Intelligent-adjustment beating-up cam control method for weaving machine
By using an intelligent adjustment weft insertion cam control method, tension signals are acquired and processed in real time to generate compensation decision values and dynamically correct the weft insertion cam trajectory. This solves the problem of dynamic mismatch between tension and cam trajectory during weaving, thereby improving fabric quality and production efficiency.
Patent Information
- Application Number
- CN202511057740.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-30
- Publication Date
- 2025-11-21
AI Technical Summary
The beat-up mechanism in high-speed looms has fixed kinematic characteristics of the beat-up cam, which cannot adapt to changes in yarn characteristics and workshop environment. This leads to a dynamic mismatch between the real-time tension of the fabric and the rigid motion trajectory of the cam, causing weaving defects such as warp breakage and shrinkage, thus limiting the improvement of weaving quality and speed.
The intelligent adjustment of the weft insertion cam control method is adopted. By acquiring the real-time tension signal, calculating the tension deviation and its rate of change, and combining the proportional gain coefficient and the differential gain coefficient, a compensation decision value is generated. This value is decoupled into the compensation phase angle and amplitude scaling factor, and the weft insertion cam trajectory is dynamically corrected to form a closed-loop adaptive control.
It achieves instantaneous compensation for each weft insertion action, effectively suppresses weaving defects, improves fabric quality and production efficiency, ensures that the weaving process converges towards the optimal state, and has high system reliability and flexibility.
Smart Images
Figure CN120989804A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of loom control technology, and more specifically to an intelligent adjustment method for controlling the beat-up cam of a loom. Background Technology
[0002] In high-speed looms, the kinematic characteristics of the core drive component, the weft-beating cam, are typically fixed. This open-loop control method cannot adapt to the dynamic changes in warp tension caused by yarn characteristics, workshop environment, and other factors during weaving. The dynamic mismatch between the real-time fabric tension and the rigid motion trajectory of the cam is a key bottleneck leading to weaving defects such as warp breakage, shrinkage, and weft skew, thus limiting the improvement of weaving quality and speed. Although existing technologies provide macroscopic control of loom tension, their response speed is far from meeting the requirements for instantaneous compensation for each weft-beating process. Summary of the Invention
[0003] The purpose of this invention is to provide an intelligent adjustment method for controlling the beat-up cam of a loom, which solves the problems existing in the background art.
[0004] To solve the above-mentioned technical problems, the present invention provides an intelligent adjustment method for controlling the beat-up cam of a loom, comprising: Acquire real-time tension signals that characterize the real-time load state of weaving; Based on the real-time tension signal and the preset target tension benchmark, the real-time tension deviation is calculated and generated. Based on the real-time tension deviation and its rate of change with time, the comprehensive compensation decision value is calculated by integrating the preset proportional gain coefficient and differential gain coefficient. The comprehensive compensation decision value is decoupled into compensation phase angle and amplitude scaling factor according to the preset decoupling mapping relationship; By combining the compensation phase angle and the amplitude scaling factor, the preset standard weft insertion cam reference trajectory is dynamically corrected to generate the actual execution trajectory after compensation.
[0005] Preferably, the target tension reference is determined through a tension-mass calibration experiment, which includes: A series of constant tension values with varying gradients were set for trial weaving; Quantitatively evaluate the fabric quality indicators of each trial weaving sample; The tension value corresponding to the optimal quality index is selected as the target tension benchmark.
[0006] Preferably, the proportional gain coefficient and the differential gain coefficient are obtained by experimental tuning on the loom.
[0007] Preferably, the experimental tuning method is the Ziegler-Nichols method, which includes: Set the differential gain coefficient to zero and gradually increase the proportional gain coefficient until the system exhibits constant-amplitude oscillations, then record the critical gain and oscillation period. Based on the recorded critical gain and oscillation period, the proportional gain coefficient and differential gain coefficient are calculated.
[0008] Preferably, the pre-defined decoupling mapping relationship is generated through system identification experiments, which include: Send a command containing a specific phase lead and amplitude scaling to the servo execution module; The actual impact of measurement instructions on fabric tension; Based on multiple sets of experimental data, a functional relationship between the comprehensive compensation decision value and the compensation phase angle and amplitude scaling factor was fitted.
