Intelligent Adaptive Spinning Tension Control Method and System

Abstract

The present invention belongs to the technical field of spinning tension control, especially an intelligent adaptive spinning tension control method and system. Aiming at the problem that existing traditional tension control methods often rely on simple PID control, and these methods are difficult to cope with complex dynamic changes, resulting in low tension control accuracy, slow response speed, and inability to adapt to the requirements of various textile materials and processes, the following solutions are proposed, including the following steps: Step 1: Based on Newton's second law and the mass, acceleration, friction, gravitational acceleration, and external disturbance factors of the yarn in the textile process, establish a detailed tension dynamic model; Step 2: Convert the dynamic equation in the time domain into the complex frequency domain, and establish a transfer function in the Laplace domain to describe the response of the system to input and disturbance. This application improves the response speed and stability of the control system. The controller also has an adaptive ability and can adjust the control parameters according to real-time feedback to meet the control requirements under different working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of spinning tension control, and particularly to an intelligent adaptive spinning tension control method and system. Background Art

[0002] In the textile production process, tension control is a key factor affecting product quality and production efficiency. Although the traditional PID control method can achieve basic control of tension to a certain extent, its control effect is often unsatisfactory when facing complex and changeable textile environments and diverse material and process requirements.

[0003] During the textile process, tension is affected by various factors, such as the quality of the yarn, acceleration, friction, and external disturbances (such as raw material non-uniformity, changes in equipment operating conditions, etc.). The changes of these factors are often complex and non-linear, and the PID control method is designed based on a linear model, making it difficult to accurately capture and handle these complex changes.

[0004] Due to the limitations of the PID control method in dealing with complex dynamic changes, it is difficult to achieve an ideal level of control accuracy for tension. In actual production, large tension fluctuations will affect the quality of textiles, such as causing wrinkles, uneven tightness, and other problems.

[0005] When facing rapidly changing tension requirements, the response speed of the PID control method may not be fast enough, resulting in the inability to adjust the tension in a timely manner to maintain a stable production state.

[0006] Different textile materials and process requirements have different requirements for tension control, and the parameter settings of the PID control method are often fixed and difficult to adapt to diverse production requirements. Summary of the Invention

[0007] The present invention provides an intelligent adaptive spinning tension control method and system, which solve the disadvantages in the prior art that the existing traditional tension control methods often rely on simple PID control, and these methods are difficult to cope with complex dynamic changes, resulting in low tension control accuracy, slow response speed, and inability to adapt to various textile materials and process requirements.

[0008] The present invention provides the following technical solutions:

[0009] On the one hand, the present application provides an intelligent adaptive spinning tension control method, characterized by including the following steps:

[0010] Step 1: Based on Newton's second law and the mass, acceleration, friction, gravitational acceleration, and external disturbance factors of the yarn during the textile process, establish a detailed dynamic tension model. By considering the physical properties and force conditions of the yarn, it can accurately describe the dynamic changes of tension during the textile process. By establishing a dynamic tension model that includes external disturbance factors, the control system can identify and compensate for the impact of these disturbances on tension control, enhancing the adaptability and robustness of the control system, enabling it to maintain stable control performance in a complex and changing textile environment.

[0011] Step 2: Convert the dynamic equation in the time domain to the complex frequency domain and establish a transfer function in the Laplace domain to describe the system's response to inputs and disturbances. The Laplace transform converts the dynamic equation in the time domain into an expression in the complex frequency domain, enabling the clear display of the system's dynamic characteristics in the complex plane and allowing for a more in-depth analysis of the system, such as stability analysis and frequency response analysis. In the complex frequency domain, the dynamic characteristics of the system can be described by the transfer function. By using an appropriate transfer function, it is convenient to achieve the design goals of the control system, such as increasing the response speed and reducing the overshoot. In the Laplace domain, various control strategies, such as PID control and adaptive control, can also be easily introduced. This can be achieved by adjusting the parameters of the transfer function, thereby optimizing the dynamic characteristics of the system and enabling a more accurate prediction of the system's response to inputs and disturbances.

