Crosslinked cable production control method based on improved PID control algorithm

Abstract

The invention relates to the technical field of cable production automation control, and provides a crosslinked cable production control method based on an improved PID (Proportion Integration Differentiation) control algorithm, which comprises the step of constructing an optimal reference model by establishing a cooperative control framework of temperature, pressure and traction speed and taking the temperature of a vulcanization pipe as a main control variable and the pressure and the traction speed of an extruder as slave control variables. A main controller tracks a temperature set value in real time, PID parameters are dynamically set according to technological parameters such as the thickness deviation of an insulating layer and the crosslinking degree of a material, and precise control is achieved through a three-ring cooperative coupling improved PID controller. A temperature control ring enables the steady-state error of the actual temperature and the set value to be converged to zero. The pressure and speed control loop converges the fluctuation amplitude to a process allowable range. The temperature stability, the pressure uniformity and the traction synchronism of crosslinked cable production can be improved, the process fluctuation and the defect rate are reduced, and the product quality and the production efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control in cable production, and more specifically, the present invention relates to a cross-linked cable production control method based on an improved PID control algorithm. Background Art

[0002] In the process of cross-linked cable production, precise control of process parameters such as temperature, pressure, and traction speed is crucial for ensuring product quality. Traditional control methods usually adopt a single PID controller to independently control the temperature of the vulcanization pipe, the pressure of the extruder, and the speed of the traction wheel. However, this method has many limitations. First, there is a lack of coordination between the control variables, making it difficult to achieve global optimization of the production process. Second, the parameters of the traditional PID controller are fixed and cannot be dynamically adjusted according to the changes in real-time process parameters, resulting in insufficient control accuracy. In addition, there are many interference factors in the cross-linked cable production process, such as changes in material properties and fluctuations in ambient temperature. These factors will have a significant impact on the production process, and traditional control methods are difficult to effectively cope with.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the traditional control method cannot achieve the coordinated control of temperature, pressure, and traction speed, and it is difficult to meet the requirements of precise control of multiple variables in the cross-linked cable production process; the parameters of the PID controller are fixed and cannot be dynamically adjusted according to real-time working conditions, resulting in insufficient control accuracy and adaptability; there is a lack of effective means to suppress and compensate for interference factors in the production process, affecting product quality and production efficiency. Summary of the Invention

[0004] The present invention provides a cross-linked cable production control method based on an improved PID control algorithm, including: establishing a coordinated control architecture for temperature, pressure, and traction speed in the cross-linked cable production process; taking the temperature of the vulcanization pipe as the main control variable, and the pressure of the extruder and the speed of the traction wheel as the slave control variables, and constructing an optimal reference model based on process parameters; the main controller real-time tracks the set value of the temperature of the vulcanization pipe, and according to the process parameters including the deviation of the insulation layer thickness and the degree of material cross-linking collected in real time, performs dynamic tuning of the PID parameters to obtain adjustment strategies for the proportional coefficient, integral time, and differential time to adapt to real-time working conditions; precisely controls the heating system of the vulcanization pipe through an improved PID controller with triple-loop coordinated coupling; enables the temperature control loop to track the set value of the reference model; through tracking, makes the steady-state error between the actual temperature and the set value of the vulcanization pipe converge to zero; makes the fluctuation amplitude of the pressure of the extruder and the traction speed converge to the error range allowed by the process.

[0005] As a further improvement of the present application, the method for obtaining the adjustment strategy of the proportional coefficient, integral time, and derivative time includes: in the cable extrusion molding stage, an initial PID parameter group is established based on a preset process parameter reference value; the main controller collects the temperature distribution of 9 points along the axis of the vulcanization tube in real time through a distributed temperature sensor and calculates the temperature field uniformity coefficient; a parameter correction instruction is generated according to the temperature field uniformity coefficient and sent to the slave controller, so that the extrusion pressure control loop and the traction speed control loop obtain corresponding compensation coefficients.

[0006] As a further improvement of the present application, the main controller monitors the cross-linking state of the insulating layer in real time through an infrared thermal imager and obtains the extrusion die head pressure fluctuation data through a pressure transmitter; extracts the standard deviation of the temperature gradient, calculates the material residence time, and detects and identifies the bubble defect characteristics; uses the detected abnormal temperature points as parameter correction points to perform anti-saturation processing on the PID integral term to obtain an optimized control quantity output.

[0007] As a further improvement of the present application, by establishing a material viscosity-temperature transfer function model and setting a feed-forward compensation link, the adjustment of the extruder screw speed is advanced ahead of the temperature change of the vulcanization tube.

[0008] As a further improvement of the present application, the main controller uses the ratio of the measured maximum axial temperature difference to the process allowable temperature difference as the PID parameter self-tuning factor.

[0009] As a further improvement of the present application, generating a parameter correction instruction according to the self-tuning factor includes: when the self-tuning factor is less than 0.5, keep the current PID parameter group and only enable the feed-forward compensation channel.

[0010] When the self-tuning factor is in the range of 0.5 - 1.0, activate the proportional coefficient adaptive adjustment module and adjust the proportional band dynamically, where, is the real-time adjustment amount of the proportional coefficient; is the adaptive adjustment coefficient; is the absolute value of the temperature deviation.

[0011] When the self-tuning factor is in the range of 1.0 - 1.5, activate the fuzzy PID switching mechanism and replace the integral term with a variable-speed integral algorithm.

[0012] When the self-tuning factor is greater than 1.5, trigger the emergency adjustment mode, adjust the three control parameters simultaneously, and start the cooling system for auxiliary intervention.

[0013] As a further improvement of the present application, the control method of the improved PID controller with three-loop collaborative coupling includes: establishing a second-order lag model of the vulcanization tube temperature control loop: ; where, is the equivalent gain coefficient of the vulcanizing tube heating system; is the thermal inertia time constant of the vulcanizing tube; is the heat conduction delay time.

[0014] Design an anti-integral saturation PID control law: ; where, is the controller output, is the proportional coefficient; is the temperature deviation, is the integral time constant; is the variable-speed integral factor, when the threshold value, the integral term is automatically attenuated; is the differential time constant; is the feedforward compensation amount.

[0015] Couple a cascade control loop of the extruder pressure and the traction speed at the output end of the temperature control loop to form a three-loop collaborative control architecture.

[0016] As a further improvement of the present application, the feedforward compensation amount is dynamically calculated by establishing a correlation model between the material extrusion amount and the heating power.

