A self-feedback yarn tension control method

Abstract

The present invention relates to the field of yarn textile control, and in particular to a self-feedback yarn tension control method. The self-feedback yarn tension control method provided by the present invention comprises the following steps: obtaining response data of a yarn tension control system; obtaining a predicted response value of yarn tension using the response data; setting a target value of yarn tension; setting a control amount using the target value of yarn tension and the predicted response value of yarn tension; obtaining an actual response value of yarn tension of the control amount; and optimizing the control amount using the actual response value of yarn tension. The self-feedback yarn tension control method provided by the present invention has the advantages of fast response speed, low requirements on model accuracy, strong robustness, etc., and can achieve precise control in complex industrial processes, effectively improve production efficiency and product quality, and has a wide range of application prospects.

Description

Technical Field

[0001] The invention relates to the field of yarn spinning control, in particular to a self-feedback yarn tension control method. Background Art

[0002] With the advancement of science and technology, higher requirements are placed on the technology of control engineering in production and life. In the yarn winding process of the winding machine, the tension and tension fluctuation of the yarn will affect the quality of the product. The traditional winding machine tension control scheme adopts closed-loop control and fuzzy control methods, which can effectively control the size and fluctuation of tension. However, the parameter selection of these control methods depends on the experience of experts and the technical staff's understanding of the problem background. Once faced with new equipment, environment and working conditions, the control parameters need to be redesigned, so there are many disadvantages and it is not predictable.

[0003] The model predictive control (MPC) algorithm adopts control strategies such as multi-step prediction, rolling optimization and feedback correction, and has the advantages of good control effect, strong robustness and low requirement for model accuracy. This method can be effectively used for the control of complex industrial processes and has been verified in process control systems in industrial sectors such as petroleum, chemical, metallurgy, and machinery. Therefore, in view of the lack of predictability of the existing tension control strategy of winding machines, a yarn tension control method based on model predictive control is urgently needed to achieve the purpose of predicting and controlling the winding tension of the winding machine yarn. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides a self-feedback yarn tension control method, which aims to solve the problem that the existing winder tension control strategy lacks predictability. The self-feedback yarn tension control method provided by the present invention comprises the following steps: obtaining the response data of the yarn tension control system; using the response data to obtain the predicted response value of the yarn tension; setting the target value of the yarn tension; using the target value of the yarn tension and the predicted response value of the yarn tension to set the control amount; obtaining the actual response value of the yarn tension of the control amount; and optimizing the control amount using the actual response value of the yarn tension. The self-feedback yarn tension control method provided by the present invention obtains the predicted response value of the yarn tension by obtaining the response data of the yarn tension control system, and sets the control amount in combination with the set target value of the yarn tension, thereby optimizing the yarn tension control, and solves the problem that the existing winder tension control strategy lacks predictability. The self-feedback yarn tension control method provided by the present invention has the advantages of fast response speed, low requirements on model accuracy, strong robustness, etc., and can achieve precise control in complex industrial processes, effectively improve production efficiency and product quality, and has a wide range of application prospects.

[0005] Optionally, the method of using the response data to obtain the predicted response value of yarn tension includes the following steps: constructing the response model of the yarn tension control system through the response data; constructing a yarn tension prediction response model using the yarn tension control system response model; and obtaining the predicted response value of yarn tension through the yarn tension prediction response model. The present invention can more accurately predict the response value of yarn tension and improve control accuracy by building a response model and constructing a prediction model. At the same time, the yarn tension prediction response model can be continuously updated and optimized during the actual operation of the system, thereby making the prediction result more accurate. Therefore, the present invention can better adapt to complex and changeable production environments and process processes, thereby improving the stability and robustness of the control system.

[0006] Further optionally, the yarn tension control system response model includes the following formula: ,in, Indicates k The yarn tension response value at time , Indicates the sampling time sequence number, Indicates the sampling time sequence number when the yarn tension control system response reaches a steady state, Indicates The dynamic coefficient of the moment, Indicates The dynamic coefficient of the moment, Indicates that the yarn tension control system is The control increment of the moment, Indicates that the yarn tension control system is Control increments at a moment.

