A control method and system for a sanding machine based on adaptive tension adjustment

Abstract

This invention provides a control method and system for a napping machine based on adaptive tension adjustment, belonging to the field of napping machine control technology. The method includes: real-time acquisition and comparison of the actual tension value of the fabric with the target tension value to obtain the real-time tension deviation and the rate of change of tension deviation; generating a first control signal based on the real-time tension deviation and the rate of change of tension deviation; acquiring the real-time roll diameter signals of the napping machine's unwinding and take-up rollers; and generating a second control signal based on the roll diameter signal and the current fabric linear speed through a feedforward calculation model; combining the first and second control signals to obtain a total control signal, which is then output to the drive device to adjust the motor speed. This invention solves the technical problems in existing napping machine control technology, such as limited response speed, difficulty in timely adjustment of control strategies, and impact on the normal operation of the napping machine and fabric quality. It 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.

Description

Technical Field

[0001] This invention relates to the field of brushing machine control technology, specifically to a brushing machine control method and system based on tension adaptive adjustment. Background Technology

[0002] A napping machine creates a fine nap on the surface of fabric through friction, enhancing its softness, warmth, and comfort. It is widely used in clothing, home textiles, and many other industries. During the operation of a napping machine, precise control of fabric tension is crucial for ensuring processing quality. The stability of tension directly affects the uniformity and density of the nap, as well as the overall smoothness of the fabric. Improper tension control can easily lead to quality problems such as wrinkles, edge tears, and uneven nap length, thus reducing product quality. Currently, tension control technology in napping machines is mainly based on traditional PID control algorithms. These algorithms perform proportional, integral, and derivative calculations on the deviation between the set tension value and the actual tension value to generate corresponding control signals to adjust the motor speed, thereby controlling the fabric tension. However, traditional PID control algorithms typically have fixed control parameters and limited response speed, making it difficult to adjust the control strategy in a timely and effective manner. This can easily lead to tension runaway, causing fabric damage and production interruptions, and fails to meet the modern textile industry's demand for high-precision, high-stability, and high-adaptability control in napping machines.

[0003] Existing technologies for napping machine control suffer from limited response speed, making it difficult to adjust control strategies in a timely manner, leading to tension loss of control, which affects the normal operation of the napping machine and the quality of fabric products. Summary of the Invention

[0004] The purpose of this application is to provide a brushing machine control method and system based on tension adaptive adjustment, which solves the technical problems in the existing brushing machine control, such as limited response speed, difficulty in timely adjustment of control strategy, resulting in tension loss of control, affecting the normal operation of brushing machine and the quality of fabric products.

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

[0006] The first aspect of this application provides a control method for a napping machine based on tension adaptive adjustment. The method includes: acquiring the actual tension value of the fabric in real time through tension detection devices installed at the fabric inlet and outlet ends of the napping machine, comparing it with a preset target tension value to obtain the real-time tension deviation and the rate of change of tension deviation; generating a first control signal based on the real-time tension deviation and the rate of change of tension deviation using a model-free adaptive control algorithm; acquiring the real-time roll diameter signals of the unwinding and take-up rollers of the napping machine, and generating 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; combining the first control signal and the second control signal to obtain a total control signal, and outputting the total control signal to the drive device of the fabric inlet motor and / or the fabric outlet motor of the napping machine to adjust the motor speed.

[0007] Furthermore, 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.

[0008] Furthermore, 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.

[0009] Furthermore, the speed signal fed back by the motor encoder and the torque current signal fed back by the driver are acquired; the total disturbance is estimated by the disturbance observer based on the speed signal and the torque current signal, and a third control signal is generated to cancel the total disturbance; the total control signal is generated based on the first control signal, the second control signal and the third control signal.

[0010] Furthermore, a disturbance-free inverse model is constructed based on the driving principle 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 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 total disturbance estimated torque.

[0011] Furthermore, the system monitors the changes in tension deviation in real time. When the change in tension deviation exceeds a first preset threshold within a unit of time, a sudden disturbance event is determined to have occurred, and a compliant control mode is immediately triggered. In the compliant control mode, the system temporarily switches to a preset set of compliant control parameters. After the sudden disturbance event ends, the system returns to the normal control mode.

[0012] Furthermore, using a preset target tension value as a retrieval factor, the roll diameter test data of the unwinding roller and the winding roller, as well as the corresponding fabric linear speed test data, are retrieved to construct a synchronization ratio relationship; based on the synchronization ratio relationship, synchronization ratio deviation data and corresponding correction signals 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.