[0009] Preferably, the dynamic correction step is achieved by superimposing a compensation phase angle on the standard weft-beating cam reference trajectory in the angular domain and multiplying it by a gain term determined by the amplitude scaling factor.
[0010] Preferred, including: The tension sensing module is used to acquire real-time tension signals that characterize the real-time load state of weaving. The central controller module calculates the real-time tension deviation based on the real-time tension signal and the preset target tension benchmark; calculates the comprehensive compensation decision value based on the real-time tension deviation and its rate of change with time; decouples the comprehensive compensation decision value into the compensation phase angle and amplitude scaling factor; and combines the compensation phase angle and amplitude scaling factor to dynamically correct the preset standard weft insertion cam benchmark trajectory and generate the actual execution trajectory after compensation. The servo execution module is used to receive the actual execution trajectory after compensation and drive the weft insertion mechanism to complete the weft insertion action.
[0011] Preferably, the central controller module further includes: storing preset parameters required for the execution method, the preset parameters including target tension reference, proportional gain coefficient, differential gain coefficient, decoupling mapping relationship and standard weft insertion cam reference trajectory.
[0012] Preferably, it also includes a human-computer interaction and parameter setting module: Setting and fine-tuning the target tension reference; Set and fine-tune the proportional gain coefficient and the derivative gain coefficient; And execute the calibration procedure used to determine the parameters.
[0013] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention solves the problem of dynamic mismatch between tension and cam trajectory during weaving by introducing closed-loop control. The system obtains real-time feedback that is not available in traditional open-loop control by acquiring real-time tension signals. By comparing the real-time tension with the optimal target tension reference, a quantified real-time tension deviation is generated, which provides a basis for precise control. Based on this deviation and its rate of change, the system calculates the compensation decision value, decouples it into compensation phase angle and amplitude scaling factor, and then dynamically corrects the reference trajectory of the standard weft insertion cam. This method enables each weft insertion action to accurately respond to the current load state, specifically solving the problem of dynamic mismatch between tension and cam trajectory during weaving, effectively suppressing weaving defects such as warp breakage and shrinkage caused by mismatch, thereby improving fabric quality and production efficiency. (2) This invention realizes instantaneous compensation for each weft insertion process. The target tension benchmark is determined by the tension-quality calibration experiment of the system. This experiment quantitatively evaluates the fabric quality of each sample by weaving a series of constant tension values with gradient changes, and finally selects the tension value corresponding to the optimal quality index as the target. This method ensures that the entire adaptive adjustment system always converges towards the optimal weaving state verified by science, so that the improvement of fabric quality has a scientific basis and repeatability. (3) The parameter determination methods used in this invention are all mature technologies in the field of control engineering, which ensures the reliability and operability of the system. For example, the proportional gain coefficient and the differential gain coefficient are obtained by tuning through the classic Ziegler-Nichols experiment on the loom, while the decoupling mapping relationship is generated by the system identification experiment calibration. The system architecture consisting of the tension sensing module, the central controller module and the servo execution module is clear and the responsibilities are well defined. The central controller is responsible for storing all preset parameters, while the human-machine interaction module provides operators with a convenient interface for performing calibration, setting and fine-tuning parameters, which greatly improves the practicality and flexibility of the system. Attached Figure Description
[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a logic block diagram of the system of the present invention; Figure 2 This is a logic block diagram of the tension-mass calibration experiment of the present invention; Figure 3 This is a logic block diagram of the experimental tuning method of the present invention; Figure 4This is a logic block diagram of the system identification experiment calibration of the present invention. Detailed Implementation
[0015] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0016] Example 1 Please see Figure 1 This invention provides an intelligent adjustment method for controlling a weft insertion cam on a loom, comprising: acquiring a real-time tension signal characterizing the real-time load state of weaving; calculating and generating a real-time tension deviation based on the real-time tension signal and a preset target tension reference; calculating and generating a comprehensive compensation decision value based on the real-time tension deviation and its rate of change with time, by fusing preset proportional gain coefficients and differential gain coefficients; decoupling the comprehensive compensation decision value into a compensation phase angle and an amplitude scaling factor according to a preset decoupling mapping relationship; and dynamically correcting a preset standard weft insertion cam reference trajectory by combining the compensation phase angle and the amplitude scaling factor to generate the compensated actual execution trajectory.