[0012] Step 3: Through system monitoring and data analysis, identify various disturbance sources that may affect the spinning tension, including raw material non-uniformity, equipment operating status, environmental factors, and changes in operating conditions. Identifying the disturbance sources is the basis for precisely controlling the spinning tension. Understanding the ways and degrees of influence of different disturbance sources on tension helps introduce corresponding compensation measures in the control system, thereby reducing the impact of disturbances on tension stability and improving the control accuracy.

[0013] Step 4: Classify the disturbance sources into deterministic disturbances and random disturbances, establish a mathematical model for the deterministic disturbances, and apply the Laplace transform to convert the disturbance model into the complex frequency domain. Classifying the disturbance sources can provide a more precise understanding of their impact on the system. Deterministic disturbances have predictable patterns and can be described by mathematical equations, thereby improving the accuracy of the control model.

[0014] Step 5: Adopt an intelligent algorithm to analyze and predict the tension changes during the spinning process. The algorithm will automatically adjust the control parameters based on historical data and real-time monitoring information, design an adaptive controller whose transfer function can automatically adjust the proportional, integral, and derivative parameters according to the system state and disturbance conditions. The controller calculates the control input in real-time and outputs it to the actuator of the spinning machine.

[0015] Step 6: Real-time monitor key parameters such as tension, speed, and position through sensors installed on the spinning machine;

[0016] Step 7: Based on the real-time monitored data, the system will perform feedback adjustment on the parameters of the controller.

[0017] In a possible implementation, according to Newton's second law F = ma, where force equals mass multiplied by acceleration, a dynamic equation is established for the yarn. During the textile process, it can be simplified to: , where: T is the total tension acting on the yarn, m is the mass per unit length of the yarn, a is the acceleration of the yarn, μ is the friction coefficient, and g is the acceleration due to gravity.

[0018] Use Laplace transform to convert the above dynamic equation to:

[0019]

[0020] where: s is the complex variable of the Laplace transform, and V(s) is the Laplace transform of the yarn speed.

[0021] Integrate the dynamic equation and parameters to establish a transfer function in the Laplace domain. For a simple first-order system, the transfer function can be expressed as:

[0022]

[0023] where: T(s) is the tension transfer function; V(s) is the Laplace transform of the yarn speed; K is the proportionality constant, reflecting the response intensity of the system to changes in the input speed; T_m is the time constant, reflecting the time required for the system to reach a new steady state; s is the variable of the Laplace transform.

[0024] Inhomogeneity of raw materials such as different fiber lengths and thicknesses, operating conditions of equipment such as mechanical vibration and bearing wear, environmental factors such as temperature and humidity changes, and changes in operating conditions such as fluctuations in loom speed.

[0025] For deterministic disturbances, the impact on tension can be described by a mathematical equation, and the following model can be established:

[0026]

[0027] where: D(t) is the time function of the disturbance signal, B is the amplitude of the disturbance, ω is the frequency of the disturbance, and ϕ is the phase of the disturbance;

[0028] Apply Laplace transform to obtain: where: D(s) is the Laplace transform of the disturbance, and L{D(t)} represents the Laplace transform operation.

[0029] The control model adopts the following formula:

[0030]

[0031] Where: U(s) is the Laplace transform of the control input; T(s) is the tension transfer function; D(s) is the Laplace transform of the disturbance; P(s) is the transfer function of the controller.

[0032] A control system includes various sensors installed on a textile machine for real-time monitoring of yarn speed, tension, and position parameters, a data processing unit for preprocessing, filtering, and denoising the collected data, actuators such as motors, cylinders, and hydraulic devices, an intelligent algorithm module for predicting and analyzing tension changes during the textile process, and a feedback loop for real-time feedback of the actual output of the actuator to the control system.

[0033] It should be understood that the above general description and the following detailed description are only exemplary and do not limit the present invention.

[0034] In the present invention, by establishing a tension dynamic model and a disturbance model, this method can comprehensively consider various factors affecting textile tension, such as yarn quality, acceleration, friction coefficient, gravitational acceleration, and external disturbance sources (such as raw material inhomogeneity, equipment operating conditions, environmental factors, and changes in operating conditions).