[0017] As a further improvement of the present application, in the correlation model: a traction speed disturbance observer is set to estimate the influence factor of speed fluctuation on the temperature field in real time; the output of the observer is weighted and fused with the real-time data of the pressure sensor to generate a feedforward compensation coefficient; the accurate distribution of the compensation amount is realized by dynamically adjusting the weighting factor to solve the overshoot problem in the large-lag system.

[0018] As a further improvement of the present application, establish a multi-objective optimization function based on expert rules: ; where, is the optimization index; is the temperature error weight coefficient, is the pressure error weight coefficient, is the control variable change rate weight coefficient, is the vulcanizing tube temperature tracking error; is the extruder pressure fluctuation amount; is the control variable change rate.

[0019] Dynamically adjust the PID parameters of each loop through an online rolling optimization algorithm to achieve the global optimal control of the production process.

[0020] The above embodiments of the present invention have at least the following beneficial effects: The cross-linked cable production control method of the present invention can achieve the coordinated control of temperature, pressure, and traction speed. By constructing an optimal reference model based on process parameters, the control variables cooperate with each other, thereby improving the stability of the production process and the product quality. The main controller can track the temperature set value of the vulcanization tube in real time and dynamically tune the PID parameters according to the process parameters collected in real time. This can not only make the steady-state error of the temperature control loop converge to zero, but also control the fluctuation range of the extruder pressure and traction speed within the process-allowed error range, improving the control accuracy and adaptability. In addition, by establishing a material viscosity-temperature transfer function model and setting a feed-forward compensation link, the rotation speed of the extruder screw can be adjusted in advance to effectively cope with the influence brought by the temperature change of the vulcanization tube and further optimize the production process.

[0021] The present invention can also self-tune the PID parameters through various strategies, activate the corresponding adjustment modules or mechanisms according to different self-tuning factors, so as to achieve the optimal control effect under different working conditions. For example, when the self-tuning factor is small, the feed-forward compensation channel is enabled, and when the factor is large, the emergency adjustment mode is triggered and the cooling system is started for auxiliary intervention. This flexible parameter adjustment method can effectively cope with various complex situations. At the same time, by establishing a multi-objective optimization function based on expert rules and using an online rolling optimization algorithm to dynamically adjust the PID parameters of each loop, the global optimal control of the production process can be achieved, further improving the production efficiency and product quality and reducing the production cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By referring to the detailed description below with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, wherein: Figure 1 It is a schematic flowchart of a cross-linked cable production control method based on an improved PID control algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and then implement the present invention, and do not limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to be able to convey the scope of the present invention completely to those skilled in the art.

[0024] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, a device, an equipment, a method or a computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: completely hardware, completely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0025] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0026] The following refers to Figure 1 , Figure 1 which is a schematic flow chart of a cross-linked cable production control method based on an improved PID control algorithm provided for an embodiment of the present invention. As Figure 1 shown, a cross-linked cable production control method based on an improved PID control algorithm includes: S1 establishing a coordinated control architecture for temperature, pressure and traction speed in the cross-linked cable production process; taking the vulcanization tube temperature as the main control variable, and the extruder pressure and the traction wheel speed as the slave control variables, and constructing an optimal reference model based on process parameters.

[0027] S2 The main controller tracks the set value of the vulcanization tube temperature in real time, and performs dynamic tuning of PID parameters according to the process parameters including the insulation layer thickness deviation and the material cross-linking degree collected in real time, so as to obtain adjustment strategies for the proportional coefficient, integral time and differential time to adapt to the real-time working conditions.

[0028] S3 Precise control of the vulcanization tube heating system is carried out through an improved PID controller with triple-loop coordinated coupling; the temperature control loop tracks the set value of the reference model; through tracking, the steady-state error between the actual temperature of the vulcanization tube and the set value converges to zero; the fluctuation ranges of the extruder pressure and the traction speed converge to the error range allowed by the process.

[0029] It should be noted that the present invention proposes a production control method for cross-linked cables. The core lies in achieving the comprehensive control of temperature, pressure, and traction speed by establishing a collaborative control architecture. During the production process of cross-linked cables, the temperature of the vulcanization tube is a key factor affecting product quality, so it is set as the main control variable. The pressure of the extruder and the speed of the traction wheel are used as slave control variables, which also have important impacts on the stability of the production process and product quality. By constructing an optimal reference model based on process parameters, an ideal control target can be provided for the entire production process. The main controller will continuously track the set value of the vulcanization tube temperature and dynamically adjust the proportional coefficient, integral time, and derivative time in the PID parameters according to the real-time collected process parameters, such as the deviation of the insulation layer thickness and the degree of cross-linking of the material, to adapt to the real-time working conditions changes during the production process. This dynamic tuning strategy can ensure the flexibility and adaptability of the control system. Through the improved PID controller with triple-loop collaborative coupling, precise control is carried out on the vulcanization tube heating system, enabling the temperature control loop to closely track the set value of the reference model, thereby achieving the convergence of the steady-state error between the actual temperature of the vulcanization tube and the set value to zero, and controlling the fluctuation range of the extruder pressure and traction speed within the error range allowed by the process, effectively improving the stability of the production process and product quality.

[0030] Specifically, the collaborative control architecture in the present invention refers to an integrated control system that controls the three key process parameters of the vulcanization tube temperature, extruder pressure, and traction wheel speed as a whole, rather than controlling them independently. The vulcanization tube temperature is used as the main control variable because it is directly related to the cross-linking quality and final performance of the cable insulation layer. The extruder pressure and traction wheel speed are used as slave control variables because they have a direct impact on the shaping and production efficiency of the cable. The optimal reference model is established based on the process requirements and empirical data of cross-linked cable production, which provides an ideal state for the entire production process, and the main controller will adjust the parameters in the actual production process according to this model. The dynamic tuning of PID parameters means automatically adjusting the proportional coefficient, integral time, and derivative time of the PID controller according to the real-time collected process parameters, such as the deviation of the insulation layer thickness and the degree of cross-linking of the material. The proportional coefficient determines the response intensity of the control quantity to the deviation; the integral time is used to eliminate the steady-state error; the derivative time is used to predict the change trend of the deviation. Through this dynamic tuning, the PID controller can better adapt to various changes during the production process. The improved PID controller with triple-loop collaborative coupling refers to organically combining the temperature control loop, extruder pressure control loop, and traction speed control loop to form a collaborative control system. The temperature control loop is responsible for precisely controlling the temperature of the vulcanization tube to make it track the set value of the reference model; the extruder pressure control loop and traction speed control loop are adjusted according to the output of the temperature control loop and their own process requirements to ensure the stability of the entire production process and product quality.

