Fully Automatic Control Method and System for Pre-processing Flat-panel Integrated Machine

Abstract

This application discloses a fully automatic control method and system for a pre-processing flat-width integrated machine, relating to the field of integrated machine automatic control. First, it acquires real-time data on the fabric's liquid content, temperature, and operating status to construct a fabric state feature vector. Then, it uses a rheological model to analyze this vector and accurately estimate the dynamic elastic modulus of the fabric under humid and hot conditions. Based on this, it dynamically calculates the optimal tension reference value adapted to the current physical state, replacing the traditional constant tension setting, and combines a feedforward and impedance composite control algorithm to solve the motor torque command. By sensing changes in fabric properties in real time and predictively adjusting the tension threshold, it solves the dynamic tension mismatch problem caused by feedback lag and a single model in existing technologies, effectively avoiding the formation of dead creases in high-density fabrics during processing.

Description

Technical Field

[0001] This application relates to the field of automatic control of integrated machines, and more specifically, to a fully automatic control method and system for a pre-processing flat-panel integrated machine. Background Technology

[0002] Pre-treatment integrated flat-width machines play a crucial role in the textile printing and dyeing industry. They are primarily used for continuous processing of woven fabrics, including desizing, scouring, and bleaching, to remove impurities such as sizing agents and grease, thereby improving wicking properties and whiteness. In this process, especially with high-density woven fabrics (such as high-density polyester imitation silk), ensuring fabric flatness and preventing wrinkles and physical damage during high-speed transport are core indicators of equipment control performance. Therefore, developing a fully automated control scheme for pre-treatment integrated flat-width machines that can adapt to complex working conditions has significant industrial value in improving the first-pass yield and finished product quality of textiles.

[0003] However, existing fully automatic control schemes for pretreatment integrated machines typically employ a relatively simple control model, generally simplifying the fabric as a rigid or linear elastomer and using a constant tension reference value for control throughout the entire process. This traditional approach relies primarily on tension sensors for hysteretic feedback adjustment; that is, the system only passively adjusts the motor torque when the sensor detects a significant deviation between the actual tension and the set value. This control strategy has serious limitations when processing high-density fabrics: when the fabric enters the high-temperature alkaline environment of the pretreatment process from a "dry" state, the fibers rapidly absorb moisture and swell, and under high temperature, they are in a high-energy state, causing drastic nonlinear changes in their physical modulus and viscoelasticity. Due to the lack of sensing and feedforward mechanisms for the fabric's current liquid content and temperature state, existing technologies cannot predict the softening or stiffness changes caused by moisture absorption and temperature rise. This results in the control system still applying a mismatched tension threshold to the fabric, whose properties have already changed. This dynamic tension mismatch often leads to micro-creases in the fabric before the system can react, which are then crushed by subsequent rolling mills, forming irreversible dead creases that severely affect product quality. Summary of the Invention

[0004] To address the aforementioned problems, according to one aspect of this application, a fully automatic control method for a pre-treatment flat-width integrated machine is provided, comprising: acquiring an original operating data packet, the original operating data packet including the actual tension value at the current moment, the actual tension value at the previous moment, the motor angular velocity, the current transmission linear velocity of the fabric, the current liquid carrying rate of the fabric, and the real-time temperature of the processing tank; performing fabric state feature vectorization on the original operating data packet to obtain a fabric state feature vector; performing dynamic modulus estimation on the fabric state feature vector based on a rheological model to obtain a dynamic elastic modulus parameter; determining an optimal tension reference value based on the dynamic elastic modulus parameter and the work order ID; and performing feedforward and impedance composite control quantity calculation on the optimal tension reference value and the original operating data packet to obtain a motor torque control command.

[0005] According to another aspect of this application, a fully automatic control system for a pre-processing flat-width integrated machine is provided, comprising: a raw operating data packet acquisition module for acquiring raw operating data packets, the raw operating data packets including the actual tension value at the current moment, the actual tension value at the previous moment, the motor angular velocity, the current transmission linear velocity of the fabric, the current liquid carrying rate of the fabric, and the real-time temperature of the processing tank; a fabric state feature extraction module for vectorizing the raw operating data packets into fabric state features to obtain fabric state feature vectors; an elastic modulus parameter calculation module for estimating the dynamic modulus of the fabric state feature vectors based on a rheological model to obtain dynamic elastic modulus parameters; an optimal tension reference value determination module for determining the optimal tension reference value based on the dynamic elastic modulus parameters and the work order ID; and a control command generation module for performing feedforward and impedance composite control quantity calculation on the optimal tension reference value and the raw operating data packets to obtain motor torque control commands.

[0006] Compared with existing technologies, this application provides a fully automatic control method and system for a pre-processing flat-width integrated machine, addressing the problem of tension mismatch and creases caused by changes in modulus due to fabric moisture absorption and heat in the background technology. First, it acquires real-time data on the fabric's liquid content, temperature, and operating status to construct a fabric state feature vector. Then, it uses a rheological model to analyze this vector, accurately estimating the dynamic elastic modulus of the fabric under humid and hot conditions, thereby quantifying fiber swelling and changes in physical properties under high-energy states. Based on this, it dynamically calculates the optimal tension reference value adapted to the current physical state, replacing the traditional constant tension setting, and combines a feedforward and impedance composite control algorithm to calculate the motor torque command. By sensing changes in fabric properties in real time and predictively adjusting the tension threshold, it solves the dynamic tension mismatch problem caused by feedback lag and a single model in existing technologies, effectively avoiding the generation of dead creases in high-density fabrics during processing. Attached Figure Description

[0007] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0008] Figure 1 This is a flowchart of a fully automatic control method for a pre-processing flat-panel integrated machine according to an embodiment of this application.

[0009] Figure 2 This is a schematic diagram of the data flow in a fully automatic control method for a pre-processing flat-panel integrated machine according to an embodiment of this application.

[0010] Figure 3 This is a flowchart of step S3 in the fully automatic control method of the pre-processing flat-panel integrated machine according to an embodiment of this application.

[0011] Figure 4 This is a schematic diagram of the data flow in step S5 of the fully automatic control method of the pre-processing flat-panel integrated machine according to an embodiment of this application.

[0012] Figure 5 This is a block diagram of a fully automatic control system for a pre-processing flat-panel integrated machine according to an embodiment of this application. Detailed Implementation

[0013] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0014] In response to the problems mentioned in the background description, this application proposes a fully automatic control method for a pre-processing flat-panel integrated machine. Figure 1 This is a flowchart of a fully automatic control method for a pre-processing flat-panel integrated machine according to an embodiment of this application. Figure 2 This is a schematic diagram of the data flow in a fully automatic control method for a pre-processing flat-panel integrated machine according to an embodiment of this application. Figure 1 and Figure 2As shown, the fully automatic control method of the pretreatment flat-width integrated machine according to the embodiment of this application includes: S1, acquiring the original operation data packet, which includes the actual tension value at the current moment, the actual tension value at the previous moment, the motor angular velocity, the current transmission linear velocity of the fabric, the current liquid carrying rate of the fabric, and the real-time temperature of the processing tank; S2, performing fabric state feature vectorization on the original operation data packet to obtain the fabric state feature vector; S3, performing dynamic modulus estimation based on a rheological model on the fabric state feature vector to obtain the dynamic elastic modulus parameter; S4, determining the optimal tension reference value based on the dynamic elastic modulus parameter and the work order ID; S5, performing feedforward and impedance composite control quantity calculation on the optimal tension reference value and the original operation data packet to obtain the motor torque control command.

[0015] In S1, the raw operating data package is acquired. This package includes the current actual tension value, the previous actual tension value, the motor angular velocity, the current fabric transmission linear velocity, the current fabric liquid load, and the real-time temperature of the processing tank. It is understandable that during the continuous pretreatment process of high-density woven fabrics, the fabric transitions from a dry, room-temperature state to a high-temperature alkaline environment. The internal structure of the fibers undergoes significant physical changes due to moisture absorption and heat, particularly the intensified movement of polymer chain segments in a high-energy state. This results in highly nonlinear and time-varying characteristics in the fabric's viscoelasticity and Young's modulus. Traditional control strategies often neglect the real-time impact of these environmental factors on the fabric's mechanical properties, relying solely on fixed mechanical parameters for open-loop or delayed closed-loop control, which is insufficient to adapt to the actual tension requirements of the fabric under different temperature and humidity conditions. In order to accurately capture the rheological state of the fabric in this dynamic process and provide accurate physical boundary conditions and input variables for subsequent dynamic modulus estimation based on the rheological model, the original running data package is acquired to construct a multi-dimensional data snapshot covering the fabric's stress state, motion state, and thermodynamic and humidity environment, thereby providing a digital representation of the fabric's current physical properties for the fully automatic control system.