[0035] Based on the above mathematical model, a dedicated controller is designed. The controller adjusts the control input (such as motor driving force, braking force, etc.) to achieve precise control of textile tension. The controller design considers the dynamic characteristics of the system and the influence of disturbances, and improves the response speed and stability of the control system by introducing control parameters such as proportional, integral, and differential. In addition, the controller also has an adaptive ability and can adjust the control parameters according to real-time feedback to meet the control requirements under different working conditions.

[0036] By real-time monitoring the change of textile tension, comparing the feedback signal with the set value, and adjusting the parameters of the controller according to the comparison result. This closed-loop control strategy can ensure that the system always maintains a high control accuracy and stability during actual operation. At the same time, by continuously learning and optimizing the control parameters, the system can gradually adapt to various complex working conditions and achieve self-optimization and improvement. Brief Description of the Drawings

[0037] Figure 1 It is a schematic flow chart provided by an embodiment of the present invention. Detailed Embodiment

[0038] The embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention.

[0039] In the description of the embodiments of the present invention, it should be noted that unless otherwise clearly specified and defined, the terms "connection" and "installation" should be understood in a broad sense. For example, "connection" can be a detachable connection or a non-detachable connection; it can be a direct connection or an indirect connection through an intermediate medium. In addition, "communication" can be a direct communication or an indirect communication through an intermediate medium. Among them, "fixing" means that they are connected to each other and the relative positional relationship after connection remains unchanged. The orientation terms mentioned in the embodiments of the present invention, such as "inside", "outside", "top", "bottom", etc., are only references to the direction of the attached drawings. Therefore, the orientation terms used are for better and clearer description and understanding of the embodiments of the present invention, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation on the embodiments of the present invention.

[0040] In the embodiments of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features.

[0041] In the embodiments of the present invention, "and / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the front and rear associated objects.

[0042] Refer to Figure 1 , an intelligent adaptive spinning tension control method, which real-time monitors key parameters such as tension, speed, and position through sensors installed on the spinning machine;

[0043] According to the real-time monitored data, the system will feedback and adjust the parameters of the controller.

[0044] The intelligent adaptive spinning tension control method includes establishing a detailed tension dynamic model based on Newton's second law and the mass, acceleration, friction, gravitational acceleration, and external disturbance factors of the yarn in the textile process. According to Newton's second law F = ma (force equals mass multiplied by acceleration), a dynamic equation is established for the yarn. In the textile process, it can be simplified to: , where: T is the total tension acting on the yarn, m is the mass per unit length of the yarn, a is the acceleration of the yarn, μ is the friction coefficient, and g is the acceleration due to gravity. By real-time monitoring of parameters such as the speed and acceleration of the yarn and combining with the prediction ability of the model, the total tension currently acting on the yarn can be accurately calculated. At the same time, considering the influence of external disturbance factors (such as raw material inhomogeneity, equipment operating conditions, environmental factors, and changes in operating conditions) on the tension, the system can automatically adjust the control strategy to maintain a stable tension output. During the control process, the intelligent adaptive system will also correct and optimize the model parameters according to the real-time feedback signal to improve the accuracy and adaptability of the control. This closed-loop control strategy ensures that the spinning tension can be maintained within the optimal range under different working conditions, thereby improving the quality and production efficiency of textile products.

[0045] Convert the dynamic equation in the time domain to the complex frequency domain and establish the transfer function in the Laplace domain to describe the response of the system to inputs and disturbances. Use the Laplace transform to convert the above dynamic equation to:

[0046]

[0047] where: s is the complex variable of the Laplace transform, and V(s) is the Laplace transform of the yarn speed. The Laplace transform can convert complex differential equations into simple algebraic equations, simplifying the solution process and making the problem easier to handle. In the Laplace domain, the system performance can be evaluated by analyzing the characteristics of its transfer function in the complex plane.

[0048] Integrate the dynamic equation and parameters to establish the transfer function in the Laplace domain. For a simple first-order system, the transfer function can be expressed as:

[0049]

[0050] where: T(s) is the tension transfer function; V(s) is the Laplace transform of the yarn speed; K is the proportionality constant, which reflects the response intensity of the system to changes in the input speed; T_m is the time constant, which reflects the time required for the system to reach a new steady state; s is the variable of the Laplace transform. The transfer function accurately describes the dynamic characteristics of the system in the Laplace domain, including the way the system responds to input signals, the stability of the system, and transient response characteristics, etc. Based on the transfer function, a controller that meets specific performance indicators can be designed. For example, by adjusting the controller parameters to change the characteristics of the transfer function, the optimization of the system performance can be achieved. Through the transfer function, key indicators such as the steady-state error, transient response, bandwidth, and stability of the system can be evaluated. Given an input signal, the output response of the system can be predicted through the transfer function to achieve real-time control and fault diagnosis.