[0017] This embodiment aims to illustrate an intelligent adjustment method for weft insertion cam control in looms. Its core lies in constructing a complete closed-loop adaptive control link from real-time tension sensing to weft insertion motion correction. Compared to the traditional open-loop control method where the weft insertion cam's motion trajectory remains fixed, this method enables the weft insertion motion to accurately respond to dynamic fluctuations in warp tension caused by changes in yarn characteristics or the workshop environment during weaving. The fundamental motivation for this is that the dynamic mismatch between the real-time fabric tension and the rigid motion trajectory of the cam is a key bottleneck leading to weaving defects such as warp breakage and shrinkage. The implementation of this control method begins with the high-frequency acquisition of real-time tension signals, which characterize the real-time load state of the weaving, using a tension sensor installed near the weave point. The central controller module receives this signal and compares it with the preset target tension reference. Compare and calculate the real-time tension deviation. The calculation of this deviation provides a precise quantitative basis for all subsequent compensation decisions. The technical motivation behind this process lies in the fact that the difference between the current state and the ideal state must first be quantified before targeted adjustments can be made. Its mathematical model is expressed as follows:
[0018] This formula is the starting point for compensation control, where, It originates from the real-time dynamic tension signal of the tension sensor, with the physical dimension being force, such as Newton N; It is the ideal tension value preset to achieve the best quality for a specific type of fabric, and its dimension is also force; As the difference between the two, it intuitively expresses the degree and direction of the current tension state deviating from the ideal state, and its dimension is also force; Based on this real-time tension deviation Based on its rate of change with time, the controller further integrates preset proportional gain coefficients and differential gain coefficients to calculate and generate a comprehensive compensation decision value. The technical motivation for this step is that the control action should not only focus on the magnitude of the current deviation, but also anticipate the trend of deviation changes, thereby achieving rapid and stable compensation and avoiding oscillations or lags in the adjustment process. The mathematical expression of this dynamic compensation decision model is:
[0019] in, It is a dimensionless normalized comprehensive compensation decision value obtained through calculation, which unifies the intensity and direction of subsequent compensation adjustments; This is the proportional gain coefficient, whose dimension is the reciprocal of force, i.e. To ensure The term is dimensionless, which determines the system's sensitivity to the magnitude of the deviation; The differential gain coefficient has the dimension of the reciprocal of (force / time), i.e. To ensure The term is dimensionless, which determines the system's response sensitivity to the rate of change of the deviation; This is the rate of change of tension deviation with respect to time, and its dimension is force / time, i.e. This characterizes the trend of tension fluctuations.
[0020] This formula is the core link between deviation assessment and control decision-making; Comprehensive compensation decision value Based on the preset decoupling mapping relationship, it will be converted into a compensating phase angle that directly acts on the physical motion. With amplitude scaling factor The controller combines these two decoupled physical adjustment values to determine the preset standard weft insertion cam reference trajectory. Dynamic corrections are made to generate the compensated actual execution trajectory. This dynamic correction step is the final output of the entire control method. It transforms the results of a series of digital calculations into precise physical motion commands for the weft insertion mechanism. The mathematical model for this trajectory reconstruction is as follows:
[0021] in, The final compensated trajectory generated for servo execution is measured in length. It is a standard cam curve pre-designed by the loom manufacturer based on the mechanical structure, describing the relationship between the follower displacement S and the spindle rotation angle. The relationship is that its dimension is length; It is the compensation phase angle used to compensate for timing issues. The subscript 'c' indicates compensation, and the unit is radians. It is the amplitude scaling factor used to compensate for the force. The subscript A indicates the amplitude and is a dimensionless value. The logic of this formula is to adjust the timing of the weft insertion action by superimposing a compensation phase angle on the angle domain, and to adjust the intensity of the weft insertion action by scaling the entire displacement curve proportionally through an amplitude scaling factor. Through this series of interconnected steps, this method revolutionizes weft insertion control from a mechanically fixed open-loop mode to a closed-loop adaptive mode based on real-time load feedback. The ultimate effect is that each weft insertion action is no longer a rigid repetition, but a tailored response to the current yarn tension, thereby eliminating the dynamic mismatch between tension and trajectory, effectively suppressing weaving defects during high-speed weaving, and significantly improving fabric quality and production efficiency.
