Spindle rotating speed control method and system of spinning frame

Abstract

The invention relates to the technical field of spinning frame rotating speed control, in particular to a spinning frame spindle rotating speed control method and system. Determining a tension abnormal saliency value of each cycle period; obtaining a deviation significant value of the rotating speed of the spindle in each cycle period; the rotating speed state difference value of the spindle in each cycle period is obtained; analyzing the correlation degree of the tension abnormal saliency value and the rotating speed state difference value of all the cycle periods during the running period of the spinning frame, and determining the abnormal influence degree of the rotating speed of the spindle during the running period of the spinning frame; optimal control parameters of the PID controller are obtained through an optimization algorithm, the abnormal influence degree serves as the weight, in combination with performance indexes of the PID controller, a fitness function of the optimization algorithm is constructed, and the spindle rotating speed of the spinning frame in the next operation period is controlled. Therefore, the spindle rotating speed control stability of the spinning frame is improved.

Description

Technical Field

[0001] This application relates to the field of spinning frame speed control technology, specifically to a spinning frame spindle speed control method and system. Background Technology

[0002] As the final and crucial step in yarn production, yarn winding significantly impacts the profitability of textile mills in terms of both output and quality. During the winding process, the spindle's brushless DC motor speed typically fluctuates considerably, leading to issues such as significant differences in yarn twist, high twist unevenness, and easy yarn breakage. Therefore, controlling the spindle speed is extremely important for yarn quality.

[0003] The spindle speed should remain relatively stable during operation. However, in actual processing, factors such as abnormal fluctuations in yarn tension and changes in motor parameters can easily cause significant fluctuations in spindle speed, thereby reducing the quality of yarn production. Conventional methods typically use classic PID controllers to adjust the spindle speed, but these methods fail to adequately consider the interference from abnormal tension fluctuations and spindle speed fluctuations. This results in insufficient adaptability and low accuracy in the setting of PID controller control parameters, making it difficult to precisely control the spindle within the target speed range. Consequently, the spindle speed control accuracy is insufficient, reducing the quality of yarn production. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for controlling the spindle speed of a spinning frame, the specific technical solution of which is as follows: In a first aspect, embodiments of this application provide a method for controlling the spindle speed of a spinning frame, the method comprising the following steps: Collect the spindle speed and yarn tension of the spinning machine at various times; The trend of all yarn tensions in each cycle and the symmetry of yarn tension distribution are analyzed. Combined with the correlation of yarn tension in each cycle and its adjacent cycles, the significant values ​​of tension anomalies in each cycle are determined. The significant deviation of the spindle speed in each cycle is determined by the degree of fluctuation of the spindle speed in each cycle and the degree of deviation of the spindle speed from the target speed. Based on the consistency of the significant deviation values ​​of the rotational speeds of all spindles in each cycle, the difference value of the spindle rotational speed state in each cycle is obtained; the correlation between the significant value of the tension anomaly and the difference value of the rotational speed state in all cycles during the operation of the spinning machine is analyzed to determine the degree of abnormal influence of the spindle rotational speed during the operation of the spinning machine. The optimal control parameters of the PID controller are obtained using an optimization algorithm, wherein the fitness function of the optimization algorithm is constructed by using the degree of abnormal influence as the weight and combining it with the performance index of the PID controller; the PID controller uses the optimal control parameters to control the spindle speed during the next run of the spinning machine.

[0005] In one embodiment, determining the significant value of the tension anomaly in each cycle includes: Curve fitting is performed on the yarn tension at all times in each cycle, and the mean of the absolute values ​​of the differences between the fitted values ​​and the actual values ​​of the yarn tension at all times in each cycle is calculated. Calculate the skewness of the fitted curve of yarn tension for each cycle; the significant value of the tension anomaly is positively correlated with the mean and the absolute value of the skewness, and negatively correlated with the correlation.

[0006] In one embodiment, further determination of the significant value of the tension anomaly includes: The correlation is non-negatively mapped, and the product of the mean and the absolute value of the skewness is calculated. The significance value of the tension anomaly is the ratio of the product to the result of the non-negative mapping.