[0016] In one feasible embodiment of this application, the specific process of S1 is as follows: This process is achieved through the collaborative work of the sensor network integrated into the key functional units of the pre-processing flat-panel machine and the central control unit. The original running data packet is a structured data set, whose core components include the actual tension value at the current moment, the actual tension value at the previous moment, the motor angular velocity, the current transmission linear velocity of the fabric, the current liquid content of the fabric, and the real-time temperature of the processing tank. These data are synchronously collected and encapsulated through a bus communication protocol to ensure data consistency in the time domain.

[0017] Specifically, the current tension value is acquired by a precision tension sensor installed below the bearing housing of the guide roller. This tension sensor employs a resistance strain gauge structure. When the fabric wraps around the guide roller with a certain tension, the resultant force on the guide roller acts on the sensor's elastic body, causing a change in the strain gauge's resistance, which in turn converts the physical pressure into a millivolt-level electrical signal. The signal transmitter amplifies and conditions this weak signal into a standard analog signal, such as 4-20mA or 0-10V, and then performs analog-to-digital conversion via the analog input module before entering the control system. The control system reads this value at a set high-frequency sampling period, such as once every 10 milliseconds, and after removing high-frequency noise caused by mechanical vibration through a low-pass filtering algorithm, assigns it as the current tension value. The previous tension value is a historical state variable maintained in the control system's memory, and its value comes from the tension data recorded in the previous control cycle. At the beginning of each new control cycle, the system updates the current value stored in the register to the previous value through a shift operation, and then writes the new acquired data as the latest current value. For example, if the tension detected in the current control cycle is 250.0 Newtons, and the tension of the previous cycle stored in the register is 249.5 Newtons, these two values ​​together constitute the differential basis of the tension change. The acquisition of the motor angular velocity relies on an absolute encoder installed at the tail of the main drive motor. The encoder uses photoelectric conversion to convert the rotation angle of the motor shaft into pulse signals or digital communication messages in real time. The driver calculates the instantaneous speed of the motor by parsing the position information fed back by the encoder. This speed is transmitted to the central controller in the form of angular velocity (unit: radians / second) via real-time industrial Ethernet such as EtherCAT or Profinet. For example, when the motor is running at 1500 rpm, the corresponding angular velocity value can be obtained after conversion. This parameter directly reflects the power output state of the drive unit and is an important basis for calculating the inertial torque component. The current transmission linear velocity of the fabric refers to the actual speed at which the fabric moves along the transmission direction in a flat state. While this value can be theoretically calculated using the motor angular velocity and the diameter of the drive roller, high-precision control employs independent speed encoders or laser velocimeters to directly measure the fabric surface speed, or performs high-precision conversion using the linear velocity of the main drive roller. This data reflects the fabric's production efficiency and transport status; for example, the linear velocity might be 80 meters per minute. Combined with the motor angular velocity, this parameter can be used to monitor for fabric slippage and serves as a reference for time-related terms in subsequent rheological models. The current liquid content of the fabric is a key physical quantity characterizing its moisture absorption, directly related to the moisture influence factor in the rheological model. During pretreatment, after passing through the impregnation tank and rolling mill, a certain amount of chemical solution adheres to the fiber interior and surface of the fabric. This parameter is obtained using a non-contact microwave moisture meter or an online isotope thickness gauge located at the rolling mill exit.A microwave moisture meter utilizes the absorption characteristics of water molecules to microwaves of a specific frequency to measure the amount of microwave energy attenuation after penetrating the fabric, thereby accurately calculating the moisture content per unit area of ​​the fabric. This value is expressed as a percentage, for example, 85%, meaning that the liquid mass carried by the fabric accounts for 85% of the dry fabric mass. If direct measurement is not possible due to site limitations, soft measurement calculations can be performed based on the set pressure of the rolling mill, the fabric weight, and the viscosity of the agent using a preset empirical model. However, in this embodiment, the actual sensor measurement value is preferred to ensure accuracy. The real-time temperature of the treatment tank refers to the temperature of the chemical reaction environment in which the fabric is currently located. This parameter determines the intensity of thermal motion of the fiber polymer chain segments and is the core input for calculating the temperature influence factor in the rheological model. This data is acquired through a resistance temperature sensor installed at the bottom of the treatment tank (such as a desizing tank or scouring tank). The sensor probe directly contacts the tank liquid, senses the liquid heat energy and converts it into a change in resistance value, which is then linearized by a temperature transmitter and transmitted to the controller. Since the pretreatment process is carried out at high temperatures, such as 95°C to 98°C, this real-time temperature data can accurately reflect the thermal history of the fabric after being heated.

[0018] In the specific implementation architecture, the central controller (such as a high-performance PLC) allocates a dedicated data block to store the aforementioned raw operational data packets. For example, at a sampling time t, the controller first triggers the data refresh of each I / O channel, reading a value of 250.0 N from the tension sensor, 249.5 N from the history register, the motor angular velocity of 10 rad / s from the inverter communication message, the linear velocity of 60 m / min from the speed measurement module, the liquid content of 0.9 (i.e., 90%) from the microwave moisture meter, and the tank temperature of 98℃ from the temperature transmitter. These heterogeneous data are aligned and encapsulated according to a predefined data structure, and the current timestamp and work order ID are appended to form a complete raw operational data packet.

[0019] In S2, the original running data package is vectorized into fabric state features to obtain a fabric state feature vector. Specifically, the fabric state feature vector includes work order ID, fabric type ID, linear velocity, temperature, and liquid carryover rate. Correspondingly, physical sensors, constrained by mechanical structure and process layout, are inevitably dispersed and installed in different locations on the equipment. For example, the temperature sensor is located at the bottom of the impregnation tank, while the liquid carryover rate detection device is located at the rolling mill exit, and the tension sensor is distributed at the guide roller. This means that the parameters in the original running data package collected at the same time t actually reflect the state of the fabric at different physical locations; that is, the tank temperature corresponds to the fabric in the impregnation zone, while the liquid carryover rate corresponds to the fabric just leaving the rolling mill. If these spatially misaligned data are directly used for subsequent rheological model calculations, the input physical boundary conditions will be misaligned in time and space, failing to truly reflect the comprehensive physical properties of the currently controlled fabric micro-element, thus causing the calculated elastic modulus to deviate from the actual situation. Furthermore, the original data package only contains physical quantities and lacks semantic information related to work orders and fabric types, which are the index keys for calling specific rheological model parameter sets. Therefore, implementing fabric state feature vectorization eliminates the phase difference caused by sensor distribution through a spatiotemporal alignment algorithm, and deeply integrates discrete physical signals with production semantic information to construct a standardized mathematical feature vector that can accurately describe the full-dimensional physical state of the current controlled fabric micro-element, providing rigorous data input for subsequent high-precision dynamic modulus estimation.

[0020] In one feasible embodiment of this application, the specific process of S2 is as follows: In the previous step, a raw operation data packet containing the current actual tension value (e.g., 250.0 N), motor angular velocity (e.g., 10 rad / s), transmission linear velocity (e.g., 60 m / min), liquid carryover rate (e.g., 90%), and tank temperature (e.g., 98°C) has been output. This data packet is first received, and the first stage of semantic enhancement processing is initiated. In the semantic enhancement stage, the processing unit uses the timestamp or batch tracking signal in the raw data packet to initiate an index query to the local cache mirror of the production management database, such as the MES system. The purpose of this operation is to obtain the identification information of the fabric currently being processed. Based on the query results, the work order ID and fabric type ID are extracted. For example, the work order ID returned by the query is ORD-202510-A05, and the fabric type ID is PET-HighDensity-75D. These two ID parameters are crucial because the work order ID is associated with specific process objectives such as target tension and target whiteness, while the fabric type ID directly points to the specific rheological model parameter set that needs to be loaded in subsequent steps, such as the dry modulus reference of high-density polyester fabric. These two semantic identifiers are temporarily stored in the feature construction buffer, waiting to be combined with physical parameters.