[0031] Preferably, the optimal reference model in the present invention can be constructed by collecting a large amount of actual data in the production process, including the temperature of the vulcanization tube, the pressure of the extruder, the speed of the traction wheel, the deviation of the insulation layer thickness, and the degree of crosslinking of the material, etc., and then using methods such as data fitting and machine learning. The input parameters of the model include the initial process parameters and the process parameters collected in real time during the production process. Through these parameters, the model can output the ideal set values of the vulcanization tube temperature, the extruder pressure, and the traction wheel speed. During the dynamic tuning of the PID parameters, the main controller will calculate the adjustment strategies of the proportional coefficient, integral time, and derivative time according to the process parameters collected in real time, such as the deviation of the insulation layer thickness and the degree of crosslinking of the material. Specifically, the main controller will generate a parameter correction instruction according to the temperature field uniformity coefficient and send it to the slave controller, so that the extrusion pressure control loop and the traction speed control loop obtain corresponding compensation coefficients. The temperature field uniformity coefficient is calculated by the main controller by collecting the temperature distribution at multiple points along the axis of the vulcanization tube in real time, and it reflects the uniformity of the temperature field of the vulcanization tube. In terms of data processing, the main controller will collect the temperature data along the axis of the vulcanization tube in real time through a distributed temperature sensor, calculate the temperature field uniformity coefficient using statistical methods, and then generate a parameter correction instruction according to this coefficient. During the control process, the main controller will also use the ratio of the measured maximum axial temperature difference to the process allowable temperature difference as the PID parameter self-tuning factor to achieve more accurate control.

[0032] In some embodiments, the method for obtaining the adjustment strategies of the proportional coefficient, integral time, and derivative time includes: in the cable extrusion and forming stage, an initial PID parameter group is established based on a preset process parameter reference value; the main controller collects the temperature distribution at 9 points along the axis of the vulcanization tube in real time through a distributed temperature sensor and calculates the temperature field uniformity coefficient; a parameter correction instruction is generated according to the temperature field uniformity coefficient and sent to the slave controller, so that the extrusion pressure control loop and the traction speed control loop obtain corresponding compensation coefficients.

[0033] It should be noted that in the cable extrusion and forming stage of the present invention, the control parameters are dynamically adjusted by establishing an initial PID parameter group and collecting the temperature distribution along the axis of the vulcanization tube in real time. The initial PID parameter group is constructed based on a preset process parameter reference value, which provides an initial parameter setting for the entire control process. The main controller collects the temperature distribution data at 9 points along the axis of the vulcanization tube in real time through a distributed temperature sensor and calculates the temperature field uniformity coefficient, which is used to evaluate the uniformity of the temperature field of the vulcanization tube. According to the temperature field uniformity coefficient, the main controller will generate a parameter correction instruction and send it to the slave controller, so that the extrusion pressure control loop and the traction speed control loop obtain corresponding compensation coefficients to optimize the control effect of the entire production process. This dynamic adjustment strategy can effectively cope with problems such as uneven temperature distribution in the production process and improve product quality and production efficiency.

[0034] Nine temperature monitoring points set axially on the vulcanization tube are evenly distributed. Specifically: The effective heating area of the vulcanization tube (from the inlet end to the outlet end) is evenly divided into 8 segments, forming 9 equally spaced monitoring points (including both endpoints). A distributed temperature sensor is installed at each monitoring point to collect temperature data at the corresponding position in real time. For example, if the effective length of the vulcanization tube is L, the position coordinates of each point are 0, L / 8, 2L / 8 up to L (the outlet end), a total of 9 points.

[0035] Specifically, the specific areas where the vulcanization tube is axially divided into 8 segments are as follows (taking the effective length L as an example): The 1st segment: from the inlet end to L / 8, usually corresponding to the area where the heating power is concentrated at the front end of the vulcanization tube, responsible for the initial temperature rise of the material.

[0036] The 2nd segment: from L / 8 to 2L / 8, located in the middle of the heating section, monitoring the temperature stability at the initial stage of the cross-linking reaction of the material.

[0037] The 3rd segment: from 2L / 8 to 3L / 8, still belonging to the core area of the heating section, focusing on capturing the temperature gradient change under the action of the heating element.

[0038] The 4th segment: from 3L / 8 to 4L / 8, close to the end of the heating section, monitoring the critical area where the heating transitions to heat preservation.

[0039] The 5th segment: from 4L / 8 to 5L / 8, belonging to the first half of the transition section, ensuring the uniform progress of the cross-linking reaction of the material.

[0040] The 6th segment: from 5L / 8 to 6L / 8, the second half of the transition section, monitoring the attenuation trend of the temperature field from active heating to passive heat preservation.

[0041] The 7th segment: from 6L / 8 to 7L / 8, the front end of the heat preservation section, maintaining the temperature stability after the cross-linking of the material is completed.

[0042] The 8th segment: from 7L / 8 to L (the outlet end), the end of the heat preservation section, preventing the material from shrinking or having defects due to a sudden drop in temperature.

[0043] Specifically, the initial PID parameter set refers to a set of PID control parameters set according to the preset process parameter reference values at the beginning of the cable extrusion forming stage, including the proportional coefficient, integral time, and derivative time. These parameters are the basic settings of the control system and are used for temperature control in the initial stage. The distributed temperature sensor is a sensor array installed at different axial positions of the vulcanization tube and is used to collect the temperature data of 9 points axially on the vulcanization tube in real time. The temperature data of these 9 points can comprehensively reflect the temperature distribution inside the vulcanization tube. The temperature field uniformity coefficient is obtained by calculating the statistical characteristics of the temperature data of these 9 points, and it reflects the degree of uniformity of the temperature field. The parameter correction instruction is generated by the main controller according to the temperature field uniformity coefficient and is used to adjust the control parameters of the slave controller so that the extrusion pressure control loop and the traction speed control loop can make corresponding compensations according to the changes in the temperature field. The compensation coefficient is calculated according to the parameter correction instruction and is used to adjust the control parameters of the extrusion pressure and traction speed to optimize the control effect of the entire production process.

