Sanding machine control method and system based on tension self-adaptive adjustment

Abstract

The invention provides a sueding machine control method and system based on tension self-adaptive adjustment, and relates to the technical field of sueding machine control, and the method comprises the steps: obtaining an actual tension value of a fabric in real time, comparing the actual tension value with a target tension value, obtaining a real-time tension deviation and a tension deviation change rate, and according to the real-time tension deviation and the tension deviation change rate, obtaining a real-time tension value of the fabric; generating a first control signal, collecting real-time rolling diameter signals of an unwinding roller and a winding roller of the sueding machine, generating a second control signal through a feedforward calculation model according to the rolling diameter signals and the current fabric linear speed, synthesizing the first control signal and the second control signal to obtain a total control signal, outputting the total control signal to a driving device, and adjusting the rotating speed of a motor. The technical problems that in the prior art, control over the sueding machine is limited in response speed, a control strategy is difficult to adjust in time, and normal operation and fabric quality of the sueding machine are affected are solved. The technical effects that the tension of the sueding machine is adjusted in a self-adaptive mode, and the operation stability of the sueding machine and the fabric quality are improved are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sanding machine control, and particularly relates to a sanding machine control method and system based on tension adaptive adjustment. BACKGROUND

[0002] A sanding machine produces fine and dense nap on the surface of fabric through friction, which can improve the softness, warmth and comfort of the fabric, and is widely used in many industries such as clothing and home textiles. During the operation of the sanding machine, accurate control of the tension of the fabric is the core link to ensure the processing quality, because the stability of the tension directly affects the uniformity, density of the fabric nap and the overall flatness of the fabric. If the tension control is improper, it is easy to cause quality problems such as fabric wrinkles, broken edges, uneven nap length, etc., and thus reduce the product quality. At present, the tension control technology of the sanding machine is mainly based on the traditional PID control algorithm, which generates a corresponding control signal to adjust the motor speed by proportional, integral and differential operation on the deviation between the set tension value and the actual tension value, and thus realizes the control of the fabric tension. However, the control parameters of the traditional PID control algorithm are usually fixed and have limited response speed, which makes it difficult to adjust the control strategy in time and easily causes tension out of control, resulting in fabric damage and production interruption, and it is difficult to meet the control requirements of high precision, high stability and high adaptability of the sanding machine in the modern textile industry.

[0003] The existing sanding machine control has the technical problems of limited response speed, difficulty in adjusting the control strategy in time, leading to tension out of control, affecting the normal operation of the sanding machine and the quality of the fabric product. SUMMARY

[0004] The purpose of the present application is to provide a sanding machine control method and system based on tension adaptive adjustment, which solves the technical problems of limited response speed, difficulty in adjusting the control strategy in time, leading to tension out of control, affecting the normal operation of the sanding machine and the quality of the fabric product in the prior art.

[0005] In view of the above problems, the present application provides a sanding machine control method and system based on tension adaptive adjustment.

[0006] In a first aspect of the present application, a control method for a fuzzing machine based on tension self-adaptive adjustment is provided, which comprises: acquiring actual tension values of the fabric in real time through tension detection devices arranged at the fabric input end and the fabric output end of the fuzzing machine, comparing the actual tension values with preset target tension values to obtain real-time tension deviations and tension deviation change rates; using a model-free adaptive control algorithm to generate a first control signal according to the real-time tension deviations and the tension deviation change rates; collecting real-time roll diameter signals of the unwinding roller and the winding roller of the fuzzing machine, and generating a second control signal for compensating tension disturbances caused by roll diameter changes through a feedforward calculation model according to the roll diameter signals and the current fabric linear speed; synthesizing the first control signal and the second control signal to obtain a total control signal, and outputting the total control signal to a driving device of the fabric input motor and / or the fabric output motor of the fuzzing machine to adjust the motor speed.

[0007] Further, a model-free adaptive control algorithm is used to estimate the relationship between the control signal and the tension, and a pseudo-Jacobian matrix is established; based on the pseudo-Jacobian matrix, the first control signal is generated according to the tension deviation and the tension deviation change rate.

[0008] Further, the expression of the first control signal is as follows: ; wherein, and is a control gain for adjusting the intensity of the control response; is a pseudo-Jacobian matrix, , is the change amount of the control signal, is the change amount of the tension deviation; is the real-time tension deviation at the current time; is the real-time tension deviation change rate at the current time.

[0009] Further, the speed signal fed back by the motor encoder and the torque current signal fed back by the driver are collected; a disturbance observer is used to estimate the total disturbance according to the speed signal and the torque current signal, and generate a third control signal for offsetting the total disturbance; the total control signal is generated according to the first control signal, the second control signal and the third control signal.

[0010] Further, a disturbance-free inverse model is constructed based on the driving principle of the driver and the motor; the torque current signal is input into the disturbance-free inverse model for forward estimation to obtain an ideal motor speed, and the ideal motor speed is compared with the speed signal to obtain an actual speed deviation; the actual speed deviation is input into the disturbance-free inverse model for reverse calculation of the disturbance to obtain a total disturbance estimated torque; the third control signal is generated based on the total disturbance estimated torque.

[0011] Further, the change state of the tension deviation is monitored in real time, when the change amount of the tension deviation in a unit time exceeds a first preset threshold value, it is determined that a sudden disturbance event occurs, and a soft control mode is triggered immediately; in the soft control mode, a preset soft control parameter set is temporarily switched to; and after the sudden disturbance event ends, the normal control mode is restored.