[0013] Furthermore, 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 optimized control parameter set has a preset tension value label; the current fabric material information is received, the optimized control parameters matching the current fabric material are called from the expert rule base, the initialization control of the brushing machine is performed, and the corresponding preset tension value label is extracted to generate a preset target tension value.

[0014] Furthermore, the optimized control parameters are used as the initial parameters for the model-free adaptive control algorithm to initialize the pseudo-Jacobi matrix.

[0015] A second aspect of this application provides a control system for a napping machine based on tension adaptive adjustment. The system includes: a data acquisition module for acquiring the actual tension value of the fabric in real time via tension detection devices installed at the fabric inlet and outlet ends of the napping machine, comparing it with a preset target tension value to obtain the real-time tension deviation and the rate of change of tension deviation; a first control signal generation module for generating a first control signal based on the real-time tension deviation and the rate of change of tension deviation using a model-free adaptive control algorithm; a second control signal generation module for acquiring the real-time roll diameter signals of the napping machine's unwinding and take-up rollers, and generating 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; and a motor control module for combining the first and second control signals to obtain a total control signal, and outputting the total control signal to the drive devices of the napping machine's inlet and outlet motors to adjust the motor speed.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0017] This application provides a control method for a napping machine based on adaptive tension adjustment. Tension detection devices installed at the feed and output ends of the napping machine acquire the actual tension value of the fabric in real time, compare it with a 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 tension deviation. 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 using a feedforward calculation model based on the roll diameter signal and the current fabric linear speed to compensate for tension disturbances caused by roll diameter changes. The first and second control signals are combined to obtain a total control signal, which is then output to the drive devices of the feed motor and / or output motor of the napping machine to adjust the motor speed. This 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.

[0018] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in 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 merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a brushing machine control method based on tension adaptive adjustment provided in this application.

[0021] Figure 2 This is a schematic diagram of a grinding machine control system based on tension adaptive adjustment, provided in this application.

[0022] Explanation of reference numerals in the attached drawings: Data acquisition module 11, first control signal generation module 12, second control signal generation module 13, motor control module 14. Detailed Implementation

[0023] 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.

[0024] 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.

[0025] Example 1, as Figure 1 As shown, this application provides a brushing machine control method based on tension adaptive adjustment, which includes:

[0026] 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.

[0027] Specifically, tension detection devices are installed at both the feed and output ends of the napping machine. These devices include tension sensors and signal processing units, such as magnetic, strain gauge, or floating roller sensors. The feed end refers to the feeding side, where the fabric enters the napping roller, while the output end is the take-up side, the stable area after napping and before winding. The feed end tension detection device monitors the initial tension of the fabric before it enters the napping zone in real time using the tension sensor, while the output end tension detection device continuously tracks the tension change after the napping process. After the napping machine starts, the tension detection devices at both the feed and output ends collect the actual tension value of the fabric in operation in real time and convert it into a digital signal using an analog-to-digital converter. The real-time tension value is compared with a preset target tension value, and the real-time tension deviation is calculated using a difference calculation algorithm, reflecting the accuracy of the current tension control. Simultaneously, the continuously collected tension deviation is differentiated to obtain the tension deviation change rate, which reflects the tension fluctuation trend. The preset target tension value is set in advance based on expert experience, fabric type, napping machine operating conditions, and production requirements. It is used to assess whether the actual tension meets production requirements. By monitoring and comparing tension deviations and rates of change in real time, precise control basis is provided for tension adjustment, achieving stable tension control of the napping machine during operation.

[0028] Furthermore, the steps for determining the preset target tension value include: constructing an expert rule base, which stores a set of optimized control parameters corresponding to different fabric materials, and each control parameter in the optimized control parameter set has a preset tension value label; receiving the input current fabric material information, calling the optimized control parameters matching the current fabric material from the expert rule base, performing the initialization control of the brushing machine, and extracting the corresponding preset tension value label to generate the preset target tension value.

[0029] Furthermore, the optimized control parameters are used as the initial parameters for the model-free adaptive control algorithm to initialize the pseudo-Jacobi matrix.