[0007] Further optionally, the yarn tension prediction response model includes the following formula: ,in, Indicates The predicted response value of yarn tension at time , Indicates the sampling time sequence number, Indicates the sampling time sequence number when the yarn tension control system response reaches a steady state, Indicates The dynamic coefficient of the moment, Indicates The dynamic coefficient of the moment, , Indicates the prediction time sequence number, Indicates that the yarn tension control system is The control increment of the moment, Indicates the yarn tension control system Control increments at a moment.

[0008] Optionally, the method of setting the control amount by using the yarn tension target value and the yarn tension predicted response value comprises the following steps: constructing a control function by using the yarn tension target value and the yarn tension predicted response value; solving the control increment matrix of the yarn tension control system through the control function; and setting the control amount according to the control increment matrix. By constructing a control function and solving the control increment matrix, the present invention can more accurately calculate the control increment that needs to be set, thereby avoiding the error and instability caused by manually setting the control amount. In addition, by setting the control amount according to the obtained control increment, the yarn tension control system can be made more intelligent and adaptive, thereby adapting to different production environments and process processes, and improving control accuracy and production efficiency. Therefore, the self-feedback yarn tension control method provided by the present invention has a wide range of applicability and practicality in practical applications.

[0009] Further optionally, the control function includes the following formula: ,in, represents the yarn tension prediction response matrix, represents the yarn tension expected response matrix, represents the weight coefficient, represents the control increment matrix, Represents matrix transpose.

[0010] Further optionally, the control increment matrix includes the following formula: ,in, represents the control increment matrix, represents the response matrix of the yarn tension control system, represents the weight coefficient, represents the identity matrix, represents the yarn tension expected response matrix, represents the initial quantity matrix of yarn tension prediction response, Represents matrix transpose.

[0011] Optionally, the use of the actual response value of the yarn tension to optimize the control amount includes the following steps: comparing the actual response value of the yarn tension with the predicted response value of the yarn tension to obtain a prediction error model; using the prediction error model to correct the predicted response value of the yarn tension; and using the corrected predicted response value of the yarn tension to optimize the control amount. The present invention can obtain a prediction error model of the yarn tension in real time by comparing the actual response value and the predicted response value of the yarn tension, and use the model to correct the predicted response value, thereby further optimizing the control amount. Therefore, the present invention can achieve more accurate and real-time control, improve production efficiency and quality, and continuously update and optimize the prediction model in actual operation, so as to adapt to the ever-changing production environment and process, and improve the stability and robustness of the control system.

[0012] Further optionally, the prediction error model includes the following formula: ,in, Indicates that the yarn tension control system is inputting control quantity After The actual response value of the yarn tension at the moment, Indicates that the yarn tension control system is inputting control quantity After The predicted value of yarn tension at time Indicates that the yarn tension control system is in control quantity Under the effect The prediction error at that moment.

[0013] Further optionally, the method of correcting the yarn tension prediction response value by using the prediction error model comprises the following steps: correcting the yarn tension prediction response value by using the prediction error model, and the corrected yarn tension prediction response value comprises the following formula: ,in, Indicated in The yarn tension prediction response matrix after time correction, represents the yarn tension prediction response matrix obtained at time k, represents the error correction vector, Indicates that the yarn tension control system is in control quantity Under the effect The prediction error generated at the moment; using the corrected yarn tension prediction response value, the yarn tension prediction response initial quantity matrix is ​​corrected. The corrected yarn tension prediction response initial quantity matrix satisfies the following formula: ,in, Indicated in The initial quantity matrix of the predicted response after time correction, S is the displacement matrix, Indicated in Yarn tension prediction response matrix after time correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart of a self-feedback yarn tension control method provided by an embodiment of the present invention;

[0015] Figure 2 This is a structural diagram of a yarn tension control system provided in an embodiment of the present invention. Implementation

[0016] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0017] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0018] In an alternative embodiment, see Figure 1 , Figure 1 The following is a flow chart of the self-feedback yarn tension control method provided by an embodiment of the present invention. Figure 1 As shown, the self-feedback yarn tension control method comprises the following steps:

[0019] S01. Acquire response data of a yarn tension control system.