[0012] Further, the target tension value is used as a retrieval factor to retrieve the roll diameter test data and the corresponding fabric line speed test data of the unwinding roller and the winding roller, and a synchronization ratio relationship is constructed; the feedforward calculation model is trained based on the synchronization ratio deviation data and the corresponding correction signal collected based on the synchronization ratio relationship; the real-time roll diameter signal is input into the feedforward calculation model to generate the second control signal.

[0013] Further, an expert rule base is constructed, the expert rule base stores optimized control parameter sets corresponding to different fabric materials, and each control parameter in the optimized control parameter set has a preset tension value label; the input current fabric material information is received, the optimized control parameter matching the current fabric material is called from the expert rule base, the initialization control of the sanding machine is performed, and the corresponding preset tension value label is extracted to generate the preset target tension value.

[0014] Further, the optimized control parameter is used as the initial parameter of the model-free adaptive control algorithm to initialize the pseudo-Jacobian matrix.

[0015] In a second aspect of the present application, a sanding machine control system based on tension adaptive adjustment is provided, the system comprising: a data acquisition module for acquiring the actual tension value of the fabric in real time through the tension detection device arranged at the fabric inlet end and the fabric outlet end of the sanding machine, comparing the actual tension value with the preset target tension value to obtain the real-time tension deviation and the tension deviation change rate; a first control signal generation module for generating a first control signal according to the real-time tension deviation and the tension deviation change rate by using a model-free adaptive control algorithm; a second control signal generation module for acquiring the real-time roll diameter signal of the unwinding roller and the winding roller of the sanding machine, and generating a second control signal for compensating the tension disturbance caused by the roll diameter change through a feedforward calculation model according to the roll diameter signal and the current fabric line speed; a motor control module for obtaining a total control signal by synthesizing the first control signal and the second control signal, and outputting the total control signal to the driving device of the fabric inlet motor and the fabric outlet motor of the sanding machine to adjust the motor speed.

[0016] One or more technical solutions provided in the present application have at least the following technical effects or advantages: The embodiment of the application provides a control method of a fuzzing machine based on tension self-adaptive adjustment, the actual tension value of a fabric is acquired in real time through a tension detection device arranged at the fabric feeding end and the fabric discharging end of the fuzzing machine, the actual tension value is compared with a preset target tension value, a real-time tension deviation and a tension deviation change rate are obtained, a model-free adaptive control algorithm is used, the first control signal is generated according to the real-time tension deviation and the tension deviation change rate, the real-time roll diameter signals of the unwinding roller and the winding roller of the fuzzing machine are collected, and the second control signal for compensating the tension disturbance caused by the roll diameter change is generated through a feedforward calculation model according to the roll diameter signals and the current fabric linear speed, the first control signal and the second control signal are integrated to obtain a total control signal, and the total control signal is output to the driving device of the fabric feeding motor and / or the fabric discharging motor of the fuzzing machine, so that the motor rotating speed is adjusted. The technical effect that the tension of the fuzzing machine is self-adaptively adjusted and the running stability of the fuzzing machine and the fabric quality are improved is achieved.

[0017] The above description is only a summary of the technical scheme of the application, in order to more clearly understand the technical means of the application, the specific embodiments of the application can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the application more obvious and easy to understand, the following specific embodiments of the application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the application, nor is it used to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical scheme in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by the provided drawings without creative labor for those skilled in the art.

[0019] Figure 1 A flowchart of a control method of a fuzzing machine based on tension self-adaptive adjustment provided by the application.

[0020] Figure 2 A structure diagram of a control system of a fuzzing machine based on tension self-adaptive adjustment provided by the application.

[0021] Explanation of reference signs: data acquisition module 11, first control signal generation module 12, second control signal generation module 13, motor control module 14. DETAILED DESCRIPTION

[0022] This application provides a control method and system for a napping machine based on adaptive tension adjustment. It addresses the technical problems in existing napping machine control technologies, such as limited response speed, difficulty in timely adjustment of control strategies, leading to tension loss of control and affecting the normal operation of the napping machine and the quality of fabric products. The method achieves the technical effect of adaptively adjusting the tension of the napping machine, improving the operational stability of the napping machine and the quality of the fabric.

[0023] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0024] Example 1, as Figure 1 As shown, this application provides a brushing machine control method based on tension adaptive adjustment, which includes: By using tension detection devices installed at the infeed and outfeed ends of the napping machine, the actual tension value of the fabric is obtained in real time and compared with the preset target tension value to obtain the real-time tension deviation and the rate of change of tension deviation.

[0025] Specifically, a tension detection device is arranged at the fabric inlet end and the fabric outlet end of the sanding machine, respectively, and the tension detection device includes a tension sensor and a signal processing unit, such as a magnetostrictive, strain gauge or float roller sensor. The fabric inlet end of the sanding machine refers to the fabric feeding side, i.e. the area where the fabric enters the sanding roller, and the fabric outlet end refers to the fabric collecting side, i.e. the stable area before the fabric is wound after sanding. The fabric inlet end tension detection device monitors the initial tension state of the fabric before entering the sanding area in real time through the tension sensor, and the fabric outlet end tension detection device continuously tracks the tension change of the fabric after sanding. After the sanding machine is started, the tension detection devices installed at the fabric inlet end and the fabric outlet end of the sanding machine collect the actual tension value of the fabric in real time, and convert the actual tension value into a digital signal through the signal processing unit analog-digital converter. Compare the real-time tension value with the preset target tension value, calculate the real-time tension deviation through the difference calculation algorithm, which reflects the accuracy of the current tension control. At the same time, the differential operation is performed on the continuously collected tension deviation to obtain the tension deviation change rate, which is used to reflect the tension fluctuation trend. The preset target tension value is set in advance according to the expert experience, the type of fabric, the working condition of the sanding machine and the production requirement, and is used to evaluate whether the actual tension meets the production requirement. Through real-time monitoring and comparison of tension deviation and change rate, accurate control basis is provided for tension adjustment, and stable tension control of the sanding machine in the running state is realized.