[0030] Specifically, an expert rule base is constructed using industry expert experience, historical data, and experimental test data. This base stores optimized control parameter sets corresponding to different fabric materials, such as pure cotton, polyester-cotton blends, silk, and denim. Each optimized control parameter set contains multiple control parameters, such as tension and speed, and each parameter has a preset tension value label. This preset tension value label indicates the target tension value used when processing the corresponding fabric material. When the napping machine processes the fabric, it receives the input material information of the current fabric and retrieves the optimized control parameter set matching the current fabric material from the expert rule base. For example, if the input fabric material is 60S pure cotton poplin, the cotton-high count parameter set is matched from the expert rule base to obtain optimized control parameters, such as a preset target tension value of 120N, an initial fabric linear speed of 25 m / min, and an initial napping roller speed of 1000 rpm. The optimized control parameters obtained from matching the expert rule base are used as the initial parameters for the model-free adaptive control algorithm. This is used to initialize the pseudo-Jacobi matrix, which describes the dynamic relationship between the input control parameters and the output tension value. The initial value is estimated through the static mapping relationship between the parameter set and the target tension. This initialization control of the grinding machine sets the initial tension, speed, etc. Simultaneously, the corresponding tension value labels are extracted from the optimized control parameters and used as the preset target tension value.

[0031] By obtaining optimized control parameters and preset tension values ​​from an expert rule base, precise initialization control for different fabric materials is achieved. This not only improves the adaptability and flexibility of the napping machine control but also ensures the stability of the napping process and product quality. Furthermore, by initializing the pseudo-Jacobi matrix, the performance of the model-free adaptive control algorithm can be improved, thereby achieving faster and more precise tension control.

[0032] 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.

[0033] Furthermore, generating a first control signal based on the tension deviation and the rate of change of the tension deviation includes: estimating the relationship between the control signal and the tension using a model-free adaptive control algorithm, and establishing a pseudo-Jacobi matrix; generating the first control signal based on the pseudo-Jacobi matrix and the tension deviation and the rate of change of the tension deviation.

[0034] Specifically, the model-free adaptive control algorithm is an algorithm that can achieve adaptive control without a system data model, adapting to parameter changes by learning control laws online. During initialization, the model-free adaptive control algorithm sets an initial control gain based on optimized control parameters obtained from an expert rule base, providing a starting point for the algorithm and enabling it to adapt to the dynamic characteristics of the napping machine control. Within each control cycle, tension measurements and control signals are acquired in real time, and tension deviation and its rate of change are calculated. Using this data, the control gain is continuously adjusted through an iterative learning mechanism to minimize the tension deviation, establishing a pseudo-Jacobi matrix. The pseudo-Jacobi matrix is ​​a dynamically changing coefficient; its physical meaning can be understood as an estimate of the fabric tension change caused by a unit change in the control signal, such as the motor speed adjustment, at the current moment. Based on the established pseudo-Jacobi matrix, the tension deviation and its rate of change are input to generate the first control signal for the current napping machine control. By driving control decisions through real-time tension deviation and its rate of change, combined with the online updating of the pseudo-Jacobi matrix, adaptive control without a data model is achieved. The online update mechanism of the pseudo-Jacobi matrix enables the algorithm to continuously learn changes in the dynamic characteristics of the system, avoids the degradation of the grinding machine control performance due to model mismatch, and ensures long-term operational stability.

[0035] Furthermore, 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.

[0036] Specifically, the expression for generating the first control signal is as follows:

[0037] ,in, and It refers to the control gain. If the control system responds too quickly, for example, due to excessive tension changes, the gain is reduced. This can reduce the corrective force of the control system for the current tension deviation, thereby reducing the amplitude of changes in the control signal and decreasing... This reduces the control system's sensitivity to tension change trends, preventing excessive tension fluctuations caused by over-response to these trends. Reducing these two gains decreases the control system's overreaction and improves the stability of the control process. If the response is too slow, such as delayed tension adjustment, it indicates insufficient control signal strength. In this case, increasing the gain speeds up the adjustment, enabling the control process to respond promptly to tension changes and ensuring timely and accurate tension control. By appropriately adjusting the control gain, the performance of the model-free adaptive control algorithm can be optimized, allowing the grinding machine to achieve stable, fast, and accurate tension control under various operating conditions. It is a pseudo-Jacobi matrix. This reflects the degree to which a unit change in the control signal affects the change in tension deviation. 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; This is the rate of change of the real-time tension deviation. The first term... −k 1 ⋅J ( t ) ⋅e ( t The second item provides a proportional adjustment function based on the current deviation magnitude. −k2⋅J ( t ) ⋅ė ( t Based on the trend of deviation change, differential adjustment is provided. When generating the first control signal, the real-time tension deviation at the current moment is used. and its rate of change With the updated By combining these methods and substituting them into the expression for generating the first control signal, a precise, smooth, and adaptive tension adjustment control signal can be generated.

[0038] 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.