[0021] Subsequently, the most critical spatiotemporal alignment and process segment mapping calculations are performed. Since the fabric is transported in the machine as a continuous bundle of filaments, an integration-based virtual position tracking model needs to be constructed to eliminate data lag caused by the physical installation distance of the sensors. Let point A be the installation location of the temperature sensor, point B be the liquid content detection location, and point C be the tension control point along the fabric transport direction. The processing unit's memory pre-sets the physical path lengths between each key point, for example, L{AB} is 5 meters and L{BC} is 2 meters. To obtain the true state characteristics of the fabric at control point C, the processing unit integrates the current transport linear velocity in real time. To track the motion trajectory of the fabric micro-elements. The integral formula is as follows: ,in, This represents the cumulative displacement of the fabric element at time t. It is the instantaneous linear velocity that varies with time. Based on this integral model, the processing unit manages historical data through a first-in, first-out (FIFO) circular data queue. The sensor records the real-time temperature as the fabric micro-element passes through the impregnation tank at point A. A location tag of 98℃ was attached and added to the temperature data queue. As the fabric moves at a speed of 60 m / min (1 m / s), this micro-element requires a 5-second time delay to reach point B at the rolling mill exit. At this point, the liquid carryover sensor measures the liquid carryover rate of this micro-element. 0.9. The processing unit retrieves the temperature record of the same fabric micro-element in the impregnation tank from the temperature queue 5 seconds ago, or calculates the equivalent temperature of the micro-element when it reaches point C based on the thermodynamic decay model and the current ambient temperature.

[0022] In a preferred embodiment, to adapt to the special pretreatment conditions—that is, once the fabric absorbs moisture and is heated, its internal chemical reaction continues for a certain period of time—the processing unit employs a process segment mapping method. The processing unit calculates, based on the linear velocity integral, that the currently controlled fabric micro-element is in the high-temperature alkaline reaction segment. Under this segment definition, the processing unit calculates the real-time temperature of the tank. This is considered the environmental boundary temperature of this section, and it is assumed that the fabrics within this section have reached thermal equilibrium. Therefore, the current temperature is directly adopted. =98℃ is used as the characteristic temperature. Meanwhile, regarding the liquid carryover rate, since the rolling mill is located at the entrance or exit of this section, the processing unit will provide the latest... =0.9 maps to the saturated moisture content of the fabric within that section. This processing method effectively avoids complex fluid dynamics calculations while ensuring data timeliness, meeting the requirements of real-time control in industrial settings.

[0023] After completing the data alignment and mapping, the processing unit enters the feature vector assembly stage. At this point, the processing unit gathers all the data elements that have been acquired and verified: the work order ID, denoted as... ORD-202510-A05, Fabric Type ID, denoted as : PET-HighDensity-75D, current linear velocity after filtering 60 m / min, mapped process temperature 98℃ and measured liquid carryover rate 0.9. The processing unit standardizes these heterogeneous data into a high-dimensional mathematical vector, namely the fabric state feature vector, according to a predefined data structure protocol. This vector can be represented as: Substituting specific values, the vector's representation in memory is as follows: The entire vectorization process is executed cyclically within millisecond-level control cycles, ensuring that the control system always makes decisions based on the latest, spatiotemporally calibrated, real physical state of the fabric, thereby achieving an effective transformation from raw sensor data to high-level control characteristics.

[0024] In S3, the dynamic modulus of the fabric's state feature vector is estimated based on a rheological model to obtain the dynamic elastic modulus parameters. It is understandable that fabrics, as typical viscoelastic polymer materials, do not have static mechanical properties but exhibit significant dynamic evolution characteristics with changes in the processing environment. Especially for high-density woven fabrics, when they enter a high-temperature chemical impregnation zone from a dry, unwound state, the macromolecular chains of the fibers undergo untangling and slippage under the combined effects of heat and water molecules, leading to a significant decrease in macroscopic tensile stiffness. If the control strategy ignores this real-time decay of material properties and continues to use stiffness models based on dry conditions or fixed empirical values ​​to set the tension, the applied tension will far exceed the fabric's yield strength under humid and hot conditions, resulting in irreversible plastic deformation or wrinkles. To enable the control algorithm to perceive and adapt to this drastic change in physical properties, this application implements dynamic modulus estimation based on a rheological model to provide a quantitative benchmark reflecting the fabric's current true softness and stiffness for subsequent precise tension control, ensuring that the control command matches the fabric's instantaneous bearing capacity.

[0025] Figure 3 This is a flowchart of step S3 in the fully automatic control method of the pre-processing flat-panel integrated machine according to an embodiment of this application. Figure 3As shown, in one feasible embodiment of this application, S3, dynamic modulus estimation based on a rheological model is performed on the fabric state feature vector to obtain dynamic elastic modulus parameters, including: S31, loading a rheological model parameter set based on the fabric type ID in the fabric state feature vector, wherein the rheological model parameter set includes dry modulus, moisture absorption influence coefficient, temperature rise influence coefficient, and reference temperature; S32, independently calculating the moisture and heat influence factors on the rheological model parameter set and the fabric state feature vector to obtain the moisture influence factor and temperature influence factor; S33, performing a comprehensive dynamic elastic modulus solution on the moisture influence factor, temperature influence factor, and dry modulus in the rheological model parameter set to obtain dynamic elastic modulus parameters.

[0026] In the above feasible embodiment, the specific process of S3 is as follows: S31: The processing unit extracts the key identification information, fabric type ID, from the fabric state feature vector, such as PET-HighDensity-75D, representing a 75 denier high-density polyester imitation silk fabric. This ID is not only a label for production management but also an index key to the fabric's micromechanical constitutive equation. The fabric material property library is a structured database or parameter lookup table built from a large amount of offline experimental data. In the process preparation stage before the equipment is put into production, technicians use precision testing instruments such as electronic fabric tensile testers to conduct full-dimensional rheological property tests on different types of fabrics, such as pure cotton poplin, nylon taffeta, and high-density polyester fabrics. The tests cover standard dry conditions such as 20℃, 65% relative humidity, completely wet conditions, and tensile tests at different high temperature gradients such as 60℃, 80℃, and 98℃, thereby obtaining a series of stress-strain curves. By performing nonlinear regression analysis and fitting on these experimental curves, constitutive parameters that can characterize the modulus decay law of each fabric under humid and hot conditions are extracted. These parameters are encapsulated into independent data structures and stored in the attribute database using the fabric type ID as the primary key. The attribute database architecture includes an index area and a parameter data area, supporting microsecond-level fast retrieval via hash algorithms or B-tree indexes to meet the timeliness requirements of real-time control. When the processing unit obtains the ID PET-HighDensity-75D, it immediately initiates a query request to the fabric material attribute database. Based on this primary key, it quickly locates the corresponding storage address and loads the associated set of rheological model parameters. This parameter set contains four core physical quantities: dry modulus... Moisture absorption effect coefficient Temperature rise influence coefficient and reference temperature Among them, the dry modulus This represents the initial stiffness reference of the fabric when it is not affected by damp heat. For the high-density polyester fabric in this embodiment, this value may be set to 1500 (normalized relative stiffness value or modulus value in specific units), reflecting the fiber's resistance to deformation when it is in a glassy or frozen state in the crystalline region. Reference temperature Set the temperature to a standard laboratory temperature, such as 20°C, for the calculated temperature rise. Zero-point reference. Moisture absorption effect coefficient. and temperature rise influence coefficient This is a key weighting parameter describing the sensitivity to rheological behavior. It is a dimensionless empirical coefficient used to quantify the intensity of the plasticizing effect produced when water molecules enter the non-crystalline region of a fiber. For polyester fabrics with poor hygroscopicity but sensitive to interlayer lubrication caused by water molecules, this value was experimentally determined to be 0.4, meaning that the presence of moisture will significantly weaken the friction between fibers according to a specific functional relationship. This is a coefficient with a dimension inversely related to temperature, used to characterize the promoting effect of thermal activation energy on polymer chain segment motion. For example, setting... A value of 0.015 indicates that for every 1 degree Celsius increase in temperature, the modulus will decrease proportionally according to an exponential law. These two coefficients are fundamental to the rheological model, determining the shape of the model's response curve to environmental changes. After the loading process is complete, the processing unit will process this set of static physical constants (dry modulus) =1500, Moisture absorption effect coefficient =0.4, temperature rise influence coefficient =0.015, reference temperature =20) is transferred from non-volatile memory to the high-speed operation cache, along with dynamic variables from the eigenvector ( 98℃ Alignment is performed using 0.9.