[0026] Further, the determination step of the preset target tension value includes: constructing an expert rule base, wherein the expert rule base stores a set of optimized control parameters corresponding to different fabric materials, and each control parameter in the set of optimized control parameters has a preset tension value label; receiving input current fabric material information, calling the optimized control parameters matched with the current fabric material from the expert rule base, performing initialization control of the sanding machine, and extracting the corresponding preset tension value label to generate the preset target tension value.

[0027] Further, the optimized control parameters are used as initial parameters of the model-free adaptive control algorithm to initialize the pseudo-Jacobian matrix.

[0028] Specifically, by industry expert experience, historical data and experimental test data, an expert rule base is constructed, which stores corresponding optimized control parameter sets for different fabric materials such as pure cotton, polyester cotton, silk, denim, etc. Each optimized control parameter set contains multiple control parameters such as tension, speed, etc., and each parameter has a preset tension value label, which refers to the target tension value used when processing the corresponding fabric material. When the sanding machine processes fabric, the input material information of the current fabric is received, and according to the material information, the optimized control parameter set matching the material of the current fabric is retrieved from the expert rule base. For example, the input fabric material is 60S pure cotton, and the cotton-high count parameter set is matched from the expert rule base, and the optimized control parameters such as the preset target tension value 120N, the initial fabric line speed 25m / min, and the sanding roller initial speed 1000r / min are obtained. The optimized control parameters obtained by matching the expert rule base are used as the initial parameters of the model-free adaptive control algorithm, the pseudo-Jacobian matrix is initialized, which is used to describe the dynamic relationship between the input control parameters and the output tension value, and the initial value is estimated by the static mapping relationship between the parameter set and the target tension. The initial control of the sanding machine is performed, and the initial tension, speed, etc. of the sanding machine are set. At the same time, the corresponding tension value label is extracted from the optimized control parameters as the preset target tension value.

[0029] By obtaining the optimized control parameters and the preset tension value from the expert rule base, accurate initialization control is realized for different fabric materials, which not only improves the adaptability and flexibility of the sanding machine control, but also ensures the stability of the sanding process and product quality. In addition, by initializing the pseudo-Jacobian matrix, the performance of the model-free adaptive control algorithm can be improved, thereby realizing faster and more accurate tension control.

[0030] The model-free adaptive control algorithm is adopted to generate a first control signal according to the real-time tension deviation and the tension deviation change rate.

[0031] Further, generating a first control signal according to the tension deviation and the tension deviation change rate includes: using a model-free adaptive control algorithm to estimate the relationship between the control signal and the tension, and establishing a pseudo-Jacobian matrix; based on the pseudo-Jacobian matrix, generating the first control signal according to the tension deviation and the tension deviation change rate.

[0032] Specifically, the model-free adaptive control algorithm is an algorithm that can achieve adaptive control without system data model, and the control law is learned online to adapt to the change of parameters. When the model-free adaptive control algorithm is initialized, the initial control gain is set according to the optimized control parameters obtained from the expert rule library, which provides a starting point for the model-free adaptive control algorithm, so that the model-free adaptive control can start to adapt to the dynamic characteristics of the milling machine control. In each control period, the tension measurement value and the control signal are collected in real time, and the tension deviation and the tension deviation rate data are calculated. The tension deviation and the tension deviation rate data are used to continuously adjust the control gain through the iterative learning mechanism to minimize the tension deviation, and a pseudo-Jacobian matrix is established. The pseudo-Jacobian matrix is a dynamically changing coefficient, and its physical meaning can be understood as the estimated value of the fabric tension change caused by the unit change of the control signal, such as the motor speed adjustment amount, at the current time. Further, based on the established pseudo-Jacobian matrix, the tension deviation and the tension deviation rate are input to generate the first control signal of the current milling machine control. Through the real-time tension deviation and its rate driving control decision, combined with the online update of the pseudo-Jacobian matrix, adaptive control without data model is realized. Moreover, the online update mechanism of the pseudo-Jacobian matrix enables the algorithm to continuously learn the change of system dynamic characteristics, avoids the decline of milling machine control performance caused by model mismatch, and ensures long-term running stability.

[0033] Further, the expression of generating the first control signal is as follows: ; wherein, and is a control gain for adjusting the intensity of control response; is a pseudo-Jacobian matrix, , refers to the change amount of the control signal, refers to the change amount of the tension deviation; is the real-time tension deviation at the current time; is the real-time tension deviation rate at the current time.