[0051] Through system monitoring and data analysis, identify various disturbance sources that may affect the spinning tension, including raw material non-uniformity, equipment operating status, environmental factors, and changes in operating conditions; the non-uniformity of raw materials such as uneven fiber length and thickness, the operating status of equipment such as mechanical vibration and bearing wear, environmental factors such as temperature and humidity changes, and changes in operating conditions such as fluctuations in loom speed. Classify the disturbance sources into deterministic disturbances and random disturbances, and establish a mathematical model for deterministic disturbances. Apply the Laplace transform to convert the disturbance model into the complex frequency domain; for deterministic disturbances, its impact on the tension can be described by a mathematical equation, and the following model can be established:

[0052]

[0053] Where: D(t) is the time function of the disturbance signal, B is the amplitude of the disturbance, ω is the frequency of the disturbance, and ϕ is the phase of the disturbance;

[0054] Applying the Laplace transform gives:

[0055]

[0056] Where: D(s) is the Laplace transform of the disturbance, and L{D(t)} represents the Laplace transform operation.

[0057] The Laplace transform simplifies the problem-solving process. In the Laplace domain, it is convenient to analyze the impact of disturbances on the system output. By observing the characteristics of D(s), such as amplitude, phase, and frequency, etc., we can deeply understand how disturbances affect the dynamic behavior of the system. After understanding the characteristics of the disturbance signal in the Laplace domain, design a robust controller to suppress the impact of disturbances. By adjusting the controller parameters, the system can have better resistance to disturbances with specific frequencies and amplitudes. Utilizing the linear property of the Laplace transform, we can combine the disturbance signal with the system transfer function to predict the response of the system under the action of disturbances, and thus take measures in advance to reduce the impact of disturbances on the system performance.

[0058] Adopt an intelligent algorithm to analyze and predict the tension changes in the spinning process. The algorithm will automatically adjust the control parameters based on historical data and real-time monitoring information, and design an adaptive controller whose transfer function can automatically adjust the proportional, integral, and differential parameters according to the system state and disturbance conditions. The controller calculates the control input in real time and outputs it to the actuator of the spinning machine. The control model uses the following formula:

[0059]

[0060] Where: U(s) is the Laplace transform of the control input; T(s) is the tension transfer function; D(s) is the Laplace transform of the disturbance; P(s) is the transfer function of the controller.

[0061] By introducing the tension transfer function T(s), the model can accurately reflect the dynamic relationship between the system output (tension) and the input (control input), thereby achieving precise control of the tension. The introduction of the disturbance term D(s) allows the model to consider the effects of various external and internal disturbances on the system. Through the controller P(s), these disturbances can be effectively suppressed, improving the robustness and stability of the system. The transfer function P(s) of the controller can be designed according to specific control requirements and system characteristics, such as using PID control, fuzzy control, adaptive control, etc. The flexibility enables the model to be applicable to different textile processes and materials, meeting diverse production requirements. Through the model, the output response of the system under given control inputs and disturbances can be predicted. This helps to detect potential problems in advance and take corresponding optimization measures to improve production efficiency and product quality. The control input U(s) in the model can be adjusted through real-time feedback to cope with various changes in the production process. The real-time control ability enables the system to quickly respond to external disturbances and internal changes, maintaining stable tension control.

[0062] The implementation steps of the control model are as follows: First, test the textile process to obtain the speed and tension data of the textile through the data of sensors such as speed deployed in the textile system. Then, use the maximum likelihood estimation method in the identification algorithm to calculate the model parameters, namely the proportional parameter K, the steady-state time parameter T_m, the amplitude B, the frequency ω, and the phase ϕ.