[0027] S32: The processing unit adopts a parallel computing architecture, simultaneously initiating mathematical calculations for the thermal and moisture-affected paths. In one feasible embodiment of this application, S32, independently calculating the moisture and thermal influence factors on the rheological model parameter set and the fabric state feature vector to obtain the moisture influence factor and temperature influence factor, includes: independently calculating the moisture and thermal influence factors on the rheological model parameter set and the fabric state feature vector using the following formula: , ;in, Temperature is a feature vector representing the state of the fabric. As the reference temperature, For the temperature rise, This is the temperature rise influence coefficient. Temperature is a factor that affects the environment. This is the moisture absorption effect coefficient. The liquid content is the percentage of liquid carried in the fabric's state feature vector. This refers to the moisture influence factor. Specifically, in the calculation path of the heat influence factor, the processing unit first performs a temperature rise calculation. The calculation is a fundamental but crucial preprocessing step used to determine the degree of deviation of the current thermodynamic state from the standard state. The processing unit reads the real-time temperature from the feature vector. In the aforementioned embodiments, 98°C is the reference temperature in the parameter set. 20℃, according to the formula Perform a difference operation. Substituting the values, we get... =98-20=78℃. This value physically represents the thermal potential difference of the injected fabric micro-element. Subsequently, the processing unit uses the calculated temperature rise value, combined with the temperature rise influence coefficient... For example, 0.015, further calculate the temperature influence factor. This calculation is based on an Arrhenius-like exponential decay model, which accurately describes the decrease in cohesive energy density and modulus degradation in polymer materials at high temperatures due to intensified molecular thermal motion. The calculation formula is as follows: ,in The base of the natural logarithm, with the sign... This indicates that the modulus retention rate decreases exponentially with increasing temperature. Substituting specific values ​​into the formula, the calculation process is as follows: After processing by the floating-point unit, the value is approximately 0.3096. This result indicates that, considering only the thermal effect, at a high temperature of 98°C, the elastic modulus of the fabric will decrease to approximately 31% of its dry-state baseline value. Meanwhile, in the calculation path of the moisture influence factor, the processing unit solves based on the fabric's moisture absorption and swelling mechanism. Moisture entering the fiber interior acts like a lubricant, reducing the friction between molecular chains, thus macroscopically manifesting as fabric softening. For the high-density polyester fabric in this embodiment, the model uses a linearly simplified formula to characterize this physical process. The processing unit reads the moisture absorption influence coefficient. Such as 0.4 and liquid carryover rate For example, 0.9, execute the formula. In this formula, 1 represents the baseline factor under completely dry conditions. The term quantifies the stiffness loss caused by moisture introduction. Substituting into the numerical calculation yields... =1 - 0.4 × 0.9 = 1 - 0.36 = 0.64. This means that under wet conditions with a liquid content of 90%, considering only the moisture factor, the modulus of the fabric will decrease to 64% of its dry value. Finally, two key dimensionless parameters were output: temperature influence factor. ≈0.31 and moisture influencing factor =0.64. These two factors independently quantify the weakening effect of heat and moisture on the mechanical properties of fabrics. They are not directly mixed in this step, but are passed on to subsequent steps in an independent state.

[0028] S33: The processing unit employs a product-coupled model to perform a comprehensive dynamic elastic modulus calculation. This model is based on the equivalent superposition principle in rheology, assuming that, without phase change, the weakening effect of environmental factors on the modulus of polymer materials can be expressed as a continuous multiplicative correction to the baseline modulus. In one feasible embodiment of this application, S33, a comprehensive dynamic elastic modulus calculation is performed on the moisture influence factor, temperature influence factor, and dry modulus from the rheological model parameter set to obtain dynamic elastic modulus parameters. This includes: performing a comprehensive dynamic elastic modulus calculation on the moisture influence factor, temperature influence factor, and dry modulus from the rheological model parameter set using the following formula: ;in, For dry modulus, The dynamic elastic modulus parameter is no longer a static constant, but a time-varying parameter that reflects the instantaneous stiffness characteristics of the fabric. Substituting the specific values ​​obtained in the previous steps, the processing unit performs high-precision floating-point multiplication: first, it calculates 1500 × 0.64, obtaining an intermediate result of 960, which represents the equivalent modulus of the fabric only under hygroscopic conditions; then, it multiplies this intermediate result by the temperature influence factor, i.e., 960 × 0.3096, obtaining a final result of approximately 297.22. This calculation reveals a crucial physical fact: under extreme conditions of 98℃ and 90% liquid retention, the stiffness of this high-density polyester fabric has significantly decreased from the initial 1500 to approximately 297, retaining less than 20% of its deformation resistance.

[0029] In S4, the optimal tension baseline value is determined based on the dynamic elastic modulus parameter and work order ID. Correspondingly, after the rheological model calculation in the previous steps, the control system has grasped the dynamic elastic modulus of the fabric under the current humid and hot environment, thus quantifying the fabric's softness and hardness at this moment. However, this modulus value is merely a strength index characterizing the material's essential properties; it represents the material's ability to resist deformation, but it is not equivalent to the specific traction force that the motor should output. To convert this theoretical material property into a torque command that the servo motor can execute, it is necessary to introduce the fabric's geometric dimensions (such as the load-bearing cross-sectional area) and process safety constraints (such as the allowable deformation rate). Without this step, it will be impossible to distinguish the tension differences required for two equally soft but different thicknesses of fabric, easily leading to the breakage of thin fabrics due to excessive tension, or the instability of thick fabrics due to insufficient tension. Therefore, the implementation of determining the optimal tension benchmark value based on dynamic elastic modulus parameters and work order ID aims to map the abstract material rheological properties into specific safe tension thresholds that meet the current fabric specifications and process requirements. This ensures that the physical traction force applied to the fabric can maintain flat-width transmission while being strictly controlled within the material yield limit, thereby fundamentally eliminating dead creases and physical damage.

[0030] In one feasible embodiment of this application, S4, determining the optimal tension baseline value based on the dynamic elastic modulus parameter and the work order ID, includes: S41, loading a fabric geometry and process parameter set from the production work order parameter library based on the work order ID, wherein the fabric geometry and process parameter set includes the fabric cross-sectional area, target micro-strain, and process adjustment coefficient; S42, calculating the ideal elastic tension baseline using the dynamic elastic modulus parameter and the fabric geometry and process parameter set to obtain the ideal tension baseline value; S43, adjusting the ideal tension baseline value using the process adjustment coefficient in the fabric geometry and process parameter set to obtain the optimal tension baseline value.

[0031] In the above feasible embodiment, the specific process of S4 is as follows: S41: First, the processing unit reads the work order ID contained in the fabric status feature vector, such as ORD-202510-A05. This ID serves as a unique index key and is used to access the production work order parameter library stored on the local server or in the MES system image. The production work order parameter library is a pre-built static database that records in detail the physical specifications and process requirements of each batch of fabric in the production schedule. The database architecture includes a work order index table, a fabric specification detail table, and a process section parameter table, supporting quick data retrieval via SQL queries or key-value pair retrieval. When the processing unit initiates a retrieval with ORD-202510-A05, it first locks the corresponding fabric specification record and extracts two key geometric parameters: fabric width. and fabric thickness For example, for the current batch of high-density polyester imitation yarn, the database records a width of 1800mm (1.8 meters) and a thickness of 0.15mm. In some scenarios where only fabric weight per square meter (GSM) and density are recorded, they are pre-converted to equivalent thickness using physical formulas. After acquiring the basic data, the processing unit calculates the fabric cross-sectional area, a parameter representing the actual physical cross-sectional size of the fabric under tension in the transmission direction. The calculation follows the formula: Substituting the above example values ​​into the formula, the processing unit calculates the result: =1800×0.15=270mm 2 Next, the processing unit continues to extract the process constraint parameters bound to the work order from the library. The first parameter obtained is the target micro-strain. This is a dimensionless physical quantity representing the maximum elastic elongation of the fabric allowed by the process. For high-density fabrics, this value is set extremely strictly to prevent damage to the fabric structure or residual internal stress due to excessive stretching. For example, the target micro-strain set in the library is 0.003, or 0.3%, meaning that under ideal tension control, the change in fabric length should not exceed three-thousandths of the original length. Finally, the processing unit locates the fabric's current process section through linear velocity integration; if it is currently in the high-strength scouring section, it applies the corresponding process adjustment coefficient. This coefficient is an empirical correction factor used to compensate for differences in mechanical properties (such as fluid resistance and rolling mill effects) in specific sections. For example, if a slight increase in warp tension is needed in the current section to enhance the washing effect, the coefficient configured in the library might be 1.05. After the above processing, the fabric cross-sectional area is successfully integrated. =270mm 2 Target micro-strain =0.003 and process adjustment coefficient =1.05, and encapsulate these data into a set of fabric geometry and process parameters.