[0007] In one embodiment, determining the significant deviation of the spindle rotation speed within each cycle includes: Obtain the fitting curve of the spindle rotation speed at all times in each cycle, obtain all extreme points on the fitting curve, calculate the ratio between the difference in amplitude of adjacent extreme points and the corresponding time interval, and record it as the first ratio. Use the average of the first ratios of all adjacent extreme points on the fitting curve as the rotation speed fluctuation factor for each cycle. By comparing the extreme points on the fitted curve with the target speed, the speed deviation coefficient for each cycle is determined. By combining the speed fluctuation factor and the speed deviation coefficient, the significant deviation value of the spindle speed in each cycle is obtained.

[0008] In one embodiment, determining the rotational speed deviation coefficient for each cycle includes: Calculate the sum of the absolute values ​​of the differences between all extreme points on the fitted curve and the target speed, whereby the speed deviation coefficient is the ratio of the sum to the target speed.

[0009] In one embodiment, the speed state difference value is the degree of dispersion of the significant deviation values ​​of the speed of all spindles of the spinning machine in each cycle.

[0010] In one embodiment, the abnormality impact is the normalized value of the correlation coefficient between the significant value of the tension abnormality and the difference value of the rotational speed state for all cycles during the operation of the spinning frame.

[0011] In one embodiment, the fitness function is expressed as: In the formula, J is the fitness function. The impact of abnormal spindle speed during the operation of the spinning frame. This is to address the overshoot during the PID controller's control of the spindle speed. The rise time is used to control the spindle speed using a PID controller. This refers to the steady-state error during the process of controlling the spindle speed using a PID controller.

[0012] In one embodiment, the objective of the optimization algorithm is to minimize the fitness function.

[0013] Secondly, embodiments of this application also provide a spindle speed control system for a spinning frame, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0014] This application has at least the following beneficial effects: This application, through comprehensive analysis of the trend and symmetry of yarn tension in each cycle, determines the significant value of tension anomalies in each cycle. This allows for more detailed capture of subtle changes in tension fluctuations, timely detection of yarn tension unevenness or sudden tension changes, and measurement of the degree of yarn tension anomaly, thus improving the accuracy of yarn tension anomaly identification. Furthermore, by determining the significant deviation value of spindle speed within each cycle, it reflects the degree of spindle speed anomaly in each cycle, enhancing sensitivity to abnormal spindle speed changes and contributing to the accuracy of subsequent spindle speed control. By comprehensively considering the interaction between the significant value of tension anomalies and the difference in speed state, it achieves… PID controllers can adjust speed more precisely, overcoming the limitations of traditional methods that cannot effectively handle the impact of abnormal tension on yarn quality. This comprehensive optimization not only improves the accuracy of speed control but also avoids quality fluctuations caused by ignoring abnormal tension. By constructing a fitness function and using optimization algorithms, the optimal control parameters of the PID controller are obtained, avoiding the problem of unstable spindle speed caused by improper PID controller parameter settings. This improves the accuracy of PID controller parameter settings and enables better adaptation to the complex operating environment of the spinning machine, thereby enhancing the accuracy and stability of the spinning machine speed control. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of a method for controlling the spindle speed of a spinning frame, as provided in one embodiment of this application; Figure 2 Flowchart for determining the impact of abnormal spindle rotation speed. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a spinning machine spindle speed control method and system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the spindle speed control method and system for a spinning machine provided in this application.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a method for controlling the spindle speed of a spinning machine according to an embodiment of this application. The method includes the following steps: S1 collects the spindle speed and yarn tension of the spinning machine at various times.

[0021] In the textile industry, the spindle speed directly affects the quality of yarn production. To monitor the spindle speed data in real time, this embodiment uses a laser speed sensor to collect the spindle speed data at various moments. Furthermore, the spindle speed is easily affected by abnormal changes in yarn tension. To monitor the yarn tension status in real time, this embodiment uses a tension sensor to collect yarn tension data at various moments. This embodiment sets the speed measurement frequency to 100Hz and the yarn tension measurement interval to 1 second. Users can adjust the speed and yarn tension measurement frequencies according to their specific needs.