[0063] Design the controller P(s) according to the identified model parameters. The controller design can use the following formula: , where: s is the variable of the Laplace transform, P(s) is the transfer function of the controller, k_p is the proportional parameter of the controller transfer function, which can quickly respond to the system deviation, reduce the amplitude of the deviation, and make the system quickly approach the target state. k_i is the integral parameter of the controller transfer function, which compensates for the deviation by accumulating the deviation, helps to eliminate the steady-state error of the system, and improves the control accuracy. k_d is the differential parameter of the controller transfer function, which can predict the change trend of the deviation, make corrections in advance, thereby suppressing the further increase of the deviation, improving the stability and dynamic performance of the system, and can adapt to the characteristics and control requirements of different controlled objects, with strong adaptability and robustness.

[0064] A control system includes various sensors installed on the textile machine for real-time monitoring of the speed, tension, and position parameters of the yarn, a data processing unit for preprocessing, filtering, and denoising the collected data, actuators such as motors, cylinders, and hydraulic devices, an intelligent algorithm module for predicting and analyzing the tension changes in the textile process, and a feedback loop for real-time feedback of the actual output of the actuator to the control system.

Claims

1. Intelligent adaptive spinning tension control method, characterized in that: The steps include: Step 1: Establish a tension dynamic model based on Newton's second law and the mass, acceleration, friction, gravity acceleration and external disturbance factors of the yarn in the textile process; Step 2: Convert the dynamic equations in the time domain to the complex frequency domain and establish the transfer function in the Laplace domain to describe the response of the system to input and disturbance; Step 3: Identify disturbance sources that affect spinning tension through system monitoring and data analysis, including raw material unevenness, equipment operating status, environmental factors, and changes in operating conditions; Step 4: Classify the disturbance sources into deterministic disturbances and random disturbances, establish a mathematical model for the deterministic disturbances, and apply Laplace transform to convert the disturbance model into the complex frequency domain; Step 5: Use intelligent algorithms to analyze and predict tension changes during the spinning process. The algorithm will automatically adjust control parameters based on historical data and real-time monitoring information, and design an adaptive controller whose transfer function can automatically adjust proportional, integral and differential parameters according to the system state and disturbance conditions. The controller calculates the control input in real time and outputs it to the actuator of the spinning machine; Step 6: Real-time monitoring of tension, speed, and position parameters through sensors installed on the spinning machine; Step 7: Based on the real-time monitoring data, the system will make feedback adjustments to the controller parameters; Specifically, according to Newton's second law F=ma, the dynamic equation for the yarn is established, which is simplified to: , where: T is the total tension acting on the yarn, m is the mass per unit length of the yarn, a is the acceleration of the yarn, μ is the friction coefficient, and g is the acceleration due to gravity. The above dynamic equation is converted to: Where: s is the complex variable of Laplace transform, V(s) is the Laplace transform of yarn speed, the dynamic equations and parameters are integrated to establish the transfer function in Laplace domain. For a simple first-order system, the transfer function is expressed as: Where: T(s) is the tension transfer function; V(s) is the Laplace transform of the yarn speed; K is the proportional constant, which reflects the response intensity of the system to the input speed change; T_m is the time constant, which reflects the time required for the system to reach a new steady state; s is the variable of the Laplace transform. For deterministic disturbances, mathematical equations are used to describe their influence on tension, and the following model is established: Where: D(t) is the time function of the disturbance signal, B is the amplitude of the disturbance, ω is the frequency of the disturbance, and ϕ is the phase of the disturbance; Applying the Laplace transform yields: Where: D(s) is the Laplace transform of the disturbance, L{D(t)} represents the Laplace transform operation, and the control model uses the following formula: Where: U(s) is the Laplace transform of the control input; T(s) is the tension transfer function; D(s) is the Laplace transform of the disturbance; P(s) is the transfer function of the controller.

2. A control system for implementing the intelligent adaptive spinning tension control method according to claim 1, characterized in that: It includes various sensors installed on the textile machine for real-time monitoring of yarn speed, tension, and position parameters, a data processing unit for preprocessing, filtering, and denoising the collected data, actuators: motors, cylinders, hydraulic devices, an intelligent algorithm module for predicting and analyzing tension changes during the textile process, and a feedback loop that feeds back the actual output of the actuator to the control system in real time.

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