[0022] Example 2 Please see Figure 2 The target tension benchmark is determined through a tension-quality calibration experiment, which includes: setting a series of constant tension values with gradient changes for trial weaving; quantitatively evaluating the fabric quality indicators of each trial weaving sample; and selecting the tension value corresponding to the optimal quality indicator as the target tension benchmark.
[0023] This embodiment further clarifies the key parameter, target tension reference, in the aforementioned control method. The method of obtaining the target tension benchmark. A scientifically sound target tension benchmark is the cornerstone of whether the entire closed-loop control system can achieve the expected results; if the benchmark is not set properly, even if the subsequent control logic is precise, it will not be able to guide the weaving process towards the optimal state. This embodiment abandons the traditional approach of relying on empirical estimation and adopts a systematic tension-mass calibration experiment to determine the target tension benchmark. ; The calibration experiment proceeds as follows: First, for a specific fabric variety, a series of constant tension values with varying gradients are actively set for trial weaving. Next, using a fabric defect detection system or professional manual grading, a rigorous quality assessment is conducted on the trial weave samples produced under each tension condition. The assessment dimensions cover key fabric quality indicators such as shrinkage, warp breakage, and weft skew, and these are quantified into a comprehensive quality score. Finally, by plotting the tension-quality score relationship curve, the tension value corresponding to the highest quality score on the curve is precisely selected, and this value is used as the optimal target tension benchmark. It is permanently stored in the central controller module; The value of the target tension benchmark obtained in this way lies in the fact that it is not an abstract or universal value, but rather the optimal process parameter verified through rigorous experimental data for specific yarn and fabric specifications. This ensures that the adjustment target of the entire intelligent control system is a scientifically validated optimal solution, thus making every compensation for real-time tension deviations by the system strive towards achieving the highest fabric quality. This calibration method greatly enhances the loom's adaptability to different yarn raw materials, enabling the rapid and accurate setting of core process parameters when changing production varieties, significantly shortening the debugging cycle.
[0024] Example 3 Please see Figure 3 The proportional gain coefficient and the differential gain coefficient were obtained through experimental tuning on the loom.
[0025] The experimental tuning method is the Ziegler-Nichols method, which includes: setting the differential gain coefficient to zero and gradually increasing the proportional gain coefficient until the system exhibits constant-amplitude oscillations, and recording the critical gain and oscillation period; and calculating the proportional gain coefficient and differential gain coefficient based on the recorded critical gain and oscillation period. This embodiment focuses on two core parameters in the dynamic compensation generation step: the proportional gain coefficient. and differential gain coefficient The values of these two parameters directly determine the dynamic performance of the control system, namely the speed and stability of the response. Inappropriate parameters can lead to sluggish system response, excessive oscillation, or even instability. To ensure control effectiveness, this embodiment clarifies that these two coefficients are not obtained through theoretical calculations or repeated trial and error, but must be obtained through experimental tuning on an actual loom, and specifies the classic Ziegler-Nichols engineering tuning method. The execution logic of the experimental tuning method is clear and systematic. Its operation process is as follows: first, the differential gain coefficient is... The system is temporarily set to zero, operating in pure proportional control mode. The operator then gradually increases the proportional gain coefficient, starting from a small value. Simultaneously, closely observe the response curve of the tension signal until the system reaches a critical steady state after being subjected to a step disturbance, i.e., it produces continuous constant-amplitude oscillations. At this point, record the corresponding proportional gain coefficient, which is the critical gain. And measure the period of oscillation, which is the oscillation period. ; After obtaining these two key experimental data points, recommended parameter settings can be calculated using the empirical formulas provided by the Ziegler-Nichols method. For example, for a PD controller, the following can be used: and Calculate according to the rules and The initial setting value; The technical value of this method lies in providing a standardized and reproducible engineering acquisition process for seemingly abstract control parameters, ensuring that the control system can achieve the best balance between response speed and stability on actual loom hardware. The control system built with these tuned parameters can not only respond rapidly to tension fluctuations, but also effectively suppress overshoot and oscillation during the adjustment process. This is crucial for modern weaving applications that require both high speed and high precision.