[0034] Specifically, the specific expression of generating the first control signal is: , wherein, and is a control gain, if the control system responds too fast, for example, the tension changes too much, then the gain is reduced, can reduce the correction strength of the control system to the current tension deviation, so that the change amplitude of the control signal is reduced, The sensitivity of the control system to the tension change trend is reduced, and excessive response of the control system to the change trend is avoided to prevent excessive tension fluctuation. By reducing the two gains, the over-reaction of the control system is reduced, and the stability of the control process is improved. If the response is too slow, for example, the tension adjustment is not timely, which indicates that the control signal is not strong enough, the gain is increased to speed up the adjustment, so that the control process can respond to the tension change in time, and the timeliness and accuracy of the tension control are ensured. By reasonably adjusting the control gain, the performance of the model-free adaptive control algorithm can be optimized, so that the tension control of the sanding machine can be stable, fast and accurate under different working conditions. is a pseudo-Jacobian matrix, reflects the influence degree of the unit change of the control signal on the change of the tension deviation. refers to the change of the control signal, refers to the change of the tension deviation; is the real-time tension deviation at the current moment; is the real-time tension deviation rate at the current moment. The first term −k 1 ⋅J ( t ) ⋅e ( t ) provides proportional adjustment according to the current deviation, and the second term − k2· J ( t ) ⋅ė ( t ) provides differential adjustment according to the change trend of the deviation. When generating the first control signal, the real-time tension deviation at the current moment and the change rate are combined with the updated , and are substituted into the expression for generating the first control signal to calculate, so as to realize accurate, smooth and adaptive tension adjustment control signal generation.

[0035] The real-time roll diameter signals of the unwinding roller and the winding roller of the sanding machine are collected, and the second control signal for compensating the tension disturbance caused by the change of the roll diameter is generated through a feedforward calculation model according to the roll diameter signals and the current fabric linear speed.

[0036] Further, the real-time roll diameter signals of the unwinding roller and the winding roller of the sanding machine are collected, and the second control signal for compensating the tension disturbance caused by the change of the roll diameter is generated through a feedforward calculation model according to the roll diameter signals and the current fabric linear speed, including: taking a preset target tension value as a retrieval factor, retrieving roll diameter test data and corresponding fabric linear speed test data of the unwinding roller and the winding roller, and constructing a synchronous ratio relationship; collecting synchronous ratio deviation data and corresponding correction signals based on the synchronous ratio relationship to train the feedforward calculation model; inputting the real-time roll diameter signals into the feedforward calculation model to generate the second control signal.

[0037] Specifically, the real-time roll diameter signals of the unwinding roll and the winding roll of the sanding machine are collected by sensors installed on the unwinding roll and the winding roll, such as photoelectric encoders or other types of displacement sensors, which can accurately measure the change of the roll diameter. At the same time, the linear speed of the fabric running on the sanding machine is measured by a speed sensor installed on the fabric transmission path, which can convert the linear speed of the fabric into a pulse signal, and determine the linear speed value by calculating the number of pulses per unit time, ensuring the accuracy of the linear speed measurement. Since the tension disturbance of the sanding machine is mainly caused by the change of the roll diameter, the increase or decrease of the roll diameter will cause the change of the fabric tension, and as the roll diameter of the unwinding roll increases, the tension of the fabric will decrease, while as the roll diameter of the winding roll increases, the tension of the fabric will increase. At the same time, as the roll diameter of the unwinding roll decreases, if the constant linear speed is to be maintained, the angular speed of the unwinding motor must be increased, which will produce a tendency to relax the fabric, resulting in a decrease in tension. Conversely, when the roll diameter of the winding roll increases, the angular speed of the winding motor must be reduced, which will produce a tendency to tighten the fabric, resulting in an increase in tension. Based on this relationship, a feedforward calculation model is constructed, and the specific steps are as follows: taking the preset target tension value as the retrieval factor, searching in a large amount of historical test data, which contains the roll diameter test data of the unwinding roll and the winding roll in different states and the corresponding fabric linear speed test data, using the query function of the database, taking the target tension value as the key retrieval condition, screening out the relevant roll diameter and linear speed data that meet the specific tension requirement, and constructing the synchronous ratio relationship. The synchronous ratio relationship refers to the internal relationship between the roll diameter data of the unwinding roll, the roll diameter data of the winding roll and the linear speed of the fabric, which meets the constraint condition of the target tension value. For example, by data fitting method, the least square method is used to analyze the functional relationship between the roll diameter D 放 of the unwinding roll, the roll diameter D 收 of the winding roll and the linear speed V of the fabric, V=f(D 放 , D 收). The synchronous ratio deviation data, such as the difference between the actual ratio and the synchronous ratio relationship caused by mechanical characteristics or slight slippage, and the correction signal applied to eliminate the deviation, which is the compensation amount for the motor speed, are collected. A feedforward calculation model is trained using machine learning algorithms, such as neural networks, with the synchronous ratio deviation data and the corresponding correction signal data as input and the correction amount as expected output. The feedforward calculation model learns how to generate appropriate correction control signals based on the synchronous ratio deviation. The feedforward calculation model can predict the adjustment needed for the motor angular velocity to maintain constant tension under given linear speed and roll diameter changes. Using the feedforward calculation model, the second control information for compensating for the tension disturbance caused by roll diameter changes is calculated based on the relationship between linear speed and roll diameter. The second control information is used to adjust the angular velocity of the motor to compensate for the tension disturbance caused by roll diameter changes.

[0038] By collecting the roll diameter signal in real time and combining it with the linear speed information, the feedforward calculation model generates the second control signal, which can compensate for the tension disturbance caused by roll diameter changes in advance. Compared with feedback control alone, feedforward control can respond more quickly to roll diameter changes, reduce tension fluctuations, improve the accuracy and stability of tension control, and ensure that the grinding machine maintains constant fabric tension under different roll diameter changes, thereby improving the quality and consistency of grinding processing.

[0039] The first control signal and the second control signal are combined to obtain the total control signal, which is output to the drive device of the fabric feeding motor and / or the fabric discharging motor of the grinding machine to adjust the motor speed.