[0020] The yarn tension control system is a system used to control the yarn tension in textile production, which is usually composed of sensors, actuators and controllers. The sensor is used to sense the actual value of the yarn tension, the actuator is used to adjust the target value of the yarn tension, and the controller controls the output signal of the actuator according to the actual value obtained by the sensor and the target value set by the actuator, so that the yarn tension is maintained at a stable level, thereby ensuring product quality and production efficiency. The control system usually also adopts some advanced algorithms and technologies, such as PID control algorithm, neural network control, etc., to achieve more accurate and efficient yarn tension control.

[0021] In an alternative embodiment, see Figure 2 , Figure 2 The structure diagram of the yarn tension control system provided by the embodiment of the present invention. Figure 2 As shown, the yarn tension control system described in step S01 includes a sensing module, an execution module and a control module, wherein the sensing module is used to sense the yarn tension response value, the control module is used to generate a control amount according to the yarn tension response value and the yarn tension target value, and the execution module is used to adjust the yarn tension response value according to the control amount to reach the yarn tension target value.

[0022] Specifically, in this embodiment, the sensing module includes a tension sensor 5, the control module includes a single spindle CPU 4, and the control module includes a tension electromagnet 6 and a winding machine 3. Figure 2 As shown, the yarn 1 passes through the tension electromagnet 6 and is wound onto the grooved drum 2 of the winding machine, and the grooved drum 2 is driven by the grooved drum motor to perform winding. A tension sensor 5 is provided between the tension electromagnet 6 and the grooved drum 2, and the tension sensor 5 is used to detect the yarn tension response value. The single spindle CPU 4 generates a control amount (control signal) by combining the yarn tension response value with the set yarn tension target value to control the operation of the tension electromagnet 6 and the winding machine 3 so that the yarn tension reaches the yarn tension target value.

[0023] It should be understood that the tension electromagnet is an electromagnetic device that contains a certain amount of wire and an iron core. When current is passed through it, a magnetic field is generated around the iron core. During the spinning process, the tension electromagnet is usually installed on the path where the yarn passes. By controlling the current, the magnetic field strength of the tension electromagnet can be adjusted, thereby changing the force applied to the yarn when it passes through the tension electromagnet, thereby controlling the tension of the yarn.

[0024] Specifically, when the magnetic field strength of the tension electromagnet increases, a greater pulling force will be applied to the yarn, thereby increasing the tension of the yarn; when the magnetic field strength of the tension electromagnet decreases, a smaller pulling force will be applied to the yarn, thereby reducing the tension of the yarn. By continuously adjusting the current of the tension electromagnet, precise control of the yarn tension can be achieved. However, in this embodiment, the control state of the tension electromagnet 6 corresponding to the yarn is fixed, and the single spindle CPU 4 controls the voltage of the grooved drum motor mainly controlled by the winding machine 3, that is, the effect of controlling the yarn tension is achieved by changing the speed of the grooved drum motor.

[0025] Response data usually refers to the data generated by the yarn tension control system during operation, such as the actual response value of the yarn tension, the control amount of the yarn tension control system, etc. For any yarn tension control system, the response data can be obtained by continuously inputting different control amounts and collecting the corresponding yarn tension response values; the corresponding response data can also be obtained through the signal transfer function of the yarn tension control system itself and mathematical simulation tools such as MATLAB.

[0026] In an optional embodiment, based on the yarn tension control system provided in the above embodiment, the yarn tension control system transfer function , satisfying the following relationship:

[0027] ,

[0028] in, Represents a complex parameter variable, represents the radius of the grooved drum, represents the yarn moment of inertia, represents the motor's moment of inertia, represents the friction coefficient, The proportionality factor representing the winding tension of the grooved drum motor, Represents the electromagnetic torque proportionality coefficient.

[0029] S02. Using the response data, obtain a predicted response value of the yarn tension.

