Intelligent self-adaptive spinning tension control method and system

Abstract

The invention belongs to the technical field of spinning tension control, particularly relates to an intelligent self-adaptive spinning tension control method and system, and aims to solve the problems that an existing traditional tension control method often depends on simple PID (Proportion Integration Differentiation) control and is difficult to deal with complex dynamic changes, and the control precision is high. In order to solve the problems of low tension control precision, low response speed and incapability of adapting to various textile materials and process requirements in the prior art, the invention provides the following scheme: the method comprises the following steps of: 1, establishing a detailed tension dynamic model based on the Newton's second law and the quality, acceleration, friction force, gravitational acceleration and external disturbance factors of a fabric in a textile process; 2, converting a kinetic equation of a time domain into a complex frequency domain, and establishing a transfer function of a Laplacian domain to describe the response of the system to input and disturbance; the response speed and stability of the control system are improved, the controller further has the adaptive capacity, and control parameters can be adjusted according to real-time feedback so as to meet control requirements under different working conditions.

Description

Technical Field

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

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

[0003] In the textile process, tension is affected by many factors, such as fabric quality, acceleration, friction, and external disturbances (such as raw material non-uniformity, changes in equipment operating status, etc.). The changes of these factors are often complex and nonlinear, and the PID control method is based on linear model design, which is difficult to accurately capture and process these complex changes.

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

[0005] When faced with rapidly changing tension demands, the PID control method may not respond fast enough, resulting in an inability to adjust the tension in time to maintain a stable production state.

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

[0007] The present invention provides an intelligent adaptive spinning tension control method and system, which solves the problems in the prior art that traditional tension control methods often rely on simple PID control, which is difficult to cope with complex dynamic changes, resulting in low tension control accuracy, slow response speed, and inability to adapt to a variety of 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 in that it includes 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 fabric in the weaving process, a detailed tension dynamic model is established; by considering the physical properties and force conditions of the fabric, the dynamic changes of tension in the weaving process can be accurately described. By establishing a tension dynamic model that includes external disturbance factors, the control system can identify and compensate for the impact of these disturbances on tension control, enhance the adaptability and robustness of the control system, and enable it to maintain stable control performance in a complex and changeable textile environment.

[0011] 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; Laplace transform converts the dynamic equations in the time domain to the expression form in the complex frequency domain, so that the dynamic characteristics of the system can be clearly displayed on the complex plane, and a more in-depth analysis of the system can be performed, such as stability analysis, frequency response analysis, etc. In the complex frequency domain, the dynamic characteristics of the system can be described by the transfer function. Through the appropriate transfer function, the design goals of the control system can be easily achieved, such as improving the response speed and reducing the overshoot, etc. In the Laplace domain, various control strategies can also be easily introduced, such as PID control, adaptive control, etc. This can be achieved by adjusting the parameters of the transfer function, thereby optimizing the dynamic characteristics of the system and more accurately predicting the response of the system to input and disturbance.

[0012] Step 3: Through system monitoring and data analysis, identify various disturbance sources that may affect spinning tension, including raw material unevenness, equipment operating status, environmental factors and changes in operating conditions; identifying disturbance sources is the basis for precise control of spinning tension. Understanding the way and degree of influence of different disturbance sources on tension will help introduce corresponding compensation measures in the control system, thereby reducing the impact of disturbances on tension stability and improving control accuracy.

[0013] Step 4: Classify the disturbance sources into deterministic disturbances and random disturbances, and establish a mathematical model for the deterministic disturbances. Apply Laplace transform to convert the disturbance model into the complex frequency domain. Classifying the disturbance sources can more accurately understand 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: 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;

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

[0016] Step 7: Based on the real-time monitoring data, the system will make feedback adjustments to the controller parameters.

[0017] In one possible implementation, according to Newton's second law F=ma, force equals mass multiplied by acceleration, a dynamic equation is established for the fabric, which can be simplified to:

[0018] T = m·a + u·m·g, where: T is the total tension acting on the fabric, m is the unit weight of the fabric,

[0019] where is the mass per unit length, a is the acceleration of the fabric, μ is the friction coefficient, and g is the acceleration due to gravity.

[0020] Use Laplace transform to convert the above dynamic equation into:

[0021] T(s)=m·s 2 ·V(s)+μ·m·g·s

[0022] Where: s is the complex variable of Laplace transform, V(s) is the Laplace transform of fabric velocity.

[0023] Integrate the dynamic equations and parameters to establish the transfer function in the Laplace domain. For a simple first-order system, the transfer function can be expressed as:

[0024]

[0025] Where: T(s) is the tension transfer function; V(s) is the Laplace transform of the fabric speed; K is the proportional constant, which reflects the response intensity of the system to the change of 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.

[0026] The unevenness of raw materials such as different fiber lengths and thicknesses, the operating conditions of equipment such as mechanical vibrations and bearing wear, environmental factors such as changes in temperature and humidity, and changes in operating conditions such as fluctuations in loom speed.