[0022] Therefore, this embodiment obtained spindle speed data and yarn tension data during the operation of the spinning machine.

[0023] S2. Analyze the trend of all yarn tensions in each cycle and the symmetry of yarn tension distribution. Combine the correlation between the yarn tension of each cycle and its adjacent cycles to determine the significant value of tension anomalies in each cycle.

[0024] During the operation of a ring spinning frame, the spindle speed is easily affected by various factors, such as wear on the spindle surface and bearings, abnormal changes in yarn tension, and sudden disturbances to the motor. Without replacing the hardware, control algorithms are generally used to keep the spindle within a target speed range to reduce the interference from these factors. This embodiment analyzes the characteristics of the influence on spindle speed and optimizes spindle speed control to improve the stability of the ring spinning frame operation.

[0025] First, the yarn winding process is accomplished by the coordinated rotation of the spindle and the reciprocating motion of the ring rail. When the ring rail moves upward, it forms the winding layer, where the yarn loops are tightly packed and the winding speed is high. When the ring rail moves downward, it forms the binding layer, where the yarn loops are sparsely packed and the winding speed is slow. In this embodiment, the time required for one upward and downward movement of the ring rail is considered a cycle. In actual processing, the yarn tension will exhibit periodic fluctuations within a small range due to the reciprocating movement of the ring rail. Furthermore, the yarn tension increases when the ring rail rises and decreases when it descends. Within a single cycle, the tension curve exhibits a Gaussian distribution trend.

[0026] However, yarn tension may exhibit abnormal changes due to yarn quality defects or ring rail wear. Specifically, this manifests as a decrease in the trend and overall symmetry of yarn tension data changes within a single cycle, while significant differences exist in the tension curves between adjacent cycles. Therefore, this embodiment takes the i-th cycle as an example, using a least-squares fitting algorithm to perform nonlinear fitting on all yarn tension data within the i-th cycle to obtain a tension fitting curve. Then, the absolute value of the difference between the yarn tension data at each moment and its fitted value is calculated, and the mean of all such absolute differences within the i-th cycle is denoted as... The result The larger the value, the weaker the trend of yarn tension data changes within the i-th cycle, and the less it conforms to its normal distribution characteristics. The least squares method is a well-known existing technique, and its specific process will not be elaborated upon.

[0027] Then, calculate the absolute value of the skewness of the tension fitting curve in the i-th cycle, denoted as . The result The larger the value, the worse the symmetry of the yarn tension curve. Further, the Spearman correlation coefficient is calculated between the tension fitting curve of the i-th cycle and the corresponding tension fitting curve of the previous cycle, used to measure the correlation between the i-th cycle and the corresponding tension fitting curve of the previous cycle. It should be noted that the Spearman correlation coefficient is only one embodiment of this application; implementers can choose other feasible correlation calculation methods according to actual conditions, and this embodiment does not limit this.

[0028] The Spearman correlation coefficient is nonnegatively mapped, and the product of the mean of the absolute values ​​of all the differences in the i-th cycle and the absolute value of the skewness of the tension fitting curve in the i-th cycle is calculated. The ratio of this product to the result of the nonnegative mapping is taken as the significance value of the tension anomaly in the i-th cycle. The purpose of the nonnegative mapping is to avoid the inability to calculate the significant value of tension anomalies due to a denominator of 0. In this embodiment, the nonnegative mapping method is as follows: the Spearman correlation coefficient is normalized using the sigmoid function, and the normalized result is used as the result of the nonnegative mapping. Implementers can choose other existing feasible nonnegative mapping methods.

[0029] Significant value of tension anomaly It reflects the abnormal changes in yarn tension during the i-th cycle and the significance of the differences in tension changes within a local range. The larger the significance value of the tension abnormality, the more obvious the abnormal condition of the yarn tension.

[0030] S3. By measuring the fluctuation of the spindle speed in each cycle and the deviation of the spindle speed from the target speed, the significant deviation value of the spindle speed in each cycle is determined.

