A loom warp tension self-adaptive control method

Abstract

This invention relates to the field of textile machinery control, and more specifically, to a method for adaptive control of warp tension in a loom. The method includes: first, real-time sensing of the loom's current speed, acceleration, and warp beam diameter, among other multi-dimensional operating states; calculating an inertia factor based on the warp beam diameter, and then fusing this factor with the current acceleration to determine the dynamic buffer width, thus defining the parameter transition speed range; within the dynamic transition range, applying a linear weighted fusion of the proportional gain using a transition weight factor to suppress the transition zone of the integral gain; finally, inputting the processed proportional gain and integral gain into a PID controller to calculate the final control output. This invention, by constructing a dynamic transition zone and employing a parameter smoothing adjustment strategy, effectively eliminates the control output jumps and integral saturation problems caused by PID parameter step jumps, significantly improving the control stability of warp tension under variable speed conditions such as loom start-up and shutdown.

Description

Technical Field

[0001] This invention relates to the field of textile machinery control. More specifically, this invention relates to an adaptive control method for warp tension in a loom. Background Technology

[0002] During loom operation, stable control of warp tension plays a decisive role in fabric quality. As the longitudinal skeleton of the fabric, the tension of the warp yarns is crucial throughout the entire process, from warp feeding and heddle threading to weft winding and interlacing. Abnormal tension directly leads to fabric defects: excessive tension causes the warp yarns to exceed their tensile strength, resulting in breakage and weft density deviations; insufficient tension causes the warp yarns to slack, leading to heddle frame jamming, skipped patterns, and even the complete ruin of the fabric in severe cases. Therefore, precisely maintaining warp tension within the optimal range and ensuring stable fluctuations is a core prerequisite for ensuring that fabric density, texture, and strength meet standards.

[0003] During dynamic phases such as loom start-up, stopping, or speed changes, drastic speed variations significantly exacerbate warp tension fluctuations. At startup, the increased speed of the warp feed motor causes a sudden surge in warp traction; at shutdown, the reduced motor speed leads to decreased traction and warp yarn slack and accumulation; during speed changes, the change in speed causes nonlinear abrupt changes in friction between the warp and components, as well as its own inertial force, directly resulting in large-scale tension oscillations. Tension fluctuations at this stage have a more direct impact on fabric quality, becoming a major technical challenge in weaving production.

[0004] Existing technologies commonly employ gain-scheduled PID control strategies to adapt to different speed requirements. This involves first dividing the loom speed into fixed intervals, and then offline debugging of preset PID parameters based on the dynamic characteristics of each interval. However, in actual operation, when the speed crosses the interval boundary, the PID parameters instantly switch from old to new values, creating a step jump. This causes a violent jump in the controller output signal, which in turn acts on the warp motor, not only failing to suppress tension fluctuations but also becoming a new source of interference that exacerbates tension impacts, and may even lead to warp breakage and motor overload. Therefore, eliminating the abrupt step jumps in PID parameter switching and achieving a smooth transition is a pressing technical problem to be solved in the field of warp tension control for looms. Summary of the Invention

[0005] To address the problem of eliminating abrupt changes in PID parameter switching and achieving a smooth transition, this invention proposes an adaptive control method for warp tension in a loom, which includes the following steps:

[