[0030] It should be understood that the unit step response curve of the yarn tension can be obtained by using the response data, and its dynamic characteristics can be obtained through the unit step response curve. Further, the dynamic characteristics include a series of dynamic coefficients, which can be used to describe the dynamic characteristics of the yarn tension, such as inertia, delay, response speed, etc., so as to provide a basis for the design of the yarn tension control system.

[0031] In an optional embodiment, the step of obtaining the predicted response value of the yarn tension by using the response data comprises the following steps:

[0032] S021. Build a response model of the yarn tension control system based on the response data.

[0033] The corresponding dynamic coefficients are obtained from the response data: , the yarn tension control system response model constructed using the dynamic coefficient includes the following formula: ,in, Indicates k The yarn tension response value at time , Indicates the sampling time sequence number, Indicates the sampling time sequence number when the yarn tension control system response reaches a steady state, Indicates The dynamic coefficient of the moment, Indicates The dynamic coefficient of the moment, Indicates that the yarn tension control system is The control increment of the moment, Indicates that the yarn tension control system is Control increments at a moment.

[0034] The parameters in the above yarn tension control system response model can be further understood as follows: Indicates that the yarn tension control system is The control amount input at the moment is compared with the The increment of the control quantity input at any moment, Indicates that the yarn tension control system is The control amount input at the moment is compared with the The increment of the control quantity input at each moment.

[0035] S022. Utilize the yarn tension control system response model to construct a yarn tension prediction response model.

[0036] Based on the above yarn tension control system response model, the yarn tension prediction response model is constructed, including the following formula: ,in, Indicates The predicted response value of yarn tension at time , Indicates the sampling time sequence number, Indicates the sampling time sequence number when the yarn tension control system response reaches a steady state, Indicates The dynamic coefficient of the moment, Indicates The dynamic coefficient of the moment, , Indicates the prediction time sequence number, Indicates that the yarn tension control system is The control increment of the moment, Indicates the yarn tension control system Control increments at a moment.

[0037] It should be understood that the above yarn tension prediction response model , it can be predicted that Time, time, ..., The predicted response value of the yarn tension at the moment The yarn tension in the next n steps after time is predicted.

[0038] The above yarn tension prediction response model can be further transformed: ,in, The first part represents the influence of the control quantity input in the past on the predicted response value of the yarn tension, and also represents the The predicted response of the yarn tension at time instant to the initial value.

[0039] According to the modified formula, Predicted response value of yarn tension at time Satisfy the following formula: ,in, Indicates The predicted response value of yarn tension at time Indicates The dynamic coefficient of the moment, Indicates the yarn tension control system The control increment of the moment, Indicates The predicted response of the yarn tension at time instant to the initial value.

[0040] …

[0041] Similarly, Predicted response value of yarn tension at time Satisfy the following formula: ,in, Indicates The dynamic coefficients at time, …, Indicates the yarn tension control system The control increment at time, ..., Indicates that the yarn tension control system is The control increment of the moment, Indicates The predicted response of the yarn tension at time instant is the initial value.

[0042] Therefore, the prediction interval can be Write the corresponding yarn tension prediction response matrix , the yarn tension prediction response matrix , further, the yarn tension prediction response matrix satisfies the following expression: , , represents the response matrix of the yarn tension control system, , Represents the initial quantity matrix of yarn tension prediction response.

[0043] S023. Obtaining a yarn tension prediction response value through the yarn tension prediction response model.

[0044] It should be understood that the predicted response of yarn tension is a function of the unknown control increments. , , …, Therefore, the control function is constructed by using these yarn tension prediction response values ​​with unknown control quantities, and the optimal control quantity of the yarn tension control system at the kth moment under the historical input is inversely solved through the control function. .

[0045] Steps S021 to S023 can more accurately predict the response value of yarn tension and improve control accuracy by building a response model and constructing a prediction model. At the same time, the yarn tension prediction response model can be continuously updated and optimized in the actual operation of the system, so that the prediction result is more accurate. Therefore, the present invention can better adapt to complex and changeable production environments and process processes, thereby improving the stability and robustness of the control system.

[0046] S03. Setting the target value of yarn tension.