[0031] Furthermore, under the influence of abnormal tension changes in the spinning machine or sudden disturbances to the motor, the spindle speed becomes difficult to maintain stability, resulting in fluctuations around the target speed value. The greater the influence, the more pronounced the abnormal fluctuations in spindle speed. This leads to a large oscillation rate characteristic within a short period, with a more significant deviation from the target speed value. Since the instantaneous change in speed is significant, this embodiment takes the i-th cycle as an example. To obtain the continuously fluctuating oscillation frequency characteristics, this embodiment uses a quadratic polynomial fitting algorithm to obtain the fitting curve of all speed data within the i-th cycle and acquires all extreme points in the fitting curve. Then, the ratio of the difference in amplitude between any two adjacent extreme points to the corresponding time interval is calculated, denoted as the first ratio. The average of all first ratios within the i-th cycle is used as the speed fluctuation factor for the i-th cycle, reflecting the rate of change of spindle speed oscillation within the i-th cycle, denoted as... .

[0032] Furthermore, to obtain the degree of deviation from the target speed value during the speed oscillation process, all extreme points on the fitted curves of all speed data in the i-th cycle are arranged in ascending order of time to obtain the corresponding extreme value sequence, and the speed deviation coefficient in the i-th cycle is calculated. Its formula is Where N represents the total number of data points in the extreme value sequence corresponding to the i-th cycle. This represents the k-th value in the extreme value sequence. This represents the target rotational speed value within the i-th cycle. The obtained... The larger the value, the more significant the deviation between the spindle speed and the target speed within that cycle.

[0033] Then, by combining the speed fluctuation factor and the speed deviation coefficient, the significant deviation value of the spindle speed in the i-th cycle is obtained. , representing the rate of change and deviation characteristics of the spindle rotation speed within the i-th cycle, is expressed by the formula: The result This reflects the rate of change and deviation characteristics of the spindle rotation speed within the cycle, including the significant deviation value. The larger the value, the worse the spindle rotation speed stability in the i-th cycle, and the further it deviates from its target rotation speed.

[0034] S4. Based on the consistency of the significant deviation values ​​of the rotational speeds of all spindles in each cycle, obtain the difference value of the spindle rotational speed state in each cycle; analyze the correlation between the significant value of the abnormal tension and the difference value of the rotational speed state in all cycles during the operation of the spinning machine, and determine the degree of abnormal influence of the spindle rotational speed during the operation of the spinning machine.

[0035] In modern ring spinning machine production, each spindle has a corresponding drive unit for individual control, enabling the machine to be highly flexible and better adaptable to different process requirements. However, during the spinning process, the rotational speed of different spindles should be consistent. The worse the consistency of the rotational speed between spindles, the greater the impact on the quality of the spinning production. Although the influence of rotational speed on the actual speed is relatively small during operation, the measured data will still have a certain degree of oscillation, which may lead to inaccurate assessment of the differences in the rotational speed of different spindles using the measured rotational speed data. Therefore, this embodiment uses the significant deviation value, which has the characteristics of oscillation change rate and deviation, for analysis. The more inconsistent the rotational speed of the spindles, the greater the difference in the corresponding significant deviation value between the spindles. Thus, the dispersion of the significant deviation values ​​corresponding to all spindles in the i-th cycle is obtained as the difference value of the spindle's rotational speed in the i-th cycle. The larger the difference value of the rotational speed, the more inconsistent the rotational speed of the spindles in that cycle.

[0036] The degree of dispersion can be calculated using methods such as variance, standard deviation, and coefficient of variation. In this embodiment, standard deviation is used as the method for calculating the degree of dispersion.