[0039] Furthermore, 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 roll diameter changes. This includes: using a preset target tension value as a retrieval factor, retrieving roll diameter test data of the unwinding and take-up rollers and corresponding fabric linear speed test data to construct a synchronization ratio relationship; acquiring synchronization ratio deviation data and corresponding correction signals based on the synchronization ratio relationship to train the feedforward calculation model; and inputting the real-time roll diameter signal into the feedforward calculation model to generate the second control signal.

[0040] Specifically, sensors installed on the unwind and take-up rollers collect real-time roll diameter signals from the napping machine. These sensors, such as photoelectric encoders or other types of displacement sensors, accurately measure changes in roll diameter. Simultaneously, speed sensors mounted on the fabric transport path measure the linear speed of the fabric running on the napping machine. These speed sensors convert the fabric's linear speed into pulse signals, and the linear speed value is determined by calculating the number of pulses per unit time, ensuring the accuracy of the linear speed measurement. Since tension disturbances in the napping machine are mainly caused by changes in roll diameter, increasing or decreasing the roll diameter leads to changes in fabric tension. As the unwind roller diameter increases, the fabric tension decreases, while increasing the take-up roller diameter increases the fabric tension. Furthermore, as the unwind roller diameter decreases, to maintain a constant linear speed, the angular velocity of the unwind motor must increase, creating a tendency to exacerbate fabric relaxation, leading to decreased tension. Conversely, as the take-up roller diameter increases, the angular velocity of the take-up motor must decrease, creating a tendency to tighten the fabric, leading to increased tension. Based on this relationship, a feedforward calculation model is constructed. The specific steps are as follows: using a preset target tension value as a retrieval factor, a large amount of historical test data is searched. This historical test data includes roll diameter test data of the unwinding and take-up rolls under different conditions, as well as corresponding fabric linear speed test data. Utilizing the database query function, with the target tension value as the key retrieval condition, relevant roll diameter and linear speed data that meet specific tension requirements are filtered out, and a synchronization ratio relationship is constructed. The synchronization ratio relationship refers to the inherent connection between the unwinding roll diameter data, the take-up roll diameter data, and the fabric linear speed, satisfying the target tension value as a constraint. For example, the unwinding roll diameter D is analyzed using the least squares method through data fitting. 放 , take-up roller diameter D 收 The functional relationship between the linear velocity of the fabric and the fabric is V = f(D). 放 D 收The process involves collecting synchronization ratio deviation data during the actual operation of a sanding machine, along with the correction signals applied to eliminate this deviation. The synchronization ratio deviation data includes the difference between the actual ratio and the synchronization ratio caused by mechanical characteristics or slight slippage. The correction signal is a compensation amount for the motor speed. Using the synchronization ratio deviation data and corresponding correction signal data, a machine learning algorithm, such as a neural network, is used to train a feedforward calculation model. The deviation is used as input, and the correction amount as the desired output to train the feedforward calculation model, enabling it to learn how to generate appropriate correction control signals based on the synchronization ratio deviation. This feedforward calculation model can predict that, given changes in linear velocity and roll diameter, the motor angular velocity needs to be adjusted to maintain constant tension. Using the feedforward calculation model, based on the relationship between linear velocity and roll diameter, a second control information is calculated to compensate for tension disturbances caused by changes in roll diameter. This second control information is used to adjust the motor angular velocity, thereby compensating for the tension disturbances caused by changes in roll diameter.

[0041] By acquiring roll diameter signals in real time and combining them with linear velocity information, a second control signal is generated using a feedforward calculation model. This allows for proactive compensation for tension disturbances caused by roll diameter changes. Compared to feedback control alone, feedforward control can respond more promptly to roll diameter changes, reduce tension fluctuations, and improve the accuracy and stability of tension control. This ensures that the napping machine maintains constant fabric tension under different roll diameter variations, thereby improving the quality and consistency of the napping process.

[0042] 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.