[0032] S42: Based on Hooke's Law in classical mechanics, material properties are mapped to mechanical indices. In one feasible embodiment of this application, S42, the ideal elastic tension baseline is calculated for the dynamic elastic modulus parameter and the set of fabric geometry and process parameters to obtain the ideal tension baseline value, including: calculating the ideal elastic tension baseline for the dynamic elastic modulus parameter and the set of fabric geometry and process parameters using the following formula: ;in, For the target micro-strain, The cross-sectional area of ​​the fabric. This represents the ideal tension baseline value. Specifically, the calculation unit first utilizes the stress-strain relationship form of Hooke's law. The theoretical tensile stress that the fabric cross-section should withstand in order to maintain the fabric in the target micro-strain state (i.e., maintain 0.3% elastic elongation, without relaxation or plastic deformation) was calculated. The calculation process is as follows: The intermediate result, 297.22 × 0.003 ≈ 0.89166 MPa, indicates that under the current softened state, only a stress of less than 1 MPa is needed to achieve the desired deformation control target. Next, to obtain the actual physical resultant force (i.e., tension) acting on the guide roller, the calculation unit needs to extend this stress value per unit area to the entire physical cross-section of the fabric. This requires introducing the fabric cross-sectional area. It participates in the calculation. The specific calculation formula is as follows: Substituting the specific values ​​into the formula, the calculation process is as follows: =0.89166MPa×270mm 2 ≈240.75N. The final calculated value of 240.75N is the ideal tension baseline. This value has significant physical meaning: it represents the theoretical traction force required to maintain the flatness and prevent permanent deformation of a fabric of this material, softening level, thickness, and width under ideal physical experimental conditions. It completely abandons the fixed values ​​(such as 300N or 400N) set empirically in traditional control methods, and is derived entirely from the current physical state of the material. It is foreseeable that if the fabric temperature decreases or it dries, the modulus will... If the moisture level rises, the baseline value will automatically increase; conversely, if the fabric becomes softer and absorbs more moisture, the value will automatically decrease, thus achieving true adaptive control.

[0033] S43: The purpose of this step is to combine purely theoretical physical calculations with the specific process requirements of actual industrial production. Although the ideal tension baseline value... While theoretically perfect, in actual pretreatment processes, different treatment tanks (such as desizing tanks, scouring tanks, and bleaching tanks) often have specific mechanical operating environments. For example, in the high-intensity scouring section, to overcome the impact resistance of the liquid flow on the fabric, or to achieve better extrusion and dewatering at the rolling mill, process experts recommend slightly increasing the tension; while in some fine bleaching sections, to protect fiber whiteness, it may be necessary to slightly reduce the tension. This experience from process experts is quantified into process adjustment coefficients. The parameters are stored in the parameter set described in S41. In this embodiment, if the current fabric is in the heavy washing section, the process adjustment coefficient set in the parameter set is... The value is 1.05. The calculation unit reads this coefficient and performs a final correction calculation on the previously calculated ideal tension baseline value. The calculation formula is: Substitute the values ​​into the calculation: =1.05 × 240.75 ≈ 252.79 N. After this correction step, the optimal tension reference value is finally determined. The value is 252.79 N. This value is not merely a physical calculation result, but a comprehensive control command integrating material rheological properties, fabric geometry, and on-site process expert knowledge. This optimal tension reference value is then transmitted in real time to the feedforward and impedance composite control module as the target setpoint for the motor servo system. Compared to the fixed setpoint in traditional solutions, this reference value is dynamic and adaptable. Whenever the sensor detects minute fluctuations in temperature or liquid content, or when production switches to different fabric specifications, this value is automatically recalculated and adjusted within milliseconds, always ensuring that the traction force on the fabric is just right—sufficient for flat-width transmission and absolutely safe, thus effectively solving the industry problem of wrinkles caused by tension mismatch in high-density fabrics.

[0034] In S5, the optimal tension reference value and the original operating data package are processed using a combination of feedforward and impedance control to obtain the motor torque control command. In other words, after the precise calculations in the preceding steps, the control core has obtained the optimal tension reference value adapted to the current wet-heat rheological state of the fabric. However, this reference value is essentially only a scalar control target; the servo drive unit cannot directly execute the physical command of tension, but can only execute current or torque commands. Traditional PID control relies on feedback loops, meaning it needs to wait for the actual tension to deviate from the target value before the integral term begins to accumulate and output an adjustment. This lag can lead to significant dynamic errors during high-speed operation or acceleration / deceleration. To eliminate this control lag, a feedforward mechanism needs to be established that can pre-calculate the theoretical torque required by the motor to maintain the tension target based on physical laws and compensate for the inertial resistance of the mechanical system itself. Therefore, this application implements feedforward and impedance composite control quantity calculation on the optimal tension reference value and the original running data package to decouple the tension target value and convert it into a composite torque command that the motor can directly respond to, so that the motor can actively output matching power before the error occurs, thereby achieving precise control with high dynamic response.

[0035] Figure 4 This is a schematic diagram of the data flow in step S5 of the fully automatic control method for a pre-processing flat-panel integrated machine according to an embodiment of this application. Figure 4As shown, in one feasible embodiment of this application, step S5, calculating the feedforward and impedance composite control quantity of the optimal tension reference value and the original operating data package to obtain the motor torque control command, includes: S51, calculating the inertial and feedforward torque components of the motor angular velocity in the optimal tension reference value and the original operating data package to obtain the inertial torque component and the feedforward torque component; S52, calculating the proportional term torque of the actual tension value at the current moment in the optimal tension reference value and the original operating data package to obtain the proportional compensation torque; S53, determining the differential compensation torque based on the actual tension value at the current moment and the actual tension value at the previous moment in the original operating data package; S54, synthesizing the inertial torque component, the feedforward torque component, the proportional compensation torque, and the differential compensation torque to obtain the motor torque control command.

[0036] In the above feasible embodiment, the specific process of S5 is as follows: S51: Based on the rigid body dynamics model, calculate the two key open-loop components in the motor drive signal. The first stage of implementation is the calculation of the inertial torque component. During high-speed flat-width transmission, the output torque of the motor is not only used to pull the fabric, but a considerable portion is also consumed in overcoming the rotational inertia of the transmission rollers and the motor rotor itself, especially during acceleration and deceleration. In order to calculate this component, the processing unit first needs to obtain the system rotational inertia. The system moment of inertia is a preset mechanical physical parameter, automatically measured during equipment commissioning via an inertia identification program or calculated based on the mechanical design CAD model, and stored in the controller's non-volatile mechanical parameter block. It characterizes the ability of the transmission system (including the motor rotor, reducer, guide rollers, etc.) to resist changes in rotational speed. In this embodiment, the system moment of inertia is set. 0.5 kg·m 2 With the inertia parameter obtained, the processing unit retrieves the real-time motor angular velocity from the original operation data packet. To obtain angular acceleration, the processing unit performs numerical differentiation. For example, the angular velocity at the current sampling time t... The angular velocity is 10 rad / s, while the angular velocity at the previous sampling time t-1, such as a sampling period of 0.01 s, is... The value is 9.9 rad / s, indicating that the system is in a state of slight acceleration. The processing unit calculates the angular acceleration using a differential formula: Subsequently, the processing unit calculates the components of the inertial torque using the rotational form of Newton's second law. The formula is as follows: Substituting the numerical values, we can obtain: =0.5kg·m 2 ×10rad / s 2 =5 N·m, this calculation result indicates that, just to allow the mechanical roller following system to operate at 10 rad / s...2 For angular acceleration, the motor needs to output an additional 5 N·m of torque. This torque does not act on the fabric at all and must be compensated independently. The second stage of implementation is the calculation of the feedforward torque component. This torque is the fundamental power source that actually does work on the fabric and establishes tension. The processing unit first loads the roller radius... This parameter is the physical radius of the main drive roller, a fixed mechanical dimension stored in the equipment's specification table. In this embodiment, the roller radius is set. The diameter is 200mm, which is 0.1m. Next, the processing unit uses the optimal tension reference value calculated in previous step S43, which is 252.79N. To calculate the motor base torque required to maintain this static tension, the processing unit applies the torque balance principle to perform the calculation. The calculation formula is: Substitute specific values ​​into the calculation: =252.79N × 0.1m = 25.279 N·m, which is the feedforward torque component. Its physical significance is that, under ideal conditions of uniform speed and no friction, as long as the motor outputs this value of torque, it can generate a precise 252.79 Newton tangential force on the surface of a roller with a radius of 0.1 meters, thus meeting the tension requirements of the fabric. Through the above steps, the inertial torque component used to overcome mechanical inertia has been successfully separated. and the feedforward torque component used to establish fabric tension Both of these components are calculated directly based on physical models and known targets, without relying on feedback signals from any sensors, thus exhibiting extremely high response speed.