[0047] The yarn tension target value is the desired tension value that the yarn tension control system aims to achieve, which can be achieved by adjusting the output signal of the controller. In processes such as spinning, the yarn tension target value is usually pre-set and adjusted according to production requirements and process characteristics. By setting and controlling the yarn tension target value, it can be ensured that the yarn has a stable tension during the production process, thereby improving the quality of the yarn and production efficiency.

[0048] S04. Setting a control amount by using the yarn tension target value and the yarn tension predicted response value.

[0049] In an optional embodiment, the step S04 of setting the control amount by using the yarn tension target value and the yarn tension predicted response value comprises the following steps:

[0050] S041. Build a control function using the yarn tension target value and the yarn tension predicted response value.

[0051] It should be understood that the control function refers to the function used to calculate the optimal control quantity in the yarn tension control system, which is generally composed of a system model and a control algorithm. The system model describes the dynamic characteristics of the controlled object (such as a spinning machine), including the input-output relationship and the dynamic response of the system; the control algorithm calculates the optimal control quantity based on the system model so that the output of the system can approach or reach the expected value. The design of the control function is crucial to the performance of the yarn tension control system. A good control function can improve the control accuracy and stability of the system.

[0052] In this embodiment, the control function constructed using the yarn tension target value and the yarn tension predicted response value includes the following formula: ,in, represents the yarn tension prediction response matrix, represents the yarn tension expected response matrix, represents the weight coefficient, Represents the control increment matrix.

[0053] Furthermore, the yarn tension expected response matrix ,in, Indicates The expected response value of the yarn tension at time. ,in, , represents the softening coefficient, Indicates the The predicted response initial value of the yarn tension at time Indicates the target value of yarn tension. The larger the value, the closer the expected output sequence value is to the actual output. In this way, when the expected output sequence value is adjusted, the adjustment speed will become slower, otherwise the adjustment speed will become faster.

[0054] S042. Solve the control increment matrix of the yarn tension control system through the control function.

[0055] In this embodiment, is a variable, for the above control function Derivation, to find a solution that satisfies of ,Should That is, the control increment matrix used to solve the specific optimal control quantity.

[0056] Through the above solving operation, the obtained control increment matrix satisfies the following formula: ,in, represents the control increment matrix, represents the response matrix of the yarn tension control system, represents the identity matrix, represents the yarn tension expected response matrix, Represents the initial quantity matrix of yarn tension prediction response.

[0057] It should be understood that the control increment matrix obtained above is a set of control increments obtained through the yarn tension target value and the yarn tension predicted response value. Specifically, .

[0058] In another optional embodiment, in order to reduce the difficulty of calculation, the yarn tension prediction response matrix in the control function is , yarn tension expected response matrix And the control quantity difference matrix , all of them have undergone corresponding dimensionality reduction processing: .

[0059] S043. Set the control amount according to the control increment matrix.

[0060] Obtained from step S042 However, in order to achieve continuous optimization of subsequent control increments, step S043 only selects the control increment at the kth moment through the control increment matrix, that is, ,in, .

[0061] Furthermore, due to Indicates that the yarn tension control system is The control amount input at the moment is compared with the The increment of the control quantity input at the moment, therefore, based on the The control quantity input at any time is The control quantity of the yarn tension control system at the kth moment can be obtained.

[0062] By building a control function and solving the control increment matrix, steps S041 to S043 can more accurately calculate the control increment that needs to be set, avoiding the error and instability caused by manually setting the control amount. In addition, setting the control amount according to the obtained control increment can make the yarn tension control system more intelligent and adaptive, so as to adapt to different production environments and process processes, and improve control accuracy and production efficiency. Therefore, the self-feedback yarn tension control method provided by the present invention has a wide range of applicability and practicality in practical applications.

[0063] S05. Obtaining an actual response value of the yarn tension of the controlled variable.

[0064] The actual response value of the yarn tension of the yarn tension control system is measured under the condition of a given control amount. This is to verify whether the system has achieved the desired control effect, so as to further optimize the control system or make corrections.

[0065] At the same time, it should be understood that Input the control quantity of yarn tension control system at all times After that, the corresponding collection time of the actual response value of the yarn tension is .