[0043] Specifically, the first control signal generated based on a model-free adaptive control algorithm and the second control information based on the roll diameter change are integrated. For example, they are weighted and fused according to their characteristics and their influence weights on the control of the grinding machine. Feedforward control can compensate for foreseeable disturbances such as roll diameter changes in advance, while feedback control can correct for unforeseen disturbances and model errors. Based on expert experience or optimization algorithms, such as genetic algorithms, the coefficient values ​​are iteratively adjusted to minimize tension fluctuation indicators, such as the variance of tension deviation, to find the optimal combination of weighting coefficients and set appropriate weighting coefficients w1 and w2. Simultaneously, during the integration process, the effectiveness and rationality of the two control signals are monitored in real time. If one signal is abnormal, such as exceeding the preset reasonable range, the weighting coefficients are automatically adjusted to reduce the influence weight of the abnormal signal and ensure 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 and second control signals, respectively. After receiving the master control signal, it is output to the drive unit of the feed motor and / or output motor of the napping machine. The drive unit uses a frequency converter or servo driver, which can convert the master control signal into a corresponding motor speed adjustment command. For example, for a frequency converter, the master control signal, after digital-to-analog conversion, serves as the frequency setpoint signal for the frequency converter. The frequency converter adjusts the frequency of the output power supply according to this signal, thereby changing the motor speed. For a servo driver, the master control signal is used to set parameters such as the target speed and torque of the motor. The servo driver precisely adjusts the motor operation through closed-loop control. For example, if the roll diameter changes significantly during the unwinding stage of the napping process, while the winding stage remains relatively stable: the initial roll diameter of the unwinding roller is 800mm, and as the fabric is continuously unwound, the roll diameter rapidly decreases to 600mm. After analysis and processing of the master control signal generated by the feedforward calculation model and feedback control, it is determined that the change in unwinding speed mainly affects the tension. The master control signal is then output to the motor drive unit, such as a frequency converter. The inverter is initially set to a frequency of 50Hz. Taking a 4-pole motor as an example, this corresponds to a feed motor speed of 1440 r / min. After conversion, the main control signal requires the inverter to adjust the frequency to 52Hz. The inverter adjusts the output power frequency according to the main control signal, increasing the feed motor speed to 1497.6 r / min, with speed approximately proportional to frequency. By increasing the feed motor speed, the reduction in fabric tension caused by the decrease in the unwinding roller diameter is compensated for, maintaining stable fabric tension in the napped area. If the unwinding roller diameter decreases from 900mm to 700mm, while the take-up roller diameter increases from 600mm to 800mm, the main control signal, after comprehensive judgment, outputs adjustment commands to the drive units of the feed and output motors respectively.For the feed motor inverter, the frequency was adjusted from 48Hz to 50Hz, increasing the feed motor speed from 1382.4 r / min to 1440 r / min. For the output motor servo drive, the speed was adjusted from 1100 r / min to 1050 r / min. By simultaneously adjusting the speeds of the feed and output motors, the unwinding and winding speeds of the fabric are controlled in a coordinated manner, precisely maintaining the tension stability of the fabric during the napping process.

[0044] By integrating the first and second control signals to obtain the total control signal and outputting it to adjust the motor speed, the advantages of feedforward control and feedback control can be fully utilized. Feedforward control compensates for known disturbances such as changes in roll diameter in advance, while feedback control corrects unforeseen disturbances and model errors in a timely manner. The combination of the two enables the system to respond more comprehensively and quickly to various factors affecting tension, effectively reducing tension fluctuations, improving the accuracy and stability of tension control in the napping machine, and ensuring that the fabric maintains constant tension throughout the napping process, thereby improving napping quality and product consistency.

[0045] Furthermore, obtaining the total control signal also includes: acquiring the speed signal fed back by the motor encoder and the torque current signal fed back by the driver; estimating the total disturbance based on the speed signal and the torque current signal using a disturbance observer, and generating a third control signal to counteract the total disturbance; and generating the total control signal based on the first control signal, the second control signal, and the third control signal.

[0046] Specifically, to further improve the accuracy and anti-interference capability of tension control, a high-precision motor encoder is used to acquire the motor speed signal in real time. The motor encoder can be either incremental or absolute. Incremental encoders represent the motor speed and direction of rotation by outputting pulse signals, with the output pulse frequency proportional to the motor speed. Absolute encoders provide a unique digital code for each position, directly acquiring the absolute position and speed information of the motor. Simultaneously, torque current signals are acquired from the motor driver, which accurately measures the motor's torque current using a current sensor. Then, the acquired speed and torque current signals are input into a disturbance observer, a model-based control strategy. Based on the motor's dynamic model and the input speed and torque current signals, the disturbance observer estimates the total disturbance experienced by the motor during operation. By comparing the actual output with the model's predicted output, a third control signal is generated. Finally, the first control signal generated based on a model-free adaptive control algorithm, the second control signal generated through a feedforward calculation model, and the third control signal generated by the disturbance observer are combined to generate the final control signal. The comprehensive method can employ weighted summation, assigning different weights to each control signal based on its importance and effectiveness to calculate the total control signal. This total control signal is then output to the drive unit of the feed motor and / or output motor of the napping machine to adjust the motor speed.