[0037] S52: First, the processing unit calculates the tension error. This requires two key inputs: one is the optimal tension reference value determined in step S43, which is 252.79 N; the other is the actual tension value at the current moment, acquired in real time in step S1 and filtered. For example, at the current sampling moment, due to fabric splices or pressure fluctuations in the rolling mill, the actual value fed back by the tension sensor is 250.0 N. The processing unit calculates the current tension error through subtraction. : =252.79-250.0=2.79N. This positive value indicates that the actual tension is too low, and the motor needs to increase its output torque to tension the fabric. Next, the processing unit introduces a proportional gain. Parameters. Proportional gain This is a preset control parameter that defines the sensitivity or stiffness of the control system to errors. Physically, it's equivalent to the stiffness coefficient of a virtual spring. This parameter is tuned during equipment commissioning using the Ziegler-Nichols method or the critical proportional gain method and stored in the PID parameter configuration block. For this pretreatment equipment, to achieve a balance between fast response and system stability, such as... It is set to 5.0 (dimensionless or specific unity gain). This means that for every 1 Newton of tension error, the system tends to output a correction force equivalent to 5 units. The processing unit then calculates the proportional compensation force. The calculated tension error is then... With proportional gain Multiplying them together yields an intermediate variable—the linear compensating force. : =5.0 × 2.79 = 13.95 N. This means that to eliminate the 2.79 Newton deviation, the controller determines that an additional force of 13.95 Newtons is needed. It's worth noting that this is an intermediate quantity at the control algorithm level; its actual physical effect depends on the overall response of the closed-loop system. Finally, to convert this linear force into a control signal for the rotating motor, the processing unit converts it into a force rectangle. The processing unit then retrieves the stored roller radius... Parameter 0.1m, execute the final proportional compensation torque. Calculation. The calculation formula is as follows: Substitute the values ​​into the calculation: =13.95N × 0.1m = 1.395N·m, which is the calculated proportional compensation torque. This component will be superimposed on the basic feedforward torque. Its presence ensures that when the actual tension of the fabric is less than the optimal set value due to any disturbance, the motor can obtain an additional 1.395N·m of driving force, thereby accelerating rotation to tighten the fabric until the error is eliminated.

[0038] S53: First, the processing unit performs the calculation of the rate of change of tension. This calculation relies on two time-domain data points from the original runtime data packet constructed in step S1: the actual tension value at the current moment. In this embodiment, the value is 250.0 N, and it is also the actual tension value at the previous moment. Such as the controller's sampling period. The time interval is 0.01 s, and the tension value recorded at the previous moment was 249.5 N. This indicates that the tension increased by 0.5 Newtons within just 10 milliseconds, showing a rapid increasing trend. The processing unit uses a first-order difference algorithm to calculate the derivative of the tension with respect to time: =(250.0-249.5) / 0.01=50N / s. The calculation result quantifies the current rate of tension surge; if left unchecked, the tension may rapidly exceed the target value. Next, the processing unit introduces a differential gain. Parameters. Differential gain This is a preset control parameter, which corresponds to the viscous damping coefficient in the physical model. This parameter is also fine-tuned during the equipment commissioning phase by observing the system's step response curve and based on the need to suppress oscillations, and is stored in the PID parameter table. For high-density fabric processing, in order to obtain stronger steady-state retention capabilities, such as... The value is set to 0.2, with units related to the time constant. This means the system will generate a corresponding damping force for each unit of tension change rate. The processing unit then calculates the differential compensation force. The calculated tension change rate is then... With differential gain Multiply. It's important to note the directionality of the differential action here: the differential term is used to resist change, therefore in the standard PID algorithm, its sign logic is intended to generate negative feedback. However, in the compensation force calculation described in this step, the magnitude of this damping force is calculated first: =0.2 × 50 = 10 N, indicating that to counteract the tendency of the tension to surge by 50 N / s, a counterforce equivalent to 10 Newtons is required. In the final synthesis, this value is negative to provide a damping effect. To maintain consistency, if the differential is defined to suppress change, then the differential torque should be negative (deceleration / relaxation) when the tension increases, and vice versa. In this step, the conversion of its magnitude is crucial. Finally, the processing unit converts this differential compensation force into a differential compensation torque. Utilizing the roller radius Convert 0.1m: Substitute the values: =(0.2×50)×0.1=1.0N·m, which is the differential compensation torque. In the final torque synthesis logic, since the tension is increasing rapidly (positive rate of change), this damping torque will be added to the total torque as a subtraction term (or a negative value) to weaken the motor output and prevent over-extension.

[0039] S54: Before proceeding to this synthesis step, the parallel processing units of the controller have completed the quantization calculations of the four independent torque components. Reviewing the calculation results from the preceding steps: based on the mechanical dynamics model, the parameters for overcoming 10 rad / s... 2 Inertial torque component of angular acceleration drag 5.0 N·m; Based on the optimal tension reference value of 252.79 N and the roller radius of 0.1 m, the feedforward torque component used to maintain the base tension was calculated. The torque is 25.279 N·m; based on the real-time tension error of 2.79 N, the proportional compensation torque for the pull-back deviation is calculated. The value is 1.395 N·m; based on the trend of the tension increasing rapidly at a rate of 50 N / s, the differential compensation torque used to suppress overshoot is calculated. The modulus is 1.0 N·m. At this point, the processing unit initiates a linear superposition algorithm to perform torque synthesis. This operation is not a simple numerical accumulation, but an algebraic summation based on the direction of physical action. The processing unit defines the direction in which the motor drives the fabric and establishes tension as the positive direction. First, the processing unit loads the feedforward torque component and the inertial torque component. Since the system is in an accelerating state and needs to maintain tension, both components are included in the synthesis as positive values. They form the basis of the control command, accounting for a large proportion of the total output, ensuring that the motor can output a basic power of about 30 N·m even without any sensor feedback, keeping the fabric under control. Second, the processing unit superimposes the proportional compensation torque. Since the current actual tension is less than the target value (there is a positive error), the proportional term increases the output to eliminate the gap, so this component is taken as a positive value (+1.395 N·m) and added to the sum. Finally, the processing unit processes the differential compensation torque. This is the most intelligent part of the synthesis process. Although the modulus calculated in the previous steps is 1.0 N·m, the processing unit detects that the current tension change rate is positive (i.e., the tension is increasing rapidly). To prevent overshoot caused by the tension rapidly exceeding the target value under the drive of the proportional term, the differential term must act as a "damper," generating a counteracting force. Therefore, during algebraic composition, this component is given a negative sign (or participates in the calculation as a subtraction term). Based on the above logic, the processing unit performs the operation described by the following formula: Substitute the specific values ​​into the solution for the final calculation: =5.0+25.279+1.395-1.0=30.674N·m, if in the formula Since symbolic logic is already included, the values ​​are directly added; the symbols are deliberately disassembled here to demonstrate the physical logic. The calculated 30.674 N·m is the final motor torque control command. This high-precision floating-point value is then mapped to a standard communication message for the servo drive, corresponding to 61.35% of the rated torque. If the motor's rated torque is 50 N·m, this is sent to the frequency converter drive unit via industrial real-time Ethernet (such as EtherCAT bus) at microsecond intervals. The drive unit adjusts the vector magnitude and phase of the stator current accordingly, driving the motor to precisely output a shaft-end torque of 30.674 N·m. Through this synthesis mechanism, the motor not only overcomes its own mechanical inertia, providing the optimal tension required to maintain the fabric in a softened state, but also compensates for minor tension deviations and smoothly suppresses excessively rapid tension changes, thereby achieving fully automatic, highly dynamic, and damage-free transmission of high-density fabrics in the pre-treatment flat-width integrated machine.