[0026] Example 4 Please see Figure 4 The preset decoupling mapping relationship is generated through system identification experiment calibration. The experiment includes: issuing a command containing a specific phase lead and amplitude scaling to the servo execution module; measuring the actual impact of the command on the fabric tension; and fitting the functional relationship between the comprehensive compensation decision value and the compensation phase angle and amplitude scaling factor based on multiple sets of experimental data. This embodiment details how to establish an abstract comprehensive compensation decision value. To compensate phase angle for specific physical execution quantities With amplitude scaling factor The precise conversion mechanism between them, i.e., the pre-defined decoupling mapping relationship and This mapping relationship is the key to achieving effective control because it must accurately reflect the inherent relationship between control commands and mechanical system responses. If this mapping is inaccurate, even if the controller makes the correct decision, it cannot be translated into the expected physical action. Therefore, this embodiment employs a system identification experiment to calibrate the generation of this decoupling mapping relationship. During the experiment, technicians proactively issue a series of carefully designed test commands to the servo execution module. These commands contain a set of specific phase lead values. and amplitude scaling For each set of commands issued, the system uses the tension sensing module to synchronously and precisely measure the actual impact of that command combination on the fabric tension, i.e., the response result; By executing multiple sets of such input-output tests, the system accumulates a large amount of experimental data. Based on this data, methods such as mathematical fitting or machine learning can be used to construct and calibrate a functional relationship that accurately describes this intrinsic correlation. and For example, experimental data may reveal that when a positive tension deviation needs to be compensated, the system requires a positive compensation phase angle. A negatively correlated magnitude scaling factor ; This calibration method based on system identification experiments has the advantage of ensuring that the transformation from control decisions to physical execution is based on the specific dynamic characteristics of the current loom, rather than a general theoretical model. This makes the decoupling process extremely precise, guaranteeing that a given... The value can reliably and reproducibly generate the desired composite adjustment of weft insertion timing and intensity, thereby enabling the entire closed-loop compensation strategy to be truly implemented and effective, achieving precise control.
[0027] Example 5 The dynamic correction step is achieved by superimposing a compensation phase angle on the standard weft-beating cam reference trajectory in the angular domain and multiplying it by a gain term determined by the amplitude scaling factor.
[0028] This embodiment illustrates the final reconstructing and execution steps of the weft insertion trajectory. This step is the endpoint of the entire intelligent control process, materializing the results of all preceding perceptions, evaluations, and decisions into a final motion trajectory command for the weft insertion cam. This forms the connection between the control algorithm and mechanical execution; Specifically, the core of the dynamic correction step lies in a trajectory synthesis formula: The mechanism of this formula lies in using two independent dimensions to define the reference trajectory of the standard weft insertion cam. Synchronous corrections were implemented, the first dimension being time or phase correction, achieved through the angle variable of the standard trajectory. Directly superimposed compensation phase angle This achieves a translation of the entire motion curve along the time axis, effectively advancing or delaying the weft insertion action to match the optimal timing. The second dimension is the correction of force or amplitude, achieved by multiplying the entire standard trajectory by an amplitude scaling factor. Determined gain term This achieves proportional magnification or reduction of the overall amplitude of the motion curve, which in turn enhances or weakens the force of the weft insertion. The application of this method precisely translates the intelligent decisions of the controller into the physical behavior of the mechanical system. Through dual, real-time correction of the reference trajectory in both the angle and amplitude domains, the servo execution module no longer receives uniform motion commands, but rather an optimized execution trajectory tailored to the current weaving cycle. This online trajectory synthesis capability enables the loom to fine-tune the force and timing of each weft insertion with unprecedented flexibility and precision, thereby directly and efficiently offsetting real-time tension fluctuations and ultimately achieving the fundamental goal of improving fabric quality and production efficiency.