[0040] Specifically, the first control signal generated based on the model-free adaptive control algorithm and the second control information based on the change of the roll diameter are synthesized, for example, weighted fusion according to the characteristics of both and the influence weight on the control of the raising machine. The feedforward control can compensate for predictable disturbances such as changes in roll diameter in advance, and the feedback control can correct unpredictable disturbances and model errors. According to expert experience or optimization algorithms such as genetic algorithms, the coefficient value is adjusted iteratively to minimize the tension fluctuation index, such as the variance of the tension deviation, as the goal to find the optimal weighting coefficient combination, and set appropriate weighting coefficients w1 and w2. At the same time, during the synthesis process, the effectiveness and rationality of the two control signals are monitored in real time. If an abnormal signal occurs, such as exceeding the pre-set reasonable range, the weighting coefficient is automatically adjusted to reduce the influence weight of the abnormal signal, ensuring the stability of the total control signal. The total control signal u(t) = w1·u1(t) + w2·u2(t) is calculated, where u1(t) and u2(t) are the first control signal and the second control signal respectively. After obtaining the total control signal, it is output to the drive device of the fabric inlet motor and / or fabric outlet motor of the raising machine. The drive device uses a frequency converter or a servo driver, which can convert the total control signal into corresponding motor speed adjustment instructions. For example, for a frequency converter, the total control signal is converted into a digital signal after digital-to-analog conversion, and used as the frequency given signal of the frequency converter. The frequency converter adjusts the frequency of the output power supply according to the signal, thereby changing the speed of the motor. For a servo driver, the total control signal is used to set the target speed and torque parameters of the motor, and the servo driver accurately adjusts the operation of the motor through closed-loop control. For example, if the roll diameter of the unwinding section changes greatly during the raising process, while the winding section is relatively stable: the initial roll diameter of the unwinding roller is 800 mm, and as the fabric is continuously unwound, the roll diameter quickly decreases to 600 mm. After analyzing and processing the total control signal generated by the feedforward calculation model and feedback control, it is determined that the change in unwinding speed mainly affects the tension, and the total control signal is output to the drive device of the motor, such as a frequency converter. The frequency converter initially sets the frequency to 50 Hz, and for a 4-pole motor, the corresponding fabric inlet motor speed is 1440 r / min. After conversion, the total control signal requires the frequency converter to adjust the frequency to 52 Hz. The frequency converter adjusts the output power supply frequency according to the total control signal, so that the fabric inlet motor speed increases to 1497.6 r / min, and the speed and frequency are approximately proportional. By increasing the fabric inlet motor speed, the fabric tension decrease caused by the decrease in the roll diameter of the unwinding roller is compensated, and the fabric tension in the raising area is maintained stable. If the roll diameter of the unwinding roller decreases from 900 mm to 700 mm, and the roll diameter of the winding roller increases from 600 mm to 800 mm, the total control signal is output to the drive devices of the fabric inlet motor and fabric outlet motor respectively after comprehensive judgment.For the in-feed motor frequency converter, the frequency is adjusted from 48 Hz to 50 Hz, so that the in-feed motor speed is increased from 1382.4 r / min to 1440 r / min. For the out-feed motor servo driver, the speed is adjusted from 1100 r / min to 1050 r / min. By adjusting the speed of the in-feed and out-feed motors at the same time, the unwinding and winding speeds of the fabric are cooperatively controlled, and the fabric tension in the sanding process is accurately maintained stable.

[0041] By synthesizing the first control signal and the second control signal to obtain a total control signal and outputting the adjusted motor speed, the advantages of feedforward control and feedback control can be fully utilized. The feedforward control compensates for known disturbances such as roll diameter changes in advance, and the feedback control corrects unforeseen disturbances and model errors in time. The combination of the two makes the system more comprehensive and faster in responding to various factors affecting the tension, effectively reduces the tension fluctuation, improves the precision and stability of the sanding machine tension control, ensures that the fabric maintains constant tension during the sanding process, and thus improves the sanding quality and product consistency.

[0042] Further, obtaining the total control signal further includes: collecting a speed signal fed back by a motor encoder and a torque current signal fed back by a driver; estimating a total disturbance by a disturbance observer according to the speed signal and the torque current signal, and generating a third control signal for offsetting the total disturbance; and generating the total control signal according to the first control signal, the second control signal, and the third control signal.

[0043] Specifically, to further improve the accuracy and anti-interference ability of tension control, a high-precision motor encoder is used to collect the motor speed signal in real time. The motor encoder adopts an incremental or absolute encoder. The incremental encoder represents the motor speed and rotation direction through output pulse signals, and the output pulse frequency is proportional to the motor speed. The absolute encoder can provide a unique digital code for each position, and can directly obtain the absolute position and speed information of the motor. At the same time, the torque current signal is obtained from the motor driver, and the driver accurately measures the torque current of the motor through a current sensor. Then, the collected speed signal and torque current signal are input into the disturbance observer, which is a model-based control strategy. According to the dynamics model of the motor and the input information of the speed signal and the torque current signal, the total disturbance suffered by the motor during operation is estimated. By comparing the actual output with the model predicted output, a third control signal is generated. Further, the first control signal generated based on the model-free adaptive control algorithm, the second control signal generated by the feedforward calculation model, and the third control signal generated by the disturbance observer are synthesized to generate a total control signal. The synthesis method can use weighted summation, and different weights are given according to the importance and effectiveness of each control signal to calculate the total control signal. Further, the total control signal is output to the drive device of the cloth feeding motor and / or cloth discharging motor of the brushing machine to adjust the motor speed.