[0044] Preferably, the construction of the initial PID parameter set can be optimized based on historical production data and process requirements. For example, by analyzing the variation rules of parameters such as the temperature of the vulcanization tube, extrusion pressure, and traction speed in the previous production process and combining with the process requirements, a set of PID parameters that can better adapt to the initial production stage is determined. When collecting the axial temperature distribution of the vulcanization tube in real time, the distributed temperature sensors can be evenly distributed along the axis of the vulcanization tube to ensure that the collected temperature data can comprehensively reflect the temperature distribution inside the vulcanization tube. The calculation of the temperature field uniformity coefficient can be achieved by calculating the standard deviation or variance of the temperature data of 9 points. The smaller the standard deviation or variance, the more uniform the temperature field. The generation of the parameter correction instruction can be determined according to the comparison result between the temperature field uniformity coefficient and the preset threshold. When the temperature field uniformity coefficient exceeds the preset range, the corresponding correction instruction is generated. The calculation of the compensation coefficient can be obtained through control algorithms such as proportional, integral, or derivative according to the specific requirements of the correction instruction and in combination with the actual control requirements of the extrusion pressure and traction speed to achieve precise compensation for the extrusion pressure and traction speed.

[0045] In some embodiments, the main controller monitors the cross-linking state of the insulating layer in real time through an infrared thermal imager and obtains the extrusion die head pressure fluctuation data through a pressure transmitter; extracts the standard deviation of the temperature gradient, calculates the material residence time, and detects and identifies the characteristics of bubble defects; uses the detected abnormal temperature points as parameter correction points and performs anti-saturation processing on the PID integral term to obtain an optimized control quantity output.

[0046] It should be noted that the present invention monitors the cross-linking state of the insulating layer and the pressure fluctuation data of the extruder die head in real time through the main controller, and further optimizes the parameter adjustment strategy of the PID controller. The main controller uses an infrared thermal imager to monitor the cross-linking state of the insulating layer. By analyzing the standard deviation of the temperature gradient, calculating the material residence time, and detecting the characteristics of bubble defects, it can accurately identify abnormal conditions in the production process. At the same time, the main controller obtains the pressure fluctuation data of the extruder die head through a pressure transmitter, combines these data with the temperature information, and generates an optimized control quantity output. By using the detected abnormal temperature points as parameter correction points and performing anti-windup processing on the PID integral term, the phenomenon of integral windup can be effectively avoided, and the stability and response speed of the control system can be improved.

[0047] Specifically, the main controller is the core component of the entire control system, responsible for collecting and processing various sensor data, and generating control instructions according to the preset control strategy. The infrared thermal imager is a non-contact temperature measurement device that can monitor the temperature distribution on the surface of the insulating layer in real time. By analyzing the standard deviation of the temperature gradient, the uniformity of the cross-linking process can be judged. The material residence time refers to the time that the material stays in the vulcanization tube. By calculating the residence time, the stability of the production process can be evaluated. The characteristics of bubble defects refer to the possible bubble defects in the insulating layer. By detecting these characteristics, quality problems can be discovered in a timely manner. The pressure transmitter is a sensor used to measure the pressure of the extruder die head, and its output data reflects the pressure fluctuation during the extrusion process. The anti-windup processing of the PID integral term means that when the integral term reaches a certain threshold, its growth is restricted through a specific algorithm to avoid control deviation caused by integral windup.

[0048] Preferably, the infrared thermal imager can be installed near the outlet of the vulcanization tube to monitor the temperature distribution on the surface of the insulating layer in real time. The standard deviation of the temperature gradient can be obtained by calculating the statistical value of the temperature difference between adjacent monitoring points. The larger the standard deviation, the worse the uniformity of the cross-linking process. The material residence time can be calculated by monitoring the time difference between the material entering and leaving the vulcanization tube. Combining the production speed and the length of the vulcanization tube, the residence time can be accurately evaluated. The detection of the characteristics of bubble defects can be achieved by analyzing abnormal points in the temperature distribution. For example, an area where the temperature suddenly drops implies the presence of bubbles. The output data of the pressure transmitter can be processed through filtering and data fusion techniques to improve the accuracy and reliability of the data. In the PID controller, when an abnormal temperature point is detected, anti-windup processing can be achieved by adjusting the weight of the integral term or introducing a variable-speed integral algorithm. For example, when the temperature deviation exceeds a certain threshold, the gain of the integral term is appropriately reduced, or the value of the integral term is dynamically adjusted according to the change rate of the deviation, so as to optimize the control quantity output and ensure the stability of the production process and the product quality.

[0049] In some embodiments, by establishing a material viscosity-temperature transfer function model and setting up a feedforward compensation link, the adjustment of the extruder screw speed is advanced ahead of the change in the vulcanization tube temperature.

[0050] It should be noted that in the present invention, by establishing a material viscosity-temperature transfer function model and setting up a feedforward compensation link, the extruder screw speed can respond in advance to the change in the vulcanization tube temperature. This feedforward compensation mechanism can effectively reduce the impact of the change in material viscosity caused by temperature change on the production process, thereby improving the stability of the production process and the product quality. In this way, the adjustment of the extruder screw speed can be advanced ahead of the change in the vulcanization tube temperature, avoiding the adverse impact on the extrusion process caused by the viscosity fluctuation of the material due to temperature change.

[0051] Specifically, the material viscosity-temperature transfer function model is a mathematical model used to describe the relationship between material viscosity and temperature. This model is obtained by fitting experimental data based on the physical properties of the material and can predict the change in material viscosity at different temperatures. The feedforward compensation link is a control strategy that compensates for the change in material viscosity caused by temperature change by adjusting the extruder screw speed in advance. This compensation mechanism can reduce the extrusion pressure fluctuation caused by viscosity change, thereby improving the stability of the production process. The extruder screw speed is an important control parameter of the extruder, which directly affects the extrusion volume and production speed of the material. Through feedforward compensation, the screw speed can be dynamically adjusted according to the change in the vulcanization tube temperature to ensure the continuity and stability of the extrusion process.

[0052] Preferably, the material viscosity-temperature transfer function model can be constructed through the following steps: First, collect the experimental data of material viscosity at different temperatures, which can be obtained through laboratory tests or monitoring in the actual production process. Then, use data fitting techniques, such as polynomial fitting or exponential fitting, to establish the mathematical relationship between viscosity and temperature. The input parameter of the model is the actual temperature of the vulcanization tube, and the output parameter is the corresponding material viscosity. In the feedforward compensation link, according to the predicted viscosity change of the model, calculate the screw speed that needs to be adjusted. Specifically, when the temperature of the vulcanization tube rises, the material viscosity decreases, and at this time, the screw speed needs to be appropriately reduced to maintain the stability of the extrusion pressure; conversely, when the temperature decreases, the screw speed needs to be increased. This dynamic adjustment can be achieved through a simple proportional controller, which adjusts the proportional factor of the screw speed according to the amplitude of the viscosity change. In this way, precise control of the extrusion process can be realized, improving production efficiency and product quality.