[0029] Example 6 A smart adjustable weft-beating cam control system for a loom includes: a tension sensing module for acquiring a real-time tension signal characterizing the real-time load state of weaving; a central controller module for calculating the real-time tension deviation based on the real-time tension signal and a preset target tension reference; calculating a comprehensive compensation decision value based on the real-time tension deviation and its rate of change with time; decoupling the comprehensive compensation decision value into a compensation phase angle and an amplitude scaling factor; and dynamically correcting a preset standard weft-beating cam reference trajectory by combining the compensation phase angle and the amplitude scaling factor to generate a compensated actual execution trajectory; and a servo execution module for receiving the compensated actual execution trajectory and driving the weft-beating mechanism to complete the weft-beating action.
[0030] This embodiment describes the physical system carrier for implementing the aforementioned control method. The system consists of three core modules, each performing its own function and working closely together to form a complete closed-loop control hardware architecture. As the sensing unit of the system, the tension sensing module typically consists of a high-precision tension sensor and its signal conditioning circuit, installed at a key location near the weave point. Its function is to capture the dynamic tension changes of the warp yarns in real time and accurately, converting this most basic physical quantity, which characterizes the real-time load state of the weaving, into an electrical signal to provide decision-making basis for the entire control system. The central controller module is the core of the entire system's computation, typically a high-performance industrial-grade processor. It receives signals from the tension sensing module and is responsible for executing all the core calculations described above, including: calculating real-time tension deviation, generating comprehensive compensation decision values, performing decoupling mapping, and finally reconstructing the compensated actual execution trajectory. All complex control logic and algorithms are completed within this module. The servo actuator module is the physical actuator of the system, consisting of a high-response servo motor, a driver, and connected weft insertion mechanism. It executes instructions from the central controller module and receives the compensated actual execution trajectory. It drives the weft insertion mechanism with extremely high speed and precision to complete each weft insertion action after intelligent compensation. The collaborative work of these three modules effectively combines information flow and energy flow. Information flows from the fabric end to the control end, is intelligently processed, and then flows to the drive end in the form of control commands, ultimately acting back on the fabric in the form of physical actions. This architecture forms an efficient closed loop, enabling the system to adaptively adjust to the weaving process, completely changing the rigid, open-loop control mode of traditional looms.
[0031] Example 7 The central controller module also includes: storing preset parameters required for the execution method, including target tension reference, proportional gain coefficient, differential gain coefficient, decoupling mapping relationship, and standard weft insertion cam reference trajectory.
[0032] This embodiment further defines another key function of the central controller module: as a storage function for the system knowledge base, in addition to performing real-time high-speed calculations, the non-volatile storage unit inside the central controller module is also responsible for permanently storing all preset parameters and reference data necessary for executing the aforementioned intelligent control method; These stored preset parameters constitute a complete process and control model, specifically including: the target tension reference determined through tension-mass calibration experiments. The proportional gain coefficient obtained through experimental tuning and differential gain coefficient ; The decoupling mapping relationship function calibrated by the system is identified experimentally. and ; and the standard beat-up cam reference track provided by the loom manufacturer. ; Centralizing the storage of these key parameters in the central controller module offers several technological advantages. It ensures the efficiency and consistency of the control algorithm when invoking these parameters. It digitizes and integrates all calibrated process parameters and control models obtained through extensive prior experiments and calibration, making them an integral part of the equipment's inherent capabilities. This configuration greatly facilitates the configuration, replication, and management of the entire system, and guarantees the consistency and reliability of the equipment's behavior across different production times and batches, thus constructing a fully functional and robust intelligent control core.
[0033] Example 8 Human-computer interaction and parameter setting module: setting and fine-tuning the target tension reference; setting and fine-tuning the proportional gain coefficient and differential gain coefficient; and executing the calibration procedure for determining the parameters.