[0027] For deterministic disturbances, their impact on tension can be described by mathematical equations, and the following model can be established:

[0028] D(t)=B·sin(ωt+φ)

[0029] 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;

[0030] Applying the Laplace transform yields: Where: D(s) is the Laplace transform of the disturbance, and L{D(t)} represents the Laplace transform operation.

[0031] The control model uses the following formula:

[0032]

[0033] 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.

[0034] A control system includes various sensors installed on a textile machine for real-time monitoring of fabric 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 that feeds back the actual output of the actuator to the control system in real time.

[0035] It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention.

[0036] In the present invention, by establishing a tension dynamic model and a disturbance model, the method can comprehensively consider various factors affecting textile tension, such as fabric mass, acceleration, friction coefficient, gravity acceleration, and external disturbance sources (such as raw material non-uniformity, equipment operating status, environmental factors, and changes in operating conditions).

[0037] Based on the above mathematical model, a special controller was designed. The controller can achieve precise control of textile tension by adjusting the control input (such as motor driving force, braking force, etc.). The controller design takes into account the dynamic characteristics and disturbance effects of the system, and improves the response speed and stability of the control system by introducing control parameters such as proportion, integration, and differentiation. In addition, the controller also has adaptive capabilities and can adjust control parameters according to real-time feedback to meet control requirements under different working conditions.

[0038] By monitoring the changes in textile tension in real time, comparing the feedback signal with the set value, and adjusting the controller parameters according to the comparison results, this closed-loop control strategy can ensure that the system always maintains high control accuracy and stability during actual operation. At the same time, by continuously learning and optimizing control parameters, the system can gradually adapt to various complex working conditions and achieve self-optimization and improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of a flow chart provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The embodiments of the present invention are described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0041] In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly specified and limited, 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, "connection" can be a direct connection or an indirect connection through an intermediate medium. Among them, "fixed" means that they are connected to each other and the relative position relationship after connection remains unchanged. The directional terms mentioned in the embodiments of the present invention, such as "inside", "outside", "top", "bottom", etc., are only reference to the directions of the accompanying drawings. Therefore, the directional terms used are for better and clearer explanation 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 therefore cannot be understood as a limitation on the embodiments of the present invention.

[0042] In the embodiments of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0043] In the embodiments of the present invention, "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0044] Reference Figure 1 , an intelligent adaptive spinning tension control method, which monitors key parameters such as tension, speed, and position in real time through sensors installed on the spinning machine;

[0045] Based on the real-time monitoring data, the system will make feedback adjustments to the controller parameters.

[0046] 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, gravity acceleration and external disturbance factors of the fabric 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 fabric. In the textile process, it can be simplified as:

[0047] T = m·a + u·m·g, where: T is the total tension acting on the fabric, m is the unit weight of the fabric,

[0048] The total tension currently acting on the fabric can be accurately calculated by real-time monitoring of the fabric's speed, acceleration and other parameters, combined with the model's predictive ability. At the same time, considering the influence of external disturbance factors (such as raw material unevenness, equipment operating status, environmental factors and changes in operating conditions) on 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 based on real-time feedback signals 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.

[0049] The dynamic equation in the time domain is converted to the complex frequency domain, and the transfer function in the Laplace domain is established to describe the response of the system to input and disturbance. The Laplace transform is used to convert the above dynamic equation into:

[0050] T(s)=m·s 2 ·V(s)+μ·m·g·s

[0051] Where: s is the complex variable of Laplace transform, V(s) is the Laplace transform of fabric velocity, Laplace transform can transform 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 on the complex plane.

[0052] Integrate the dynamic equations and parameters to establish the transfer function in the Laplace domain. For a simple first-order system, the transfer function can be expressed as:

[0053]

[0054] Where: T(s) is the tension transfer function; V(s) is the Laplace transform of the fabric 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. The transfer function accurately describes the dynamic characteristics of the system in the Laplace domain, including the system's response to the input signal, the system's stability and transient response characteristics, etc. Based on the transfer function, a controller that meets specific performance indicators can be designed. For example, the characteristics of the transfer function can be changed by adjusting the controller parameters, thereby optimizing the system performance. The key indicators of the system such as steady-state error, transient response, bandwidth, stability, etc. can be evaluated through the transfer function. Under the condition of a given input signal, the output response of the system can be predicted through the transfer function to achieve real-time control and fault diagnosis.

[0055] Through system monitoring and data analysis, various disturbance sources that may affect spinning tension are identified, including raw material non-uniformity, equipment operating status, environmental factors and changes in operating conditions; raw material non-uniformity such as fiber length and thickness, equipment operating status such as mechanical vibration, bearing wear, environmental factors such as temperature and humidity changes, and changes in operating conditions such as fluctuations in loom speed. Disturbance sources are classified into deterministic disturbances and random disturbances, and a mathematical model is established for deterministic disturbances. The Laplace transform is used to convert the disturbance model into the complex frequency domain; for deterministic disturbances, mathematical equations can be used to describe their impact on tension, and the following model can be established:

[0056] D(t)=B·sin(ωt+φ)

[0057] 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;

[0058] Applying the Laplace transform yields:

[0059]

[0060] Where: D(s) is the Laplace transform of the disturbance, and L{D(t)} represents the Laplace transform operation.