[0053] In some embodiments, the main controller uses the ratio of the measured maximum axial temperature difference to the process-allowed temperature difference as the PID parameter self-tuning factor.

[0054] It should be noted that in the present invention, the main controller uses the ratio of the measured maximum axial temperature difference to the process - allowed temperature difference as the PID parameter self - tuning factor, thereby realizing the dynamic adjustment of PID parameters. This method can flexibly adjust the parameters of the PID controller according to the actual temperature difference in the production process to adapt to different working conditions. In this way, the control system can better cope with the influence brought by temperature changes, improving the control accuracy and the stability of the system.

[0055] Specifically, the main controller is the core component of the entire control system, responsible for collecting and processing various sensor data, and generating control instructions according to the preset control strategy. The maximum axial temperature difference refers to the difference between the highest temperature and the lowest temperature measured along the axis of the vulcanization tube, which reflects the non - uniformity of the temperature distribution inside the vulcanization tube. The process - allowed temperature difference refers to the maximum temperature difference range allowed according to the production process requirements, and this value is preset according to the requirements of product quality and production efficiency. The PID parameter self - tuning factor is obtained by calculating the ratio of the maximum axial temperature difference to the process - allowed temperature difference, and this factor is used to dynamically adjust the proportional coefficient, integral time, and derivative time of the PID controller. In this way, the PID controller can adjust the control parameters in real time according to the actual temperature difference situation to ensure the stability of the production process and product quality.

[0056] Preferably, the main controller can collect the temperature data along the axis of the vulcanization tube in real time through distributed temperature sensors, calculate the maximum axial temperature difference. Then, compare this maximum temperature difference with the preset process - allowed temperature difference to obtain the PID parameter self - tuning factor. Specifically, when the self - tuning factor is less than 0.5, it indicates that the temperature difference is small, and the control system can maintain the current PID parameter group and only enable the feed - forward compensation channel to fine - tune the control quantity. When the self - tuning factor is between 0.5 and 1.0, activate the proportional coefficient adaptive adjustment module to dynamically adjust the proportional band proportionally to adapt to the change of the temperature difference. When the self - tuning factor is between 1.0 and 1.5, activate the fuzzy PID switching mechanism to replace the integral term with a variable - speed integral algorithm to further optimize the control effect. When the self - tuning factor is greater than 1.5, trigger the emergency adjustment mode, adjust the three control parameters simultaneously and start the cooling system for auxiliary intervention to quickly correct the temperature deviation. Through this segmented self - tuning strategy, the control system can flexibly adjust the PID parameters according to different situations to ensure the stability of the production process and product quality.

[0057] In some embodiments, generating a parameter correction instruction according to the self - tuning factor includes: when the self - tuning factor is less than 0.5, maintaining the current PID parameter group and only enabling the feed - forward compensation channel.

[0058] When the self - tuning factor is in the range of 0.5 - 1.0, activate the proportional coefficient adaptive adjustment module, according to Dynamically adjust the proportional band, where ; The real-time adjustment amount of the proportional coefficient; : Adaptive adjustment coefficient, updated online according to the historical adjustment effect; : Absolute value of temperature deviation.

[0059] When the self-tuning factor is in the range of 1.0 - 1.5, activate the fuzzy PID switching mechanism and replace the integral term with a variable-speed integral algorithm.

[0060] When the self-tuning factor is greater than 1.5, trigger the emergency adjustment mode, adjust the three control parameters simultaneously and start the cooling system for auxiliary intervention.

[0061] It should be noted that the present invention generates parameter correction instructions according to the self-tuning factor, and activates the corresponding adjustment modules or mechanisms through different self-tuning factor intervals, so as to realize the dynamic adjustment of PID parameters. This method can flexibly adjust the parameters of the PID controller according to the actual temperature difference in the production process to adapt to different working conditions. In this way, the control system can better cope with the influence brought by temperature changes, improve the control accuracy and the stability of the system.

[0062] Specifically, the self-tuning factor is obtained by calculating the ratio of the maximum axial temperature difference to the process allowable temperature difference, and is used to dynamically adjust the parameters of the PID controller. When the self-tuning factor is less than 0.5, it indicates that the temperature difference is small, and the control system can maintain the current PID parameter set and only enable the feed-forward compensation channel to fine-tune the control quantity. The feed-forward compensation channel is a control strategy that adjusts the control quantity in advance to compensate for possible disturbances. When the self-tuning factor is between 0.5 and 1.0, activate the proportional coefficient adaptive adjustment module and dynamically adjust the proportional band proportionally. The real-time adjustment amount of the proportional coefficient is calculated according to the adaptive adjustment coefficient and the absolute value of the temperature deviation. The adaptive adjustment coefficient is updated online according to the historical adjustment effect to optimize the control effect. When the self-tuning factor is between 1.0 and 1.5, activate the fuzzy PID switching mechanism and replace the integral term with a variable-speed integral algorithm to further optimize the control effect. The variable-speed integral algorithm is a strategy for dynamically adjusting the integral term, which can adjust the integral gain according to the change speed of the deviation. When the self-tuning factor is greater than 1.5, trigger the emergency adjustment mode, adjust the three control parameters simultaneously and start the cooling system for auxiliary intervention to quickly correct the temperature deviation.

[0063] Preferably, the calculation of the self-tuning factor can be achieved through the following steps: First, the master controller collects the temperature data of the axial direction of the vulcanization tube in real time through the distributed temperature sensor and calculates the maximum axial temperature difference. Then, this maximum temperature difference is compared with the pre-set process allowable temperature difference to obtain the self-tuning factor. In the proportional coefficient adaptive adjustment module, the adaptive adjustment coefficient can be updated online according to the historical adjustment effect. For example, by analyzing the change trend of the temperature deviation and the control effect in the past period of time, the adaptive adjustment coefficient is dynamically adjusted. In the fuzzy PID switching mechanism, the variable speed integral algorithm can dynamically adjust the integral gain according to the change speed of the deviation. For example, when the change speed of the deviation is fast, the integral gain is appropriately reduced to avoid over-adjustment. In the emergency adjustment mode, the proportional coefficient, integral time, and differential time are adjusted simultaneously, and the cooling system is started for auxiliary intervention. For example, by increasing the flow rate of the cooling medium or adjusting the operating parameters of the cooling system, the temperature of the vulcanization tube is quickly reduced. Through this segmented self-tuning strategy, the control system can flexibly adjust the PID parameters according to different situations to ensure the stability of the production process and the product quality.