[0044] By collecting the motor speed and torque current signals and using the disturbance observer to estimate the total disturbance to generate a third control signal, and synthesizing the first and second control signals to generate a total control signal, the accuracy and stability of tension control can be further improved, ensuring that the brushing machine can achieve constant fabric tension control under different working conditions, improving brushing quality and production efficiency.

[0045] Further, the disturbance observer estimates the total disturbance based on the speed signal and the torque current signal to generate a third control signal for offsetting the total disturbance, including: constructing a disturbance-free inverse model based on the driving principle of the driver and the motor; inputting the torque current signal into the disturbance-free inverse model for forward estimation to obtain an ideal motor speed, and comparing it with the speed signal to obtain an actual speed deviation; inputting the actual speed deviation into the disturbance-free inverse model for reverse calculation of disturbance to obtain a total disturbance estimation torque; and generating the third control signal based on the total disturbance estimation torque.

[0046] Specifically, the core of the disturbance observer is to construct a disturbance-free inverse model based on the driving principle of the driver and the motor. The disturbance-free inverse model is Gn(s) / Jn*s, where Gn(s) refers to the nominal transfer function of the motor, reflecting the input-output relationship of the motor in the ideal state, Jn refers to the nominal moment of inertia of the motor, which is the theoretical or design value, and s is the Laplace operator. The disturbance-free inverse model ignores all disturbances and is a simplified mathematical model of the motor under ideal conditions of no load, no friction, and accurate parameters. Through the disturbance-free inverse model, the ideal motor speed that the motor should reach under ideal disturbance-free conditions is obtained. The ideal motor speed obtained by forward estimation is compared with the actual measured speed signal, and the actual speed deviation is calculated. The actual speed deviation reflects the influence of disturbances on the motor speed. The calculated actual speed deviation is input into the disturbance-free inverse model for inverse calculation of disturbances, i.e., multiplied by Jn*s, to obtain the total disturbance estimated torque, which is an estimated value of the actual disturbance torque and comprehensively reflects the influence of various unknown disturbances on the motor torque. Finally, the third control signal is generated based on the total disturbance estimated torque. The third control signal is a control quantity used to offset the influence of disturbances, and its size and direction are opposite to those of the disturbance torque to achieve complete compensation for disturbances. For example, if the estimated disturbance torque is in the positive direction, the third control signal will control the driver to output a torque or current in the negative direction to offset the influence of disturbances.

[0047] By constructing a disturbance-free inverse model, estimating the total disturbance using torque current signals and actual speed signals, and generating a third control signal, various unknown disturbances encountered during motor operation can be compensated in real time and accurately, further improving the performance of the disturbance observer, improving the stability and accuracy of tension control, ensuring that the brushing machine can maintain constant fabric tension under various disturbances, and improving product quality and production efficiency.

[0048] Further, the total control signal is output to the driving device of the in-feed motor and / or the out-feed motor of the brushing machine, and after adjusting the motor speed, the method further comprises: monitoring the change state of the tension deviation in real time, determining that a sudden disturbance event occurs when the change amount of the tension deviation within a unit time exceeds a first preset threshold, and immediately triggering a compliant control mode; in the compliant control mode, temporarily switching to a preset compliant control parameter set; and after the sudden disturbance event ends, returning to the normal control mode.

[0049] Specifically, after outputting the total control signal to the driving device of the fabric feeding motor and / or the fabric discharging motor of the sanding machine to adjust the motor speed, the tension deviation of the fabric is monitored in real time by using the tension sensor, and the change amount of the tension deviation in a unit time is calculated. When the change amount of the tension deviation in a unit time exceeds a first preset threshold value, it is determined that a sudden disturbance event occurs, and the first preset threshold value is obtained by comprehensively considering the normal running tension range of the sanding machine, the fabric characteristics, historical data and other factors, for example, by experiment and simulation, the change rule of the tension deviation under different disturbance conditions is analyzed, so that the first preset threshold value is determined to ensure that the sudden disturbance event can be accurately identified. The sudden disturbance event, such as the passage of the fabric joint, will cause a huge impact on the tension and cause the tension deviation to change sharply due to the completely different thickness and elasticity of the joint compared with the normal fabric. When it is determined that the sudden disturbance event occurs, the soft control mode is triggered immediately, and in the soft control mode, the sanding machine control parameters are temporarily switched to a preset soft control parameter set. The soft control parameter set is determined and configured by a person skilled in the art through a large number of tests in advance, and its function is to reduce the response speed and control gain of the control. For example, reducing the control gain can reduce the sensitivity of the sanding machine control to the tension deviation, avoiding the situation that the sanding machine overreacts to cause greater tension fluctuation under sudden disturbance, and reducing the response speed can make the sanding machine adjust more stably when facing sudden disturbance, reducing oscillation. When the sudden disturbance event ends, it is determined whether the disturbance is eliminated by monitoring the change of the tension deviation. For example, when the change amount of the tension deviation in a plurality of consecutive sampling periods is less than a smaller threshold value, it is considered that the sudden disturbance event ends. At this time, the normal control mode is automatically restored, and the control parameters of the total control signal are used for tension control.

[0050] By monitoring the change of the tension deviation in real time and setting the soft control mode, the control strategy can be adjusted in time when encountering the passage of the fabric joint and other sudden disturbance events, avoiding the situation that the sanding machine loses control due to overreaction, improving the adaptability and stability of the sanding machine tension control under complex working conditions, and effectively reducing the fabric quality problems and equipment failures caused by sudden disturbance, ensuring the normal operation of the sanding machine and the quality of the fabric product.