[0047] By collecting motor speed and torque current signals and using a disturbance observer to estimate the total disturbance to generate a third control signal, which is then combined with the first and second control signals to generate a total control signal, the accuracy and stability of tension control can be further improved. This ensures that the napping machine can achieve constant fabric tension control under different working conditions, thereby improving napping quality and production efficiency.

[0048] Furthermore, 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. This includes: constructing a disturbance-free inverse model based on the driving principle of the driver and motor; inputting the torque current signal into the disturbance-free inverse model for forward estimation to obtain the ideal motor speed, and comparing it with the speed signal to obtain the actual speed deviation; inputting the actual speed deviation into the disturbance-free inverse model to calculate the disturbance in reverse, and obtaining the estimated torque of the total disturbance; and generating the third control signal based on the estimated torque of the total disturbance.

[0049] Specifically, the core of the disturbance observer is to construct a disturbance-free inverse model based on the driving principles of the actuator and motor. The disturbance-free inverse model is Gn(s) / Jn∗s, where Gn(s) is the nominal transfer function of the motor, reflecting the input-output relationship under ideal conditions; Jn is the nominal moment of inertia of the motor, a 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, unloaded, frictionless, and precisely parameterized conditions. Through the disturbance-free inverse model, the speed that the motor should reach under ideal disturbance-free conditions is output. The collected torque and current signals are input into the disturbance-free inverse model for forward estimation to obtain the ideal motor speed under ideal disturbance-free conditions. Then, the ideal motor speed obtained from the forward estimation is compared with the actually measured speed signal to calculate the actual speed deviation. The actual speed deviation reflects the impact of disturbances on the motor speed. The calculated actual speed deviation is input into the disturbance-free inverse model for reverse disturbance calculation, i.e., multiplied by Jn*s, to obtain the total disturbance estimated torque. The total disturbance estimated torque is an estimate of the actual disturbance torque, integrating the influence of various unknown disturbances on the motor torque. Finally, a third control signal is generated based on the total disturbance estimated torque. The third control signal is a control quantity used to counteract the disturbance effect; its magnitude is equal to and its direction is opposite to the disturbance torque, to achieve complete disturbance compensation. 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 counteract the disturbance effect.

[0050] By constructing a disturbance-free inverse model, the total disturbance is estimated using torque current signals and actual speed signals, and a third control signal is generated. This enables real-time and accurate compensation for various unknown disturbances encountered during motor operation, further improving the performance of the disturbance observer, enhancing the stability and accuracy of tension control, ensuring that the brushing machine maintains constant fabric tension under various disturbances, and improving product quality and production efficiency.

[0051] Furthermore, after outputting the total control signal to the drive device of the feed motor and / or output motor of the napping machine and adjusting the motor speed, the method further includes: real-time monitoring of the change in tension deviation; when the change in tension deviation exceeds a first preset threshold within a unit time, determining that a sudden disturbance event has occurred 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.

[0052] Specifically, after outputting the main control signal to the drive unit of the feed motor and / or output motor of the napping machine to adjust the motor speed, a tension sensor is used to monitor the tension deviation of the fabric in real time and calculate the change in tension deviation per unit time. When the change in tension deviation per unit time exceeds a first preset threshold, a sudden disturbance event is determined to have occurred. The first preset threshold is derived by comprehensively considering factors such as the normal operating tension range of the napping machine, fabric characteristics, and historical data. For example, through experiments and simulations, the variation law of tension deviation under different disturbance conditions is analyzed to determine the first preset threshold, ensuring accurate identification of sudden disturbance events. A sudden disturbance event, such as the passage of a fabric splice, will cause a huge impact on the tension instantly due to the completely different thickness and elasticity of the splice compared to normal fabric, resulting in a sharp change in tension deviation. When a sudden disturbance event is determined, a compliant control mode is immediately triggered. In the compliant control mode, the control parameters of the napping machine are temporarily switched to a preset compliant control parameter set. The compliant control parameter set is configured in advance by experts in the field through extensive testing, and its function is to reduce the control response speed and control gain. For example, reducing the control gain can decrease the sensitivity of the sanding machine control to tension deviation, preventing overreaction and larger tension fluctuations under sudden disturbances. Reducing the response speed allows the sanding machine to adjust more smoothly when faced with sudden disturbances, reducing oscillations. After the sudden disturbance event ends, the change in tension deviation is monitored to determine if the disturbance has been eliminated. For example, when the change in tension deviation is less than a small threshold over multiple consecutive sampling periods, the sudden disturbance event is considered to have ended. At this point, the system automatically reverts to normal control mode and continues to use the control parameters of the total control signal for tension control.