[0040] In particular, during the fully automatic control of the pre-processing and flat-panel integrated machine, although the inertial torque component, feedforward torque component, proportional compensation torque, and differential compensation torque have been calculated separately through the physical model, it is crucial to synthesize these four components into the final drive motor command. The control scheme described above adopts a linear static weighting approach, implicitly assuming that the weight of each torque component is 1 and remains constant under all operating conditions. This approach has a clear structure and conforms to classical control theory. However, in actual industrial production, the relative importance of each component changes dynamically under different operating conditions. For example, during high-speed stable operation, the feedforward torque component should dominate, while the feedback component should be kept small to avoid introducing noise; the inertial torque component has the greatest impact when the machine starts or stops or when the speed changes drastically; especially when a violent fluctuation in tension is detected, the importance of the damping term should be temporarily increased to quickly suppress oscillations. More importantly, the tension control of the pre-treatment flat-width integrated machine exhibits significant risk asymmetry: for negative errors (i.e., tension greater than the set value), excessive fabric stretching can lead to irreversible plastic deformation, physical damage, or even breakage, placing it in a high-risk zone where the control system must be extremely sensitive; conversely, for positive errors (i.e., tension less than the set value), excessive fabric slack can result in permanent creases or curling, affecting quality, but its urgency and consequences are far less severe than overstretching, allowing the system to tolerate it to some extent. Furthermore, linear static weights often respond slowly or overshoot to unmodeled external disturbances (such as fabric splices or voltage fluctuations). Given this background and the complexity of actual operating conditions, to generate a final torque command more suited to the current conditions, it is necessary to perform rule-based adaptive torque fusion of the inertial torque component, feedforward torque component, proportional compensation torque, and differential compensation torque to obtain the motor torque control command.

[0041] In a feasible preferred embodiment of this application, S54, torque synthesis is performed on the inertial torque component, feedforward torque component, proportional compensation torque, and differential compensation torque to obtain a motor torque control command, including: performing rule-based adaptive torque fusion on the inertial torque component, feedforward torque component, proportional compensation torque, and differential compensation torque to obtain a motor torque control command.

[0042] The specific implementation process is as follows: First, the precise input quantity, i.e., the tension error calculated in the previous steps, is used. and error tension change rate Converted to regular variables. To address the aforementioned asymmetric risk problem, this embodiment constructs an asymmetric S-shaped membership function with stress-sensitive characteristics. This function contains three key structural features: a stable zero-zone flat top, i.e., defining a trapezoidal flat top for the zero (ZE) fuzzy set, ensuring that the system does not generate excessive response within a small error range, thereby protecting the motor and mechanical transmission components in steady state; and defining the dead zone half-width. If set to 0.5N, the system is considered to have zero error within a small error range of ±0.5N, ensuring that the system does not generate excessive response to protect mechanical components; a steep negative slope, i.e., for rule sets representing excessive tension such as negative small (NS) and negative medium (NM), a larger negative sensitivity coefficient is used. Setting it to 2.5 makes the transition zone of the S-curve very steep, meaning that if the tension slightly exceeds the set value, its membership will spike rapidly, triggering a strong control response. A gentler positive slope, i.e., for rule sets representing looser tension such as PS and PM, uses a smaller positive sensitivity coefficient. Setting it to 0.8 makes the S-curve relatively gentle, allowing the system to have greater tolerance for fabric slack and a softer response. Specifically, for the rule set of negative small (NS), its membership function... Defined as a piecewise function, the formula is as follows: ;in, Indicates tension error The membership degree belonging to the negative small rule set, with a value range of [0,1]; The zero-area half-width is used to define the boundary of the insensitive area; The negative saturation width, if set to 5N, defines the error boundary for achieving full membership; The negative sensitivity coefficient determines the system's sensitivity to overstretch risk; The negative center point offset, determined through calculation, is used to locate the center of the S-curve. The center of the interval. Correspondingly, the membership function of the positive minimum (PS) rule set. The core components follow the following proportional relationships: ;in, It is a positive sensitivity coefficient, and is set as follows: This reflects the asymmetry of the control strategy. Similarly, for other rule sets, the computational logic is isomorphic: for the negative center (NM) rule set, its membership function retains the same steep sigmoid core structure as NS, but by setting a more negative center point offset. The curve center is shifted to the negative value region with larger errors to cover severely overstretched conditions; while the core functions for the positive small (PS) and positive middle (PM) rule sets are also based on the Sigmoid function, they are set to be much smaller than... of Parameters and corresponding positive center offset ( , This allows the curve to show a slow upward trend within the positive error domain, thus achieving precise quantification of risk asymmetry at the mathematical level.

[0043] Secondly, rule-based reasoning is performed based on the aforementioned rule variables. By calling a pre-defined expert rule base, the decision-making logic under different operating conditions is simulated. Specifically, the corresponding control strategy is matched according to the real-time state: Rule 1 is set as follows: if the tension error is ZE and the tension change rate is ZE, indicating that the system is in an ideal steady state, then the proportional term weight is adjusted. and differential term weights All values ​​are set to small values ​​to handle stable system conditions and reduce unnecessary feedback. Rule 2 is set to the following: if the tension error is positive (PB) and the tension change rate is positive (PS), indicating that the fabric is severely loose and has a tendency to loosen further, then the weight of the proportional term is adjusted. Set as the weight of the large differential term The first rule is set to medium, used for strong correction to quickly restore tension. Rule 3 is set so that if the tension error is ZE but the tension change rate is positive (PB), it indicates that although the current tension meets the standard, there is a strong tendency for relaxation disturbance. In this case, the proportional term weight is set to small and the differential term weight is set to large, used for strong damping suppression when the error is small but changes rapidly. In addition, other combination rules can be preset according to specific process requirements (such as different types of fabrics).

[0044] Finally, dynamically generated weights are used to weight and synthesize the individual torque components. This takes into account the feedforward torque components. and inertial torque components This constitutes the basic dynamic model of the system, with its weights kept at 1, while the proportional compensation torque of the feedback component... and differential compensation torque Apply dynamic weights. The torque composition formula is as follows: ;in, This is the final motor torque control command. The weighting coefficients for the adaptively generated proportional terms. The sign direction of the adaptively generated differential term weight coefficients is determined according to the error elimination logic. This rule-based control system is essentially a highly nonlinear static mapper that maps the input error state to optimal control parameter weights, making it more flexible than traditional linear PID controllers in handling complex time-varying problems in the preprocessing stage. Simultaneously, driven by the knowledge of an expert rule base, this embodiment effectively quantifies and applies qualitative process experience (such as "increase damping if tension fluctuates rapidly") that is difficult to express mathematically to the control system. Therefore, the system no longer relies on fixed gain coefficients but dynamically adjusts the weights to change the response characteristics of the feedback loop in real time, achieving online self-tuning of the PID parameters and making the control effect tend towards optimization under various operating conditions.

[0045] Specifically, taking the data from the preliminary steps of this embodiment as an example: the preliminary steps calculate the optimal tension reference value. The actual tension value is 252.79 N. If the value is 250.0N, then the tension error is... =2.79N. By definition, a positive error means the actual tension is less than the set value (the fabric is too loose). At this point, the system is in the positive control zone. (For example, the preset dead zone half-width...) =0.5N, positive sensitivity coefficient =0.8. Since 2.79N > 0.5N, the system activates the regulation mechanism. However, due to the use of a smaller... The calculated membership degree increases relatively slowly, and the weight coefficient of the proportional term output by rule reasoning is relatively high. The value is likely 0.9 (slightly lower than the 1.0 of linear superposition, reflecting a gentler adjustment). Meanwhile, the preceding step detected a tension change rate of 50 N / s, which is relatively rapid. The rule base determines that increased damping is needed to prevent overshoot, and outputs the differential term weight coefficient. The value is 1.2. Substitute the torque component values ​​calculated in the previous steps: Inertial torque component. =5.0 N·m, feedforward torque component =25.279 N·m, proportional compensation torque =1.395 N·m, differential compensation torque magnitude =1.0 N·m. Perform adaptive torque fusion calculation: The final motor torque control command is 30.3345 N·m, approximately 30.3345 N·m. Compared to the 30.674 N·m calculated by simple linear superposition, this adaptive command slightly reduces the driving torque. This is because the system recognizes that it is currently in a slightly slack state with a rapid rate of change. To prevent sudden tightening from causing subsequent overshoot oscillations, asymmetric logic is used to soften the proportional action and enhance the differential damping effect, thus achieving smoother and safer fabric transfer control. It is worth mentioning that the aforementioned sensitivity coefficient (e.g., , ) and threshold parameters (such as , During the equipment commissioning phase, stress-strain limit data (such as breaking strength and yield point) of the fabric are obtained through actual tensile tests. Combined with the process experts' experience in the tolerance of product quality (such as wrinkles and breaks), the data is adjusted using offline simulation or trial operation data and stored in the system parameter library.