[0051] Embodiment two, based on the same inventive concept as the sanding machine control method based on tension self-adaptive adjustment in the foregoing embodiments, as Figure 2 The application provides a sanding machine control system based on tension self-adaptive adjustment, wherein the sanding machine control system based on tension self-adaptive adjustment comprises: The data acquisition module 11 is configured to acquire actual tension values of the fabric in real time through tension detection devices arranged at the fabric input end and the fabric output end of the raising machine, compare the actual tension values with preset target tension values, and obtain real-time tension deviations and tension deviation change rates; the first control signal generation module 12 is configured to generate a first control signal according to the real-time tension deviations and the tension deviation change rates by using a model-free adaptive control algorithm; the second control signal generation module 13 is configured to acquire real-time roll diameter signals of the unwinding roller and the winding roller of the raising machine, and generate a second control signal for compensating for tension disturbances caused by roll diameter changes by using a feedforward calculation model according to the roll diameter signals and a current fabric linear speed; and the motor control module 14 is configured to obtain a total control signal by synthesizing the first control signal and the second control signal, and output the total control signal to a driving device of the fabric input motor and the fabric output motor of the raising machine to adjust the motor rotating speed.

[0052] Further, the first control signal generation module 12 in the raising machine control system based on tension adaptive adjustment is further configured to estimate the relationship between the control signal and the tension by using a model-free adaptive control algorithm, and establish a pseudo-Jacobian matrix; and generate the first control signal according to the tension deviation and the tension deviation change rate based on the pseudo-Jacobian matrix.

[0053] Further, the first control signal generation module 12 in the raising machine control system based on tension adaptive adjustment is further configured to generate an expression of the first control signal as follows: ; wherein, and is a control gain, used to adjust the intensity of the control response; is a pseudo-Jacobian matrix, , is a change amount of the control signal, is a change amount of the tension deviation; is a real-time tension deviation at a current moment; is a real-time tension deviation change rate at a current moment.

[0054] Further, the motor control module 14 in the raising machine control system based on tension adaptive adjustment is further configured to acquire a rotating speed signal fed back by a motor encoder and a torque current signal fed back by a driver; estimate a total disturbance by using a disturbance observer according to the rotating speed signal and the torque current signal, generate a third control signal for offsetting the total disturbance; and generate the total control signal according to the first control signal, the second control signal and the third control signal.

[0055] Further, the motor control module 14 in the tension self-adaptive adjustment based brushing machine control system is further configured to: construct a disturbance-free inverse model based on the driving principle of the driver and the motor; input the torque current signal into the disturbance-free inverse model for forward estimation to obtain an ideal motor speed, and compare the ideal motor speed with the speed signal to obtain an actual speed deviation; input the actual speed deviation into the disturbance-free inverse model for reverse calculation of disturbance to obtain a total disturbance estimated torque; and generate the third control signal based on the total disturbance estimated torque.

[0056] Further, the motor control module 14 in the tension self-adaptive adjustment based brushing machine control system is further configured to: monitor the change state of the tension deviation in real time, determine that a sudden disturbance event occurs when the change amount of the tension deviation in a unit time exceeds a first preset threshold, and immediately trigger a compliant control mode; temporarily switch to a preset compliant control parameter set in the compliant control mode; and restore to a normal control mode after the sudden disturbance event ends.

[0057] Further, the second control signal generation module 13 in the tension self-adaptive adjustment based brushing machine control system is further configured to: take a preset target tension value as a retrieval factor, retrieve the diameter test data of the unwinding roller and the winding roller and the corresponding fabric linear speed test data, and construct a synchronization ratio relationship; collect synchronization ratio deviation data and corresponding correction signals based on the synchronization ratio relationship to train the feedforward calculation model; and input the real-time diameter signal into the feedforward calculation model to generate the second control signal.

[0058] Further, the data acquisition module 11 in the tension self-adaptive adjustment based brushing machine control system is further configured to: construct an expert rule library, the expert rule library storing optimized control parameter sets corresponding to different fabric materials, and each control parameter in the optimized control parameter set having a preset tension value label; receive input current fabric material information, call the optimized control parameter matching the current fabric material from the expert rule library, perform initialization control of the brushing machine, and extract the corresponding preset tension value label to generate a preset target tension value.

[0059] Further, the data acquisition module 11 in the tension self-adaptive adjustment based brushing machine control system is further configured to: take the optimized control parameter as an initial parameter of the model-free adaptive control algorithm to initialize the pseudo-Jacobian matrix.

[0060] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The foregoing Figure 1The control method based on tension self-adaptive adjustment in Embodiment One and the specific example are also applicable to the control system based on tension self-adaptive adjustment in this embodiment. Through the foregoing detailed description of the control method based on tension self-adaptive adjustment, those skilled in the art can clearly understand the control system based on tension self-adaptive adjustment in this embodiment. Therefore, for the sake of brevity of the description, the control system based on tension self-adaptive adjustment will not be described in detail herein.

[0061] The above description of disclosed embodiments enables one skilled in the art to make or use the application. Numerous modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0062] Obviously, for those skilled in the art, some improvements and modifications can be made to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the protection scope of the present application.