[0053] By monitoring tension deviation changes in real time and setting a compliant control mode, the control strategy can be adjusted in a timely manner when encountering sudden disturbances such as fabric splices passing through, avoiding tension loss due to overreaction of the napping machine. This improves the adaptability and stability of the napping machine's tension control under complex working conditions, and can also effectively reduce fabric quality problems and equipment failures caused by sudden disturbances, ensuring the normal operation of the napping machine and the quality of fabric products.

[0054] Example 2 is based on the same inventive concept as the tension adaptive adjustment-based brushing machine control method in the foregoing examples, such as... Figure 2 As shown, this application provides a brushing machine control system based on adaptive tension adjustment, wherein the brushing machine control system based on adaptive tension adjustment includes:

[0055] The data acquisition module 11 is used to acquire the actual tension value of the fabric in real time through tension detection devices installed 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 the rate of change of tension deviation. The first control signal generation module 12 is used to generate a first control signal based on the real-time tension deviation and the rate of change of tension deviation using a model-free adaptive control algorithm. The second control signal generation module 13 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 the tension disturbance caused by the change of roll diameter through a feedforward calculation model based on the roll diameter signals and the current fabric linear speed. The motor control module 14 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 devices of the infeed motor and the outfeed motor of the napping machine to adjust the motor speed.

[0056] Furthermore, the first control signal generation module 12 in the aforementioned tension adaptive adjustment-based grinding machine control system is also used to: estimate the relationship between the control signal and the tension using a model-free adaptive control algorithm, and establish a pseudo-Jacobi matrix; based on the pseudo-Jacobi matrix, generate the first control signal according to the tension deviation and the rate of change of the tension deviation.

[0057] Furthermore, in the aforementioned tension adaptive adjustment-based grinding machine control system, the first control signal generation module 12 is also used to generate the following expression for the first control signal: ;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.

[0058] Furthermore, in the aforementioned tension adaptive adjustment-based grinding machine control system, the motor control module 14 is also used to: acquire the speed signal fed back by the motor encoder and the torque current signal fed back by the driver; estimate the total disturbance based on the speed signal and the torque current signal using a disturbance observer, and generate a third control signal to counteract the total disturbance; and generate the total control signal based on the first control signal, the second control signal, and the third control signal.

[0059] Furthermore, in the aforementioned tension adaptive adjustment-based grinding machine control system, the motor control module 14 is also used for: 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 the ideal motor speed, and comparing it with the speed signal to obtain the actual speed deviation; inputting the actual speed deviation into the disturbance-free inverse model to calculate the disturbance in reverse, and obtaining the total disturbance estimated torque; and generating the third control signal based on the total disturbance estimated torque.

[0060] Furthermore, in the aforementioned tension adaptive adjustment-based grinding machine control system, the motor control module 14 is also used to: monitor the change state of tension deviation in real time; when the change in tension deviation exceeds a first preset threshold within a unit time, determine that a sudden disturbance event has occurred and immediately trigger the compliant control mode; in the compliant control mode, temporarily switch to a preset compliant control parameter set; and after the sudden disturbance event ends, return to the normal control mode.

[0061] Furthermore, the second control signal generation module 13 in the aforementioned tension adaptive adjustment-based napping machine control system is also used to: retrieve the roll diameter test data of the unwinding roller and the take-up roller, as well as the corresponding fabric linear speed test data, using a preset target tension value as a retrieval factor, 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 roll diameter signal into the feedforward calculation model to generate the second control signal.

[0062] Furthermore, the data acquisition module 11 in the aforementioned tension adaptive adjustment-based napping machine control system is also used to: construct 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 optimized control parameter set has a preset tension value label; receive input current fabric material information, call the optimized control parameters matching the current fabric material from the expert rule base, perform initialization control of the napping machine, and extract the corresponding preset tension value label to generate a preset target tension value.

[0063] Furthermore, in the aforementioned tension adaptive adjustment-based grinding machine control system, the data acquisition module 11 is also used to: initialize the pseudo-Jacobi matrix using the optimized control parameters as the initial parameters of the model-free adaptive control algorithm.

[0064] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1The tension-adaptive adjustment-based brushing machine control method and specific example in Embodiment 1 are also applicable to the tension-adaptive adjustment-based brushing machine control system in this embodiment. Through the foregoing detailed description of the tension-adaptive adjustment-based brushing machine control method, those skilled in the art can clearly understand the tension-adaptive adjustment-based brushing machine control system in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0065] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not 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.