[0064] In some embodiments, the control method of the improved PID controller with triple-loop collaborative coupling includes: establishing a second-order lag model of the vulcanization tube temperature control loop: ; where : the equivalent gain coefficient of the vulcanization tube heating system, representing the temperature rise caused by unit heating power; : the thermal inertia time constant of the vulcanization tube, reflecting the temperature response lag characteristic; : the heat conduction delay time, representing the transmission lag from the heating element to the measurement point.

[0065] Design an anti-integral saturation PID control law: ; In the formula, : the controller output; : the proportional coefficient; : the temperature deviation; : the integral time constant; : the variable speed integral factor, with a value range of [0, 1], and the integral term is automatically attenuated when the threshold; : the differential time constant; : the feedforward compensation amount, generated by the extruder working condition prediction model.

[0066] Couple a cascade control loop of the extruder pressure and the traction speed at the output end of the temperature control loop to form a triple-loop collaborative control architecture.

[0067] It should be noted that the present invention realizes precise control of the vulcanizing tube heating system by establishing a second-order lag model of the vulcanizing tube temperature control loop and designing an anti-integral saturation PID control law. This control method can effectively solve the integral saturation problem that may occur in traditional PID controllers when facing complex working conditions, and improve the stability and response speed of the control system. By coupling a cascade control loop of the extruder pressure and the traction speed at the output end of the temperature control loop, a three-loop collaborative control architecture is formed, further optimizing the control effect of the entire production process.

[0068] Specifically, the second-order lag model of the vulcanizing tube temperature control loop is a mathematical model used to describe the dynamic characteristics of the vulcanizing tube heating system. This model characterizes the response characteristics of the vulcanizing tube temperature to the heating power through parameters such as the equivalent gain coefficient, thermal inertia time constant, and heat conduction delay time. The equivalent gain coefficient reflects the temperature rise caused by unit heating power, the thermal inertia time constant describes the lag characteristics of the temperature response, and the heat conduction delay time represents the transmission lag from the heating element to the measurement point. The anti-integral saturation PID control law is an improved PID control algorithm that avoids the saturation phenomenon caused by the excessive accumulation of the integral term under the action of long-term deviation by introducing a variable-speed integral factor and a feedforward compensation amount. The variable-speed integral factor dynamically adjusts the gain of the integral term according to the magnitude of the deviation. When the deviation exceeds the preset threshold, the gain of the integral term is appropriately attenuated to avoid integral saturation. The feedforward compensation amount is generated according to the extruder working condition prediction model and compensates the control amount in advance. The three-loop collaborative control architecture organically combines the temperature control loop, the extruder pressure control loop, and the traction speed control loop, and through the cascade control method, enables each control loop to cooperate with each other to achieve the collaborative control of the entire production process.

[0069] Preferably, the second-order lag model of the vulcanizing tube temperature control loop can be constructed through the following steps: First, determine the equivalent gain coefficient, thermal inertia time constant, and heat conduction delay time of the vulcanizing tube heating system through experiments or actual production data. These parameters can be obtained through system identification methods. For example, through a step response experiment, analyze the temperature change curve to determine the model parameters. The implementation of the anti-integral saturation PID control law can be divided into the following steps: First, calculate the proportional term and the differential term according to the real-time collected temperature deviation; then, dynamically adjust the variable-speed integral factor according to the magnitude of the deviation. When the deviation exceeds the preset threshold, appropriately attenuate the gain of the integral term to avoid integral saturation; finally, generate the final control amount output in combination with the feedforward compensation amount. The generation of the feedforward compensation amount can be achieved by establishing a correlation model between the material extrusion amount and the heating power. This model predicts the required heating power change according to the real-time working conditions of the extruder. In this way, the control system can adjust the heating power in advance, reduce temperature fluctuations, and improve the stability of the production process and the product quality.

[0070] In some embodiments, the feedforward compensation amount is dynamically calculated by establishing a correlation model between the material extrusion amount and the heating power.

[0071] It should be noted that in the present invention, a correlation model between the material extrusion amount and the heating power is established to dynamically calculate the feedforward compensation amount. Such a model can adjust the heating power in advance according to the actual working conditions of the extruder to compensate for the temperature fluctuations caused by changes in the material extrusion amount. In this way, the overshoot phenomenon of temperature can be effectively reduced, and the stability of the production process and the product quality can be improved.

[0072] Specifically, the material extrusion amount refers to the volume or mass of the material extruded from the extruder per unit time, and it is one of the important factors affecting the temperature of the vulcanization tube. The heating power refers to the energy input required to maintain the temperature of the vulcanization tube, and its magnitude directly affects the actual temperature of the vulcanization tube. The correlation model is a mathematical model used to describe the relationship between the material extrusion amount and the heating power. This model predicts the heating power required under different extrusion amounts by analyzing historical data and real-time working conditions. The feedforward compensation amount is calculated based on this model and is used to adjust the heating power in advance to compensate for the temperature fluctuations caused by changes in the extrusion amount. In this way, the control system can quickly adjust the heating power when the extrusion amount changes, reducing the overshoot and lag phenomena of temperature.

[0073] Preferably, the correlation model between the material extrusion amount and the heating power can be constructed through the following steps: First, collect the historical data of the temperature and heating power of the vulcanization tube under different extrusion amounts, and this data can be obtained through the monitoring equipment in the production process. Then, use data fitting techniques, such as linear regression or non-linear regression, to establish the mathematical relationship between the extrusion amount and the heating power. The input parameter of the model is the actual extrusion amount of the extruder, and the output parameter is the corresponding heating power. When dynamically calculating the feedforward compensation amount, the required change in heating power can be predicted through the correlation model according to the real-time monitored extrusion amount. For example, when the extrusion amount increases, the model will predict the required increase in heating power and use this predicted value as the feedforward compensation amount to adjust the heating power in advance to maintain the stability of the temperature of the vulcanization tube. In this way, the control system can better cope with the influence brought by the change in the extrusion amount, improving the stability of the production process and the product quality.

[0074] In some embodiments, in the correlation model: a traction speed disturbance observer is set to estimate in real time the influence factor of speed fluctuation on the temperature field; the output of the observer is weighted and fused with the real-time data of the pressure sensor to generate a feedforward compensation coefficient; the precise distribution of the compensation amount is achieved by dynamically adjusting the weighting factor to solve the overshoot problem in the large lag system.