Claims

1. A control method for a sanding machine based on adaptive tension adjustment, characterized in that, include: By using tension detection devices installed at the infeed and outfeed ends of the napping machine, the actual tension value of the fabric is obtained in real time and compared with the preset target tension value to obtain the real-time tension deviation and the rate of change of tension deviation. A model-free adaptive control algorithm is used to generate a first control signal based on the real-time tension deviation and the rate of change of the tension deviation. The real-time roll diameter signals of the unwinding and take-up rollers of the napping machine are collected, and a second control signal is generated through a feedforward calculation model based on the roll diameter signals and the current fabric linear speed to compensate for tension disturbances caused by changes in roll diameter. The first control signal and the second control signal are combined to obtain a total control signal, which is then output to the drive device of the feed motor and / or output motor of the napping machine to adjust the motor speed.

2. The grinding machine control method based on tension adaptive adjustment as described in claim 1, characterized in that, Based on the tension deviation and the rate of change of the tension deviation, a first control signal is generated, including: A model-free adaptive control algorithm is used to estimate the relationship between the control signal and the tension, and a pseudo-Jacobi matrix is ​​established. Based on the pseudo-Jacobi matrix, the first control signal is generated according to the tension deviation and the rate of change of the tension deviation.

3. The brushing machine control method based on tension adaptive adjustment as described in claim 2, characterized in that, The expression for generating the first control signal is as follows: ； in, and It is the control gain, used to adjust the strength of the control response; It is a pseudo-Jacobi matrix. , This refers to the change in the control signal. This refers to the amount of change in tension deviation; It is the real-time tension deviation at the current moment; It is the rate of change of the real-time tension deviation at the current moment.

4. The brushing machine control method based on tension adaptive adjustment as described in claim 1, characterized in that, The overall control signal also includes: Acquire the speed signal fed back by the motor encoder and the torque current signal fed back by the driver; The disturbance observer estimates the total disturbance based on the speed signal and the torque current signal, and generates a third control signal to counteract the total disturbance. The total control signal is generated based on the first control signal, the second control signal, and the third control signal.

5. The brushing machine control method based on tension adaptive adjustment as described in claim 4, characterized in that, The disturbance observer estimates the total disturbance based on the speed signal and the torque current signal, and generates a third control signal to counteract the total disturbance, including: A perturbation-free inverse model is constructed based on the driving principles of the driver and motor. The torque current signal is input into the disturbance-free inverse model for forward estimation to obtain the ideal motor speed, and then compared with the speed signal to obtain the actual speed deviation. The actual speed deviation is input into the disturbance-free inverse model to calculate the disturbance in reverse, and the total disturbance estimated torque is obtained. The third control signal is generated based on the estimated torque from the total disturbance.

6. The brushing machine control method based on tension adaptive adjustment as described in claim 1, characterized in that, The main control signal is output to the drive unit of the feed motor and / or output motor of the napping machine. After adjusting the motor speed, the system further includes: The system monitors the changes in tension deviation in real time. When the change in tension deviation exceeds the first preset threshold within a unit of time, it determines that a sudden disturbance event has occurred and immediately triggers the compliant control mode. In the compliant control mode, the system temporarily switches to a preset set of compliant control parameters; And after the sudden disturbance event ends, it will return to normal control mode.

7. The brushing machine control method based on tension adaptive adjustment as described in claim 6, characterized in that, The real-time roll diameter signals of the unwinding and take-up rollers of the napping machine are acquired, and based on the roll diameter signals and the current fabric linear speed, a second control signal is generated through a feedforward calculation model to compensate for tension disturbances caused by changes in roll diameter, including: Using the preset target tension value as a retrieval factor, retrieve the roll diameter test data of the unwinding roller and the winding roller, as well as the corresponding fabric linear speed test data, and construct a synchronization ratio relationship; Based on the synchronization ratio relationship, the synchronization ratio deviation data and the corresponding correction signal are collected to train the feedforward calculation model; The real-time roll diameter signal is input into the feedforward calculation model to generate the second control signal.

8. The grinding machine control method based on tension adaptive adjustment as described in claim 2, characterized in that, The steps for determining the preset target tension value include: An expert rule base is constructed, which stores a set of optimized control parameters corresponding to different fabric materials, and each control parameter in the set of optimized control parameters has a preset tension value label; The system receives the current fabric material information, retrieves the optimized control parameters matching the current fabric material from the expert rule base, performs the initialization control of the brushing machine, extracts the corresponding preset tension value label, and generates the preset target tension value.

9. The grinding machine control method based on tension adaptive adjustment as described in claim 8, characterized in that, The optimized control parameters are used as the initial parameters for the model-free adaptive control algorithm to initialize the pseudo-Jacobi matrix.

10. A brushing machine control system based on tension adaptive adjustment, characterized in that, The steps for implementing the tension adaptive adjustment-based grinding machine control method according to any one of claims 1 to 9 include: The data acquisition module is used to acquire the actual tension value of the fabric in real time through tension detection devices set at the infeed and outfeed ends of the napping machine, compare it with the preset target tension value, and obtain the real-time tension deviation and tension deviation change rate. The first control signal generation module is used to generate a first control signal based on the real-time tension deviation and the rate of change of the tension deviation using a model-free adaptive control algorithm. The second control signal generation module is used to collect the real-time roll diameter signals of the unwinding and take-up rolls of the napping machine, and generate a second control signal to compensate for tension disturbances caused by roll diameter changes through a feedforward calculation model based on the roll diameter signals and the current fabric linear speed. The motor control module is used to combine the first control signal and the second control signal to obtain a total control signal, and output the total control signal to the drive device of the feed motor and the output motor of the napping machine to adjust the motor speed.

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