[0066] S06. Optimizing the control amount by using the actual response value of the yarn tension.

[0067] In an optional embodiment, based on the first Actual response value of yarn tension at time The step S06 of optimizing the control amount by using the actual response value of the yarn tension comprises the following steps:

[0068] S061. Compare the actual response value of the yarn tension with the predicted response value of the yarn tension to obtain a prediction error model.

[0069] It should be understood that the prediction error model is for the line tension control system. After regulation, Actual response value of yarn tension collected at all times , and the predicted response value of yarn tension The difference between.

[0070] Specifically, the prediction error model includes the following formula: ,in, Indicates that the yarn tension control system is inputting control quantity After The actual response value of the yarn tension at the moment, Indicates that the yarn tension control system is inputting control quantity After The predicted value of yarn tension at time Indicates that the yarn tension control system is in control quantity Under the effect The prediction error at that moment.

[0071] S062. Correcting the yarn tension prediction response value using the prediction error model.

[0072] In this embodiment, the corrected yarn tension prediction response value described in step S062 includes the following steps:

[0073] The prediction error model is used to correct the yarn tension prediction response value. The corrected yarn tension prediction response value includes the following formula: ,in, Indicated in The yarn tension prediction response matrix after time correction, represents the yarn tension prediction response matrix obtained at time k, represents the error correction vector, Indicates that the yarn tension control system is in control quantity Under the effect The prediction error at that moment.

[0074] The yarn tension prediction response value after correction is used to correct the yarn tension prediction response initial quantity matrix. The corrected yarn tension prediction response initial quantity matrix satisfies the following formula: ,in, Indicated in The initial quantity matrix of the predicted response after time correction, S is the displacement matrix, Indicated in Yarn tension prediction response matrix after time correction.

[0075] Through the displacement matrix S, the corrected predicted response value of the yarn tension is shifted by one time sequence as the initial value of the current moment k', thus entering a new round of prediction optimization process. It should be understood that the current moment k' is the starting moment of the new round of prediction optimization process and is also the corresponding moment of the previous prediction optimization process. time.

[0076] S063. Optimize the control amount by using the calibrated yarn tension predicted response value.

[0077] It should be understood that the corrected yarn tension prediction response value can better reflect the response performance of the yarn tension control system after the control amount acts.

[0078] Therefore, based on the control function obtained in the above steps, the corresponding data is updated and the new control quantity is obtained If the new control quantity Take time k as the time reference, that is, Indicates that after optimization operation The actual control quantity output by the yarn tension control system.

[0079] Steps S061 to S063 can obtain the prediction error model of the yarn tension in real time by comparing the actual response value and the predicted response value of the yarn tension, and use the model to correct the predicted response value, thereby further optimizing the control amount. Therefore, the present invention can achieve more accurate and real-time control, improve production efficiency and quality, and continuously update and optimize the prediction model in actual operation, thereby adapting to the ever-changing production environment and process, and improving the stability and robustness of the control system.

[0080] In the self-feedback yarn tension control method provided by the present invention, steps S01 to S06 obtain the response data of the yarn tension control system to obtain the predicted response value of the yarn tension, and set the control amount in combination with the set yarn tension target value, thereby optimizing the yarn tension control, solving the problem that the existing winder tension control strategy lacks predictability. The self-feedback yarn tension control method provided by the present invention has the advantages of fast response speed, low requirements on model accuracy, strong robustness, etc., and can achieve precise control in complex industrial processes, effectively improve production efficiency and product quality, and has a wide range of application prospects.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A self-feedback yarn tension control method, characterized in that: The steps include: Acquiring response data of the yarn tension control system; Using the response data, obtaining a predicted response value of yarn tension; The method of obtaining the predicted response value of the yarn tension by using the response data comprises the following steps: Building a response model of the yarn tension control system through the response data; Using the yarn tension control system response model, a yarn tension prediction response model is constructed; The yarn tension prediction response model includes the following formula: in, represents the predicted response value of the yarn tension at the k+jth moment, i=1,2,3,…,p-1,p, i represents the sampling time sequence number, p represents the sampling time sequence number when the yarn tension control system response reaches a steady state, a i represents the dynamic coefficient at the i-th moment, a p represents the dynamic coefficient at the pth moment, j=1,2,3,…,n, j represents the sequence number of the prediction moment, Δu(k+ji) represents the control increment of the yarn tension control system at the k+jith moment, Δu(k+jp) represents the control increment of the yarn tension control system at the k+jpth moment; Obtaining a yarn tension prediction response value through the yarn tension prediction response model; Set the target value of yarn tension; Using the yarn tension target value and the yarn tension predicted response value, setting a control amount; Obtaining an actual response value of the yarn tension of the control variable; The control amount is optimized using the actual response value of the yarn tension.