[0075] It should be noted that in the present invention, by establishing a traction speed disturbance observer, the influence factor of speed fluctuation on the temperature field is estimated in real time, and the output of the observer is weighted and fused with the real-time data of the pressure sensor to generate a feedforward compensation coefficient. This method can effectively cope with the influence of traction speed fluctuation on the production process, and achieve precise distribution of the compensation amount by dynamically adjusting the weighting factor, thereby solving the overshoot problem in the large-delay system and further optimizing the control effect of the production process.

[0076] Specifically, the traction speed disturbance observer is a device used to monitor and estimate the influence of traction speed fluctuation on the temperature field. It calculates the specific influence factor of speed fluctuation on the temperature field by collecting traction speed data in real time and combining the feedback information of the temperature sensor. This influence factor reflects the potential influence degree of traction speed change on the temperature of the vulcanization tube. Weighted fusion is a data processing method that generates a comprehensive feedforward compensation coefficient by weighted combination of the output of the traction speed disturbance observer and the real-time data of the pressure sensor. This coefficient is used to adjust the heating power or other control parameters to compensate for the influence brought by speed fluctuation. Dynamically adjusting the weighting factor means automatically adjusting the weight ratio between the observer output and the pressure data according to the real-time working conditions and control requirements to achieve more precise compensation. This method can effectively solve the overshoot problem common in large-delay systems and improve the response speed and stability of the control system.

[0077] Preferably, the construction of the traction speed disturbance observer can be based on the following steps: First, install high-precision traction speed sensors and temperature sensors to collect traction speed and vulcanization tube temperature data in real time. Then, through data analysis algorithms, establish a dynamic relationship model between the traction speed and the temperature field. This model can estimate the influence factor of traction speed fluctuation on the temperature field in real time based on historical data and real-time data. In the weighted fusion process, the initial weight ratio can be set according to the actual working conditions. For example, when the traction speed fluctuation is large, increase the weight of the output of the traction speed disturbance observer; when the pressure fluctuation is large, increase the weight of the pressure sensor data. Dynamically adjusting the weighting factor can be achieved through an adaptive algorithm. For example, automatically adjust the weight ratio according to the change trend of the temperature deviation and the control effect. For example, when the temperature deviation is large and lasts for a long time, appropriately increase the weight of the output of the traction speed disturbance observer to respond more quickly to the influence brought by speed fluctuation. In this way, the control system can more flexibly cope with various working condition changes and improve the stability of the production process and the product quality.

[0078] In some embodiments, a multi-objective optimization function based on expert rules is established: ; where : is the optimization index, which is used to comprehensively evaluate the temperature control accuracy, pressure stability and control action smoothness in the production process of cross-linked cables. is the temperature error weight coefficient, is the pressure error weight coefficient, is the weight coefficient of the control variable change rate, , , satisfy the sum of 1, and balance the importance of different control objectives according to the production process requirements and actual production experience. is the vulcanizing tube temperature tracking error; is the extrusion machine pressure fluctuation; is the control variable change rate, .

[0079] Dynamically adjust the PID parameters of each loop through an online rolling optimization algorithm to achieve the global optimal control of the production process. Specifically, collect the current , and in real time, and substitute them into the above optimization function to calculate the current optimization target value . According to the value, use the online rolling optimization algorithm to iteratively optimize the PID parameters to minimize the value, so as to achieve the global optimal control of the production process.

[0080] For example, when the value increases, increase the proportional coefficient to speed up the response speed, or adjust the integral time and differential time to reduce the steady-state error and overshoot. In this way, the control system can dynamically adjust the PID parameters under different working conditions, achieve global optimal control, improve production efficiency and product quality, and reduce production costs.

[0081] It should be noted that the present invention realizes the global optimal control of the production process by establishing a multi-objective optimization function based on expert rules and using an online rolling optimization algorithm to dynamically adjust the PID parameters of each loop. This method comprehensively considers multiple optimization objectives such as temperature error, pressure error, and control variable change rate. By dynamically adjusting the PID parameters, it ensures that the production process can achieve the optimal control effect under different working conditions. This global optimization strategy can effectively improve production efficiency and product quality and reduce production costs. Specifically, the multi-objective optimization function based on expert rules is a model that comprehensively considers multiple optimization objectives. It quantifies and synthesizes objectives such as temperature error, pressure error, and control variable change rate by setting different weight coefficients. The temperature error weight coefficient reflects the importance of temperature control accuracy; the pressure error weight coefficient reflects the importance of pressure control accuracy; the control variable change rate weight coefficient is used to balance the smoothness of control actions. The vulcanizing tube temperature tracking error represents the deviation between the actual temperature and the set temperature; the extruder pressure fluctuation represents the deviation between the actual pressure and the set pressure; the control variable change rate reflects the change amplitude of the control variable per unit time. The online rolling optimization algorithm is a real-time optimization method that dynamically adjusts the PID parameters according to the current production state and optimization objectives to achieve global optimal control.

[0082] Preferably, the construction of the multi-objective optimization function can be based on the following steps: First, according to the production process requirements and actual production experience, determine the weight coefficients of each optimization objective. For example, if the temperature control accuracy has a greater impact on product quality, the temperature error weight coefficient can be appropriately increased. Then, through experiments or historical data, determine the reasonable range of each optimization objective. For example, set the target value of the temperature error to zero, and the allowable fluctuation range to plus or minus one degree; set the target value of the pressure error to zero, and the allowable fluctuation range to plus or minus 0.1 MPa; set the target value of the control variable change rate to zero, and the maximum allowable change rate to 0.5% per minute. The implementation of the online rolling optimization algorithm can be divided into the following steps: First, collect the current temperature error, pressure error, and control variable change rate in real time; then, calculate the current optimization objective value according to the multi-objective optimization function; next, dynamically adjust the PID parameters through the optimization algorithm to minimize the optimization objective value. For example, when the temperature error is large, appropriately increase the proportional coefficient to speed up the response speed; when the pressure fluctuation is large, appropriately adjust the integral time to reduce the steady-state error. In this way, the control system can dynamically adjust the PID parameters under different working conditions to achieve global optimal control and improve the stability of the production process and product quality.

[0083] The above embodiments of the present invention have the following beneficial effects: The present invention can improve the control accuracy and stability in the production process of cross-linked cables. By establishing a three-loop collaborative control architecture for temperature, pressure, and traction speed, the PID parameters can be dynamically adjusted to adapt to real-time working conditions, converging the steady-state error of the vulcanization pipe temperature to zero, and at the same time controlling the fluctuations of the extruder pressure and traction speed within the process allowable range. The use of distributed temperature sensors and infrared thermal imagers can monitor the temperature field uniformity and the cross-linking state of the insulation layer in real time. Combining feed-forward compensation and anti-integral saturation processing can predict and compensate for process disturbances in advance, effectively avoiding quality problems such as bubble defects.