[0034] This embodiment adds a crucial functional module to the entire intelligent control system: the human-machine interaction and parameter setting module. This module is typically represented by a touchscreen or an external computer interface, and it constitutes the interface for operators to interact with, set, and monitor the automation system. The core function of this module is to empower qualified technicians with the ability to perform in-depth configuration and optimization of the system. Operators can use this module to execute calibration procedures to determine various key parameters. For example, they can initiate and guide the system to complete the aforementioned tension-mass calibration experiment to determine the optimal target tension benchmark for a specific fabric. Similarly, they can also use this module to enter parameter tuning mode and perform experiments to set and fine-tune the proportional gain coefficient. and differential gain coefficient To adapt to different operating conditions; The existence of this human-machine interaction and parameter setting module greatly enhances the practicality and flexibility of the entire system. It avoids turning the control system into a rigid and untraceable unit, instead providing operators with a transparent and controllable interface. Through this interface, factory technical experts can combine their process experience with advanced automation technology to finely adjust and optimize the system. This human-machine collaboration ensures that intelligent control technology is not only theoretically advanced but also easy to deploy and maintain in actual production, continuously creating value and maximizing its potential to improve fabric quality and production efficiency. The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.
Claims
1. A method for controlling an intelligently adjustable beat-up cam on a loom, characterized in that, include: Acquire real-time tension signals that characterize the real-time load state of weaving; Based on the real-time tension signal and the preset target tension benchmark, the real-time tension deviation is calculated and generated. Based on the real-time tension deviation and its rate of change with time, the comprehensive compensation decision value is calculated by integrating the preset proportional gain coefficient and differential gain coefficient. The comprehensive compensation decision value is decoupled into compensation phase angle and amplitude scaling factor according to the preset decoupling mapping relationship; By combining the compensation phase angle and the amplitude scaling factor, the preset standard weft insertion cam reference trajectory is dynamically corrected to generate the actual execution trajectory after compensation.
2. The method according to claim 1, characterized in that, The target tension reference is determined through a tension-mass calibration experiment, which includes: A series of constant tension values with varying gradients were set for trial weaving; Quantitatively evaluate the fabric quality indicators of each trial weaving sample; The tension value corresponding to the optimal quality index is selected as the target tension benchmark.
3. The method according to claim 1, characterized in that, The proportional gain coefficient and the differential gain coefficient were obtained through experimental tuning on the loom.
4. The method according to claim 3, characterized in that, The experimental tuning method is the Ziegler-Nichols method, which includes: Set the differential gain coefficient to zero and gradually increase the proportional gain coefficient until the system exhibits constant-amplitude oscillations, then record the critical gain and oscillation period. Based on the recorded critical gain and oscillation period, the proportional gain coefficient and differential gain coefficient are calculated.
5. The method according to claim 1, characterized in that, The pre-defined decoupling mapping relationship was generated through system identification experiments, which included: Send a command containing a specific phase lead and amplitude scaling to the servo execution module; The actual impact of measurement instructions on fabric tension; Based on multiple sets of experimental data, a functional relationship between the comprehensive compensation decision value and the compensation phase angle and amplitude scaling factor was fitted.
6. The method according to claim 1, characterized in that, The dynamic correction step is achieved by superimposing a compensation phase angle on the standard weft-beating cam reference trajectory in the angular domain and multiplying it by a gain term determined by the amplitude scaling factor.
7. A smart adjustable weft insertion cam control system for a loom, characterized in that, include: The tension sensing module is used to acquire real-time tension signals that characterize the real-time load state of weaving. The central controller module calculates the real-time tension deviation based on the real-time tension signal and the preset target tension benchmark. Based on the real-time tension deviation and its rate of change with time, the comprehensive compensation decision value is calculated; the comprehensive compensation decision value is decoupled into the compensation phase angle and the amplitude scaling factor. In addition, by combining the compensation phase angle and the amplitude scaling factor, the preset standard weft insertion cam reference trajectory is dynamically corrected to generate the actual execution trajectory after compensation. The servo execution module is used to receive the actual execution trajectory after compensation and drive the weft insertion mechanism to complete the weft insertion action.
8. The system according to claim 7, characterized in that, The central controller module also includes: storing preset parameters required for the execution method, including target tension reference, proportional gain coefficient, differential gain coefficient, decoupling mapping relationship, and standard weft insertion cam reference trajectory.
9. The system according to claim 7, characterized in that, It also includes a human-computer interaction and parameter setting module: Setting and fine-tuning the target tension reference; Set and fine-tune the proportional gain coefficient and the derivative gain coefficient; And execute the calibration procedure used to determine the parameters.