[0037] Furthermore, spinning often involves a certain duration, during which the ring rail repeatedly rises and falls. Under optimal conditions, the spindle speed needs to remain stable over a long period, and there is a certain interaction between the yarn tension and the spindle speed. Therefore, the more similar the abnormal characteristics of the yarn tension and the abnormal characteristics of the spindle speed, the more pronounced the instability of the spindle speed due to various factors. In this context, the correlation between the significant values ​​of the abnormal tension and the differences in spindle speed during all cycles of the spinning machine's operation is analyzed. In this embodiment, the spinning machine's operation period refers to the entire time from start-up to shutdown. This embodiment uses the normalized Hoeffding's D correlation coefficient between the significant values ​​of the abnormal tension and the differences in spindle speed during all cycles of the spinning machine's operation as the degree of abnormal influence of the spindle speed during operation. This reflects the similarity between the abnormal characteristics of the yarn tension and the abnormal characteristics of the spindle speed. A higher degree of abnormal influence indicates a greater impact on spindle speed control, which can easily lead to unstable yarn production quality. Implementers may choose other feasible correlation coefficient calculation methods, such as the Pearson correlation coefficient, and this embodiment does not impose any restrictions on this. The normalized value of the Hoeffding's D correlation coefficient is obtained using the sigmoid function; implementers may choose other feasible normalization methods. The flowchart for determining the abnormal influence of spindle rotation speed is shown below. Figure 2 As shown.

[0038] S5. The optimal control parameters of the PID controller are obtained using an optimization algorithm. The fitness function of the optimization algorithm is constructed by using the abnormal influence degree as the weight and combining it with the performance index of the PID controller. The PID controller uses the optimal control parameters to control the spindle speed during the next run of the spinning machine.

[0039] This embodiment analyzes in depth the abnormal changes in yarn tension and the rate and deviation of spindle speed oscillations during the operation of the spinning frame. It further considers the impact of the interaction between yarn tension and spindle speed on spindle speed control. Based on this, spindle speed control optimization is performed.

[0040] Specifically, this embodiment uses a PID controller to control the spindle speed. After one run of the spinning frame, the abnormal influence of the spindle speed during this run is calculated. Combined with the performance indicators that measure the control effect of the PID controller, a fitness function is constructed. A genetic algorithm is then used to find the optimal control parameters of the PID controller, namely the proportional coefficient. Integral coefficient and differential coefficients .

[0041] First, in the spindle speed control experiment of the spinning frame, a genetic algorithm was used to optimize the control parameters of the PID controller. The control parameters were adjusted in real time based on the steady-state error feedback from the PID controller to ensure that the spindle speed always converged to the target accuracy during the control process. The value range of the PID control parameters was set, where... , , The population of the genetic algorithm is initialized based on the interval.

[0042] Secondly, construct the fitness function, with the following expression: In the formula, J is the fitness function. The impact of abnormal spindle speed during the operation of the spinning frame. This is to address the overshoot during the PID controller's control of the spindle speed. The rise time is used to control the spindle speed using a PID controller. This refers to the steady-state error during the spindle speed control process using a PID controller. The fitness function evaluates the performance of the PID controller. The optimization process of the genetic algorithm involves minimizing the fitness function, where the maximum number of iterations is 100.

[0043] In the fitness function, if the abnormal influence of the spindle speed during the current operation of the spinning frame is greater, it means that the spindle speed is more affected, which is more likely to lead to unstable production quality of the yarn. A larger rise time weight needs to be set to improve the response speed of the control. Conversely, if the abnormal influence is smaller, it means that the current spindle speed is less affected. The rise time weight can be reduced and the overshoot weight increased, which will have a better effect on suppressing overshoot and improve control accuracy.

[0044] Finally, the genetic algorithm obtains the globally optimal control parameters after the optimization process. , and The PID controller utilizes optimal control parameters to initiate spindle speed control during the next run of the spinning frame, achieving optimized spindle speed control. This helps compensate for insufficient spindle speed control precision and improves the quality of spinning production. Genetic algorithms and PID controllers are existing known technologies; implementers can choose other feasible optimization algorithms, and this embodiment does not impose any restrictions on this.

[0045] Based on the same inventive concept as the above method, this application embodiment also provides a spinning frame spindle speed control system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described spinning frame spindle speed control methods.