[0084] By establishing a material viscosity-temperature transfer function model and a traction speed disturbance observer, precise feed-forward compensation can be achieved in a large-delay system. The multi-objective optimization function based on expert rules can coordinate the weights of temperature, pressure, and the change rate of the control quantity. Through the online rolling optimization algorithm, the parameters of each control loop can be dynamically adjusted to achieve global optimal control. The self-tuning factor set for different working conditions can trigger a hierarchical adjustment strategy, from conventional PID adjustment to emergency cooling intervention, ensuring the reliable operation of the production system under various abnormal working conditions. This improved PID control method can improve the product consistency and production efficiency of cross-linked cables.

[0085] Furthermore, the storage medium of the embodiment of the present application stores program instructions capable of implementing all the above methods. Among them, the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, or terminal devices such as computers, servers, mobile phones, and tablets.

[0086] The above description is only some preferred embodiments of the present invention and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features having similar functions disclosed in the embodiments of the present invention.

Claims

1. A production control method for cross-linked cables based on an improved PID control algorithm, characterized in that, It includes the following steps: establishing a collaborative control architecture for temperature, pressure, and traction speed in the production process of cross-linked cables; taking the temperature of the vulcanizing tube as the main control variable, the pressure of the extruder and the speed of the traction wheel as the slave control variables, and constructing an optimal reference model based on process parameters; The main controller tracks the set value of the vulcanizing tube temperature in real time, and performs dynamic tuning of PID parameters according to the process parameters collected in real time, including the deviation of the insulation layer thickness and the cross-linking degree of the material, to obtain the adjustment strategies for the proportional coefficient, integral time, and derivative time to adapt to the real-time working conditions; Precisely control the vulcanizing tube heating system through an improved PID controller with three-loop collaborative coupling; enable the temperature control loop to track the set value of the reference model; Through tracking, make the steady-state error between the actual temperature of the vulcanizing tube and the set value converge to zero; make the fluctuation range of the extruder pressure and the traction speed converge to the error range allowed by the process.

2. The production control method of the cross-linked cable according to claim 1, characterized in that The method for obtaining the adjustment strategies for the proportional coefficient, integral time, and derivative time includes: in the cable extrusion and forming stage, establishing an initial PID parameter group based on the preset process parameter reference values; the main controller collects the temperature distribution of 9 points along the axis of the vulcanizing tube in real time through a distributed temperature sensor, and calculates the temperature field uniformity coefficient; generating a parameter correction instruction according to the temperature field uniformity coefficient and sending it to the slave controller, so that the extrusion pressure control loop and the traction speed control loop obtain corresponding compensation coefficients.

3. The cross-linked cable production control method according to claim 2, wherein, The main controller monitors the cross-linking state of the insulation layer in real time through an infrared thermal imager, and obtains the extrusion die head pressure fluctuation data of the extruder through a pressure transmitter; extracts the standard deviation of the temperature gradient, calculates the material residence time, and detects and identifies the bubble defect characteristics; Taking the detected abnormal temperature point as a parameter correction point, perform anti-saturation processing on the PID integral term to obtain an optimized control quantity output.

4. The cross-linked cable production control method according to claim 3, wherein By establishing a material viscosity-temperature transfer function model and setting a feed-forward compensation link, make the adjustment of the extruder screw speed ahead of the temperature change of the vulcanizing tube.

5. The cross-linked cable production control method according to claim 2, characterized in that, The main controller takes the ratio of the measured maximum axial temperature difference to the process-allowed temperature difference as the PID parameter self-tuning factor.

6. The cross-linked cable production control method according to claim 5, characterized in that, Generating a parameter correction instruction according to the self-tuning factor includes: when the self-tuning factor is less than 0.5, maintaining the current PID parameter group and only enabling the feed-forward compensation channel; when the self-tuning factor is in the range of 0.5 - 1.0, activating the proportional coefficient adaptive adjustment module and dynamically adjusting the proportional band according to where, is the real-time adjustment amount of the proportional coefficient; is the adaptive adjustment coefficient; is the absolute value of the temperature deviation; when the self-tuning factor is in the range of 1.0 - 1.5, activating the fuzzy PID switching mechanism and replacing the integral term with a variable-speed integral algorithm; when the self-tuning factor is greater than 1.5, triggering an emergency adjustment mode, adjusting the three control parameters simultaneously and starting the cooling system for auxiliary intervention.

7. The production control method for cross-linked cables according to any one of claims 1-6, characterized in that, The control method of the improved PID controller with triple-loop collaborative coupling includes: establishing a second-order lag model for the vulcanizing tube temperature control loop: ; where is the equivalent gain coefficient of the vulcanizing tube heating system; is the thermal inertia time constant of the vulcanizing tube; is the heat conduction delay time; designing an anti-integral saturation PID control law: ; in the formula, is the controller output, is the proportional coefficient; is the temperature deviation, is the integral time constant; is the variable-speed integral factor, which automatically attenuates the integral term when is greater than the threshold; is the derivative time constant; is the feedforward compensation amount; coupling a cascade control loop of the extruder pressure and the traction speed at the output end of the temperature control loop to form a triple-loop collaborative control architecture.

8. The cross-linked cable production control method according to claim 7, characterized in that, The feed-forward compensation amount is dynamically calculated by establishing a correlation model between the material extrusion amount and the heating power.

9. The cross-linked cable production control method according to claim 8, wherein In the correlation model: set a traction speed disturbance observer to estimate the influence factor of speed fluctuation on the temperature field in real time; perform weighted fusion on the output of the observer and the real-time data of the pressure sensor to generate a feed-forward compensation coefficient; Achieve precise distribution of the compensation amount by dynamically adjusting the weighting factor to solve the overshoot problem in the large-delay system.

10. The production control method of the cross-linked cable according to claim 9, characterized in that, Establish a multi-objective optimization function based on expert rules: ; where is the optimization index; is the temperature error weight coefficient, is the pressure error weight coefficient, is the control variable change rate weight coefficient, is the vulcanizing tube temperature tracking error; is the extruder pressure fluctuation; is the control variable change rate; Dynamically adjust the PID parameters of each loop through an online rolling optimization algorithm to achieve the global optimal control of the production process.

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