[0066] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A control method of a napping machine based on tension self-adaptation, characterized in that, The application relates to a real-time tension control method for a sanding machine. The method comprises the following steps: a tension detection device is arranged at the cloth feeding end and the cloth discharging end of the sanding machine to obtain the actual tension value of the fabric in real time, compare the actual tension value with a preset target tension value, and obtain a real-time tension deviation and a tension deviation change rate; a 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; a real-time roll diameter signal of the unwinding roller and the winding roller of the sanding machine is collected, and a 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 signal 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 cloth feeding motor and / or the cloth discharging motor of the sanding machine to adjust the motor rotating speed; the first control signal is generated according to the tension deviation and the tension deviation change rate, and the method comprises the following steps: a model-free adaptive control algorithm is adopted to estimate the relationship between the control signal and the tension and establish a pseudo-Jacobian matrix; the first control signal is generated according to the tension deviation and the tension deviation change rate based on the pseudo-Jacobian matrix; ； wherein, with is a control gain for adjusting the strength 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 the current time; is a real-time tension deviation change rate at the current time.

2. The control method of the fuzzing machine based on tension self-adaptive adjustment according to claim 1, characterized in that, the expression of the first control signal is as follows: the total control signal can also be obtained in the following way: a rotating speed signal fed back by a motor encoder and a torque current signal fed back by a driver are collected; a total disturbance is estimated through a disturbance observer according to the rotating speed signal and the torque current signal, and a third control signal for offsetting the total disturbance is generated; 3. The control method of a napping machine based on tension self-adaptive adjustment according to claim 2, characterized in that, the total control signal is generated according to the first control signal, the second control signal and the third control signal. the third control signal is generated through a disturbance observer according to the rotating speed signal and the torque current signal, and the method comprises the following steps: a non-disturbance inverse model is constructed based on the driving principle of the driver and the motor; the torque current signal is input to the non-disturbance inverse model for forward estimation to obtain an ideal motor rotating speed, and the ideal motor rotating speed is compared with the rotating speed signal to obtain an actual rotating speed deviation; the actual rotating speed deviation is input to the non-disturbance inverse model for reverse calculation of the disturbance to obtain a total disturbance estimated torque; 4. The control method of the fuzzing machine based on tension self-adaptive adjustment according to claim 1, characterized in that, the third control signal is generated based on the total disturbance estimated torque. after the total control signal is output to the driving device of the cloth feeding motor and / or the cloth discharging motor of the sanding machine to adjust the motor rotating speed, the following steps are further included: the change state of the tension deviation is monitored in real time, when the change amount of the tension deviation within a unit time exceeds a first preset threshold value, it is determined that a sudden disturbance event occurs, and a compliant control mode is triggered immediately; in the compliant control mode, a preset compliant control parameter set is temporarily switched to; 5. A control method of a napping machine based on tension self-adaptive adjustment according to claim 4, characterized in that, and after the sudden disturbance event ends, the normal control mode is restored. the real-time roll diameter signal of the unwinding roller and the winding roller of the sanding machine is collected, and a 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 signal and the current fabric linear speed, and the method comprises the following steps: the roll diameter test data and the corresponding fabric linear speed test data of the unwinding roller and the winding roller are retrieved with a preset target tension value as a retrieval factor to construct a synchronous ratio relationship; The feedforward calculation model is trained based on the synchronous ratio deviation data and corresponding correction signals collected according to the synchronous ratio relationship; The real-time winding diameter signal is input into the feedforward calculation model to generate the second control signal.

6. The control method of a napping machine based on tension self-adaptive adjustment according to claim 1, characterized in that, The determination of the preset target tension value comprises: An expert rule base is constructed, which 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.

7. A control method of a napping machine based on tension self-adaptive adjustment according to claim 6, characterized in that, The optimized control parameter is used as the initial parameter of the model-free adaptive control algorithm to initialize the pseudo-Jacobian matrix.

8. A control system for a napping machine based on tension self-adaptation, characterized in that, The steps of the sanding machine control method based on tension adaptive adjustment according to any one of claims 1-7 comprise: A data acquisition module is configured to acquire 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, compare the actual tension value with the preset target tension value, and obtain the real-time tension deviation and the tension deviation change rate; A first control signal generation module is configured to generate 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 is configured to acquire real-time winding diameter signals of the unwinding roller and the winding roller of the sanding machine, and generate a second control signal for compensating the tension disturbance caused by the winding diameter change by using a feedforward calculation model according to the winding diameter signals and the current fabric linear speed; A motor control module 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 the driving device of the fabric inlet motor and the fabric outlet motor of the sanding machine to adjust the motor speed.

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