[0046] In summary, the fully automatic control method for the pre-processing flat-width integrated machine based on the embodiments of this application is explained, addressing the problem of tension mismatch and creases caused by changes in modulus due to fabric moisture absorption and heat in the prior art. First, the liquid content, temperature, and operating status data of the fabric are acquired in real time to construct a fabric state feature vector. This vector is then analyzed using a rheological model to accurately estimate the dynamic elastic modulus of the fabric under humid and hot conditions, thereby quantifying fiber swelling and changes in physical properties under high-energy states. Based on this, the optimal tension reference value adapted to the current physical state is dynamically calculated, replacing the traditional constant tension setting, and a feedforward and impedance composite control algorithm is combined to calculate the motor torque command. By sensing changes in fabric properties in real time and predictively adjusting the tension threshold, the dynamic tension mismatch problem caused by feedback lag and a single model in the prior art is solved, effectively avoiding the generation of dead creases in high-density fabrics during processing.

[0047] Figure 5 This is a block diagram of a fully automatic control system for a pre-processing flat-panel integrated machine according to an embodiment of this application. Figure 5As shown, the fully automatic control system 100 of the pretreatment flat-width integrated machine according to an embodiment of this application includes: a raw running data packet acquisition module 110, used to acquire raw running data packets, the raw running data packets including the actual tension value at the current moment, the actual tension value at the previous moment, the motor angular velocity, the current transmission linear velocity of the fabric, the current liquid carrying rate of the fabric, and the real-time temperature of the processing tank; a fabric state feature extraction module 120, used to perform fabric state feature vectorization on the raw running data packets to obtain fabric state feature vectors; an elastic modulus parameter calculation module 130, used to perform dynamic modulus estimation based on a rheological model on the fabric state feature vectors to obtain dynamic elastic modulus parameters; an optimal tension reference value determination module 140, used to determine the optimal tension reference value based on the dynamic elastic modulus parameters and the work order ID; and a control command generation module 150, used to perform feedforward and impedance composite control quantity calculation on the optimal tension reference value and the raw running data packets to obtain motor torque control commands.

[0048] Here, those skilled in the art will understand that the specific operations of each step in the fully automatic control system of the aforementioned pre-processing flat-panel integrated machine have been referenced above. Figures 1 to 4 The fully automatic control method of the pre-processing flat-panel integrated machine is described in detail, and therefore, its repeated description will be omitted.

Claims

1. A fully automatic control method for a pre-processing flat-panel integrated machine, characterized in that, include: Obtain the original operation data packet, which includes the actual tension value at the current moment, the actual tension value at the previous moment, the motor angular velocity, the current transmission linear velocity of the fabric, the current liquid carryover rate of the fabric, and the real-time temperature of the treatment tank; and perform fabric state feature vectorization on the original operation data packet to obtain the fabric state feature vector. The dynamic modulus of the fabric state feature vector is estimated based on a rheological model to obtain the dynamic elastic modulus parameters. This includes: loading a rheological model parameter set based on the fabric type ID in the fabric state feature vector, wherein the rheological model parameter set includes dry modulus, moisture absorption coefficient, temperature rise coefficient, and reference temperature; and independently calculating the moisture and heat influence factors of the rheological model parameter set and the fabric state feature vector to obtain the moisture influence factor and temperature influence factor. Dynamic elastic modulus parameters are obtained by comprehensively solving the dry state modulus of the moisture influence factor, temperature influence factor, and rheological model parameter set. Based on the dynamic elastic modulus parameters and work order ID, the optimal tension reference value is determined. The optimal tension reference value and the original operation data package are used to solve the feedforward and impedance composite control quantity to obtain the motor torque control command.

2. The fully automatic control method for the pre-processing flat-panel integrated machine according to claim 1, characterized in that, The fabric state feature vector includes work order ID, fabric type ID, linear velocity, temperature, and liquid content.

3. The fully automatic control method for the pre-processing flat-panel integrated machine according to claim 1, characterized in that, The moisture and temperature influence factors are independently calculated on the rheological model parameter set and the fabric state feature vector to obtain the moisture influence factor and temperature influence factor. This includes: independently calculating the moisture and temperature influence factors on the rheological model parameter set and the fabric state feature vector using the following formula: ;in, Temperature is a feature vector representing the state of the fabric. As the reference temperature, For the temperature rise, This is the temperature rise influence coefficient. Temperature is a factor that affects the environment. This is the moisture absorption effect coefficient. The liquid content is the percentage of liquid carried in the fabric's state feature vector. Moisture is a factor that affects the environment.

4. The fully automatic control method for the pre-processing flat-panel integrated machine according to claim 1, characterized in that, The dynamic elastic modulus parameters are obtained by comprehensively solving the dry-state modulus of the moisture influence factor, temperature influence factor, and rheological model parameter set. This includes: comprehensively solving the dynamic elastic modulus of the dry-state modulus of the moisture influence factor, temperature influence factor, and rheological model parameter set using the following formula: ;in, For dry modulus, This refers to the dynamic elastic modulus parameter.

5. The fully automatic control method for the pre-processing flat-panel integrated machine according to claim 1, characterized in that, Determining the optimal tension baseline value based on the dynamic elastic modulus parameter and work order ID includes: loading a fabric geometry and process parameter set from the production work order parameter library based on the work order ID, wherein the fabric geometry and process parameter set includes the fabric cross-sectional area, target micro-strain, and process adjustment coefficient; calculating the ideal elastic tension baseline value by applying the dynamic elastic modulus parameter and the fabric geometry and process parameter set; and adjusting the ideal tension baseline value by applying the process adjustment coefficient in the fabric geometry and process parameter set to obtain the optimal tension baseline value.

6. The fully automatic control method for the pre-processing flat-panel integrated machine according to claim 5, characterized in that, The ideal elastic tension baseline is calculated by performing an ideal elastic tension baseline calculation on the dynamic elastic modulus parameter and the fabric geometry and process parameter set to obtain the ideal tension baseline value. This includes: calculating the ideal elastic tension baseline on the dynamic elastic modulus parameter and the fabric geometry and process parameter set using the following formula: ;in, For the target micro-strain, The cross-sectional area of ​​the fabric. This is the ideal tension baseline value.

7. The fully automatic control method for the pre-processing flat-panel integrated machine according to claim 1, characterized in that, The optimal tension reference value and the original operating data package are used to calculate the feedforward and impedance composite control quantities to obtain the motor torque control command. This includes: calculating the inertial and feedforward torque components of the motor angular velocity in the optimal tension reference value and the original operating data package to obtain the inertial torque component and the feedforward torque component; calculating the proportional term torque of the actual tension value at the current moment in the optimal tension reference value and the original operating data package to obtain the proportional compensation torque; determining the differential compensation torque based on the actual tension value at the current moment and the actual tension value at the previous moment in the original operating data package; and synthesizing the inertial torque component, the feedforward torque component, the proportional compensation torque, and the differential compensation torque to obtain the motor torque control command.

8. A fully automatic control system for a pre-processing flat-panel integrated machine, characterized in that, include: The raw operation data packet acquisition module is used to acquire the raw operation data packet, which includes the actual tension value at the current moment, the actual tension value at the previous moment, the motor angular velocity, the current transmission linear velocity of the fabric, the current liquid carrying rate of the fabric, and the real-time temperature of the processing tank; the fabric state feature extraction module is used to vectorize the raw operation data packet into fabric state features to obtain the fabric state feature vector. The elastic modulus parameter calculation module is used to perform dynamic modulus estimation based on a rheological model on the fabric state feature vector to obtain dynamic elastic modulus parameters. This includes: loading a rheological model parameter set based on the fabric type ID in the fabric state feature vector, wherein the rheological model parameter set includes dry modulus, moisture absorption coefficient, temperature rise coefficient, and reference temperature; and independently calculating the moisture and heat influence factors on the rheological model parameter set and the fabric state feature vector to obtain the moisture influence factor and temperature influence factor. The system performs a comprehensive dynamic elastic modulus calculation on the moisture influence factor, temperature influence factor, and dry modulus in the rheological model parameter set to obtain the dynamic elastic modulus parameter; the optimal tension reference value determination module is used to determine the optimal tension reference value based on the dynamic elastic modulus parameter and work order ID; the control command generation module is used to perform feedforward and impedance composite control quantity calculation on the optimal tension reference value and the original operation data package to obtain the motor torque control command.

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