2. The self-feedback yarn tension control method according to claim 1, characterized in that: The yarn tension control system response model includes the following formula: Where y(k) represents the yarn tension response value at the kth moment, i = 1, 2, 3, ..., p-1, p, i represents the sampling time sequence number, p represents the sampling time sequence number when the yarn tension control system response reaches a steady state, a i represents the dynamic coefficient at the i-th moment, a p represents the dynamic coefficient at the pth moment, Δu(ki) represents the control increment of the yarn tension control system at the kith moment, and Δu(kp) represents the control increment of the yarn tension control system at the kpth moment.

3. The self-feedback yarn tension control method according to claim 1, characterized in that: The method of setting the control amount by using the yarn tension target value and the yarn tension predicted response value comprises the following steps: Using the yarn tension target value and the yarn tension predicted response value, a control function is constructed; By means of the control function, solving the control increment matrix of the yarn tension control system; The control amount is set according to the control increment matrix.

4. The self-feedback yarn tension control method according to claim 3, characterized in that: The control function includes the following formula: in, represents the yarn tension prediction response matrix, W represents the yarn tension expected response matrix, λ represents the weight coefficient, ΔU represents the control increment matrix, and T represents the matrix transpose.

5. The self-feedback yarn tension control method according to claim 4, characterized in that: The control increment matrix includes the following formula: ΔU=(A T A+λI) -1 A T (W-Y0), Among them, ΔU represents the control increment matrix, A represents the response matrix of the yarn tension control system, λ represents the weight coefficient, I represents the unit matrix, W represents the yarn tension expected response matrix, Y0 represents the yarn tension predicted response initial quantity matrix, and T represents the matrix transpose.

6. The self-feedback yarn tension control method according to claim 2, characterized in that: The method of optimizing the control amount by utilizing the actual response value of the yarn tension comprises the following steps: Comparing the actual response value of the yarn tension with the predicted response value of the yarn tension to obtain a prediction error model; Correcting the yarn tension prediction response value using the prediction error model; The control amount is optimized using the corrected yarn tension predicted response value.

7. The self-feedback yarn tension control method according to claim 6, characterized in that: The prediction error model includes the following formula: Wherein, y1(k+1) represents the actual response value of the yarn tension of the yarn tension control system at the k+1th moment after the control value u(k) is input, represents the predicted value of the yarn tension of the yarn tension control system at the k+1th moment after the control amount u(k) is input, and e(k+1) represents the prediction error generated by the yarn tension control system at the k+1th moment under the action of the control amount u(k).

8. The self-feedback yarn tension control method according to claim 6, characterized in that: The method of correcting the yarn tension prediction response value by using the prediction error model comprises the following steps: The prediction error model is used to correct the yarn tension prediction response value. The corrected yarn tension prediction response value includes the following formula: Among them, Y c represents the corrected yarn tension prediction response matrix at time k+1, represents the yarn tension prediction response matrix obtained at time k, h represents the error correction vector, and e(k+1) represents the prediction error generated by the yarn tension control system at the k+1th time under the action of the control quantity u(k); The yarn tension prediction response value after correction is used to correct the yarn tension prediction response initial quantity matrix. The corrected yarn tension prediction response initial quantity matrix satisfies the following formula: in, represents the initial matrix of the predicted response after correction at time k+1, S is the displacement matrix, Y c represents the corrected yarn tension prediction response matrix at time k+1.

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