motor through an industrial Ethernet, so as to identify the communication protocol type supported by the driver of the cold header servo motor and acquire actual parameters of the cold header servo motor; The controller PLC analyzes the feed control request to obtain a 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 preset template parameters, and adopts a parameter optimization strategy to optimize the actual parameters to obtain device optimization parameters in the case of differences, wherein the parameter optimization strategy comprises a parameter self-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 device optimization parameters to the driver of the cold header servo motor through the industrial Ethernet, wherein the control instruction is used to control the rotating speed and position of the cold header servo motor, and the driver is used to perform parameter configuration on the cold header servo motor according to the device optimization parameters; The controller PLC monitors the running state of the cold header servo motor in real time, and displays the running state on the human-machine interface; The controller PLC optimizes the control performance of the cold header servo motor based on the running state; The parameter self-adaptive adjustment strategy based on the motor dynamic model comprises the following steps: Real-time calculation of the first optimal control parameter of the cold header servo motor is performed according to a pre-constructed motor dynamic model and the actual parameters, wherein the first optimal control parameter comprises a proportional gain, an integral gain and a differential gain, the motor dynamic model is constructed based on the template parameters, and the motor dynamic model comprises a motor torque equation and a motor motion equation; The machine learning optimization strategy based on historical operation data comprises the following steps: The actual parameters are input into a pre-trained optimization model to predict the second optimal control parameter, wherein the optimization model is trained based on historical operation data of the cold header servo motor; Real-time load torque of the cold header servo motor is acquired, and the average load torque in a preset time period is calculated according to the real-time load torque; The first optimal control parameter and the second optimal control parameter are weighted and summed according to a preset weight coefficient to obtain the device optimization parameters; The weight coefficient is dynamically adjusted based on the current load torque and the average load torque of the cold header servo motor by using a preset adjustment formula, and the adjustment formula comprises the following formula: ， ； wherein, is a weight coefficient of the first optimal control parameter, is a weight coefficient of the second optimal control parameter, is the current load torque, is the average load torque, is an adjustment factor.

2. The method of claim 1, wherein, The controller PLC establishes a communication connection with the driver of the cold header servo motor through an industrial Ethernet, comprising: The controller PLC attempts to establish a communication connection with the driver of the cold header servo motor in turn by using different communication protocols, including Modbus TCP, EtherCAT, PROFINET and EtherNet / IP; In the case that the controller PLC is successfully connected with the driver of the cold header servo motor, the type of the communication protocol supported by the driver is determined, and a corresponding target communication driver program is loaded based on the type of the communication protocol; The controller PLC establishes a communication connection with the driver of the cold header servo motor based on the target communication driver program and the industrial Ethernet.

3. The method of claim 2, wherein, The controller PLC establishes a communication connection with the driver of the cold header servo motor based on the target communication driver program and the industrial Ethernet, including: The controller PLC initializes the communication parameters of the industrial Ethernet through the target communication driver program, 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.

4. The method of claim 1, wherein, The controller PLC analyzes the feeding control request to obtain the feeding control logic to be executed, and generates a control instruction based on the feeding control logic, including: The controller PLC analyzes the feeding control request to obtain the feeding control logic to be executed; The controller PLC generates a feeding speed curve and a position control instruction according to the feeding control logic; The controller PLC determines the optimal speed and the optimal position of the cold header servo motor according to the feeding speed curve and the position control instruction, and generates a control instruction based on the optimal speed and the optimal position.

5. The method of claim 4, wherein, The controller PLC determines the optimal speed and the optimal position of the cold header servo motor according to the feeding 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 header servo motor, and calculates a 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 an optimal feeding speed according to the position error and the maximum allowed acceleration; The controller PLC compares the optimal feeding speed with the feeding speed curve, and adjusts the optimal feeding speed to the maximum allowed speed in the allowed range if the optimal feeding speed exceeds the allowed range of the feeding speed curve; The controller PLC obtains the feeding wheel radius of the cold header servo motor; The controller PLC calculates the optimal speed according to the optimal feeding speed and the feeding wheel radius; The controller PLC generates a control instruction based on the optimal speed and the optimal position, and the control instruction includes a speed control instruction and a position control instruction.

6. The method of claim 5, wherein, The control performance of the cold header servo motor includes a feed speed curve, the operating state includes load torque, motor output torque, current data, temperature data and vibration frequency data, the controller PLC optimizes the control performance of the cold header 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 header 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 header servo motor according to the current data, the temperature data and the vibration frequency data; The controller PLC dynamically adjusts the control instruction of the cold header servo motor based on the health index and the load fluctuation to compensate for load fluctuation and optimize the feed speed curve.

7. The method of claim 6, wherein, The controller PLC dynamically adjusts the control instruction of the cold header servo motor based on the health index and the load fluctuation to compensate for load fluctuation and optimize the feed speed curve, including: The controller PLC adjusts the output torque of the cold header servo motor according to the load fluctuation 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 header servo motor is dynamically adjusted according to the output torque and the feed speed curve.

8. A feed control system applied to the feed control method of the servo motor of the cold header according to any one of claims 1 to 7, characterized in that, Including: A human-machine interface for routing a feed control request sent by a user through the human-machine interface to a controller PLC in response to receiving the feed control request; A cold header servo motor; A controller PLC connected to the human-machine interface and to the driver of the cold header servo motor for performing: Establishing a communication connection with the driver of the cold header servo motor through an industrial Ethernet to identify the communication protocol type supported by the driver of the cold header servo motor and to obtain actual parameters of the cold header servo motor, wherein the actual parameters include rated speed, rated torque and encoder resolution, motor inertia and load characteristics; Analyzing the feed control request to obtain a feed control logic to be executed and generating a control instruction based on the feed control logic; Comparing the actual parameters with preset template parameters and, in the case of differences, optimizing the actual parameters using a parameter optimization strategy to obtain device optimization parameters, wherein the parameter optimization strategy includes a parameter self-adaptive adjustment strategy based on a motor dynamic model and a machine learning optimization strategy based on historical operation data; Sending the control instruction and the device optimization parameters to the driver of the cold header servo motor through the industrial Ethernet, wherein the control instruction is used to control the speed and position of the cold header servo motor, and the cold header is used to configure parameters of the cold header servo motor according to the device optimization parameters; Monitoring the operating state of the cold header servo motor in real time and displaying the operating state on the human-machine interface; Optimizing the control performance of the cold header servo motor based on the operating state.

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