[0046] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0047] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0048] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for controlling the spindle speed of a spinning frame, characterized in that, The method includes the following steps: Collect the spindle speed and yarn tension of the spinning machine at various times; The trend of all yarn tensions in each cycle and the symmetry of yarn tension distribution are analyzed. Combined with the correlation of yarn tension in each cycle and its adjacent cycles, the significant values ​​of tension anomalies in each cycle are determined. The significant deviation of the spindle speed in each cycle is determined by the degree of fluctuation of the spindle speed in each cycle and the degree of deviation of the spindle speed from the target speed. Based on the consistency of the significant deviation values ​​of the rotational speeds of all spindles in each cycle, the difference value of the spindle rotational speed state in each cycle is obtained; the correlation between the significant value of the tension anomaly and the difference value of the rotational speed state in all cycles during the operation of the spinning machine is analyzed to determine the degree of abnormal influence of the spindle rotational speed during the operation of the spinning machine. The optimal control parameters of the PID controller are obtained using an optimization algorithm, wherein the fitness function of the optimization algorithm is constructed by using the degree of abnormal influence as the weight and combining it with the performance index of the PID controller; the PID controller uses the optimal control parameters to control the spindle speed during the next run of the spinning machine.

2. The method for controlling the spindle speed of a spinning frame as described in claim 1, characterized in that, The determination of the significant value of tension anomalies in each cycle includes: Curve fitting is performed on the yarn tension at all times in each cycle, and the mean of the absolute values ​​of the differences between the fitted values ​​and the actual values ​​of the yarn tension at all times in each cycle is calculated. Calculate the skewness of the fitted curve of yarn tension for each cycle; the significant value of the tension anomaly is positively correlated with the mean and the absolute value of the skewness, and negatively correlated with the correlation.

3. The method for controlling the spindle speed of a spinning frame as described in claim 2, characterized in that, Further determination of the significant value of the tension anomaly includes: The correlation is non-negatively mapped, and the product of the mean and the absolute value of the skewness is calculated. The significance value of the tension anomaly is the ratio of the product to the result of the non-negative mapping.

4. The method for controlling the spindle speed of a spinning frame as described in claim 1, characterized in that, The determination of the significant deviation value of the spindle rotation speed within each cycle includes: Obtain the fitting curve of the spindle rotation speed at all times in each cycle, obtain all extreme points on the fitting curve, calculate the ratio between the difference in amplitude of adjacent extreme points and the corresponding time interval, and record it as the first ratio. Use the average of the first ratios of all adjacent extreme points on the fitting curve as the rotation speed fluctuation factor for each cycle. By comparing the extreme points on the fitted curve with the target speed, the speed deviation coefficient for each cycle is determined. By combining the speed fluctuation factor and the speed deviation coefficient, the significant deviation value of the spindle speed in each cycle is obtained.

5. The method for controlling the spindle speed of a spinning frame as described in claim 4, characterized in that, The determination of the speed deviation coefficient for each cycle includes: Calculate the sum of the absolute values ​​of the differences between all extreme points on the fitted curve and the target speed, whereby the speed deviation coefficient is the ratio of the sum to the target speed.

6. The method for controlling the spindle speed of a spinning frame as described in claim 1, characterized in that, The speed state difference value is the degree of dispersion of the significant deviation values ​​of the speed of all spindles of the spinning machine in each cycle.

7. The method for controlling the spindle speed of a spinning frame as described in claim 1, characterized in that, The abnormality impact is the normalized value of the correlation coefficient between the significant value of the abnormal tension and the difference in the rotational speed during all cycles of the spinning machine operation.

8. The method for controlling the spindle speed of a spinning frame as described in claim 1, characterized in that, The expression for the fitness function is: In the formula, J is the fitness function. The impact of abnormal spindle speed during the operation of the spinning frame. This is to address the overshoot during the PID controller's control of the spindle speed. The rise time is used to control the spindle speed using a PID controller. This refers to the steady-state error during the process of controlling the spindle speed using a PID controller.

9. The method for controlling the spindle speed of a spinning frame as described in claim 8, characterized in that, The objective of the optimization algorithm is to minimize the fitness function.

10. A spindle speed control system for a spinning frame, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-9.

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