[0061] The problem-solving process can be simplified by Laplace transform. In the Laplace domain, the impact of disturbances on the system output can be easily analyzed. By observing the characteristics of D(s), such as amplitude, phase, and frequency, we can gain a deep understanding of how disturbances affect the dynamic behavior of the system. After understanding the characteristics of the disturbance signal in the Laplace domain, we can design a robust controller to suppress the impact of the disturbance. By adjusting the controller parameters, the system can have better resistance to disturbances of specific frequency and amplitude. By using the linear properties of Laplace transform, we can combine the disturbance signal with the system transfer function to predict the response of the system under the disturbance, so as to take measures in advance to reduce the impact of the disturbance on the system performance.

[0062] An intelligent algorithm is used 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, and design an adaptive controller whose transfer function can automatically adjust the proportional, integral and differential parameters according to the system state and disturbance. 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:

[0063]

[0064] 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.

[0065] By introducing the tension transfer function T(s), the model can accurately reflect the dynamic relationship between the system output (tension) and input (control input), thereby achieving precise control of tension. The introduction of the disturbance term D(s) allows the model to consider the impact of various external and internal disturbances on the system. Through the controller P(s), these disturbances can be effectively suppressed to improve 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 PID control, fuzzy control, adaptive control, etc. The flexibility enables the model to be applicable to different textile processes and materials to meet diverse production needs. Through the model, the output response of the system under given control input and disturbance can be predicted. This helps to discover 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 capability enables the system to respond quickly to external disturbances and internal changes and maintain stable tension control.

[0066] The implementation steps of the control model are as follows: first, test the textile process, and obtain the speed and tension data of the textile through the data of speed sensors deployed in the textile system; use the maximum likelihood estimation method in the identification algorithm to calculate the model parameter proportional parameter K, steady-state time parameter T_m, amplitude B, frequency ω and phase φ.

[0067] The controller P(s) is designed based on the identified model parameters. The controller design can be done using the following formula: Where: s is the variable of 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 system deviations, reduce the amplitude of deviations, and make the system quickly approach the target state, k_i is the integral parameter of the controller transfer function, which helps to eliminate the steady-state error of the system and improve the control accuracy by compensating for the accumulation of deviations, k_d is the differential parameter of the controller transfer function, which can predict the changing trend of the deviation and make corrections in advance, thereby suppressing the further increase of the deviation and improving the stability and dynamic performance of the system. It can adapt to the characteristics and control requirements of different controlled objects and has strong adaptability and robustness.

[0068] A control system includes various sensors installed on a textile machine for real-time monitoring of fabric 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 that feeds back the actual output of the actuator to the control system in real time.

Claims

1. Intelligent adaptive spinning tension control method, characterized in that: The steps include: Step 1: Establish a detailed tension dynamic model based on Newton's second law and the mass, acceleration, friction, gravity acceleration and external disturbance factors of the fabric during the weaving 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: Through system monitoring and data analysis, identify various disturbance sources that may affect spinning tension, 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: Monitor key parameters such as tension, speed, and position in real time 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.

2. The intelligent adaptive spinning tension control method according to claim 1, characterized in that: According to Newton's second law F=ma, the dynamic equation for the fabric is established. During the textile process, it can be simplified to: T=m·a+u·m·g, where: T is the total tension acting on the fabric, m is the mass per unit length of the fabric, a is the acceleration of the fabric, μ is the friction coefficient, and g is the acceleration due to gravity.

3. The intelligent adaptive spinning tension control method according to claim 2, characterized in that: Use Laplace transform to convert the above dynamic equation into: T(s)=m·s 2 ·V(s)+μ·m·g·s Where: s is the complex variable of Laplace transform, V(s) is the Laplace transform of fabric velocity.

4. The intelligent adaptive spinning tension control method according to claim 3, characterized in that: Integrate the dynamic equations and parameters to establish the transfer function in the Laplace domain. For a simple first-order system, the transfer function can be expressed as: Where: T(s) is the tension transfer function; V(s) is the Laplace transform of the fabric speed; K is the proportional constant, which reflects the response intensity of the system to the change of 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.

5. The intelligent adaptive spinning tension control method according to claim 4, characterized in that: For deterministic disturbances, their impact on tension can be described by mathematical equations, and the following model can be established: D(t)=B·sin(ωt+φ) 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, and L{D(t)} represents the Laplace transform operation.

6. The intelligent adaptive spinning tension control method according to claim 5, characterized in that: 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.

7. A control system for implementing the intelligent adaptive spinning tension control method according to any one of claims 1 to 6, characterized in that: It includes various sensors installed on the textile machine for real-time monitoring of the speed, tension, and position parameters of the fabric, 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 that feeds back the actual output of the actuator to the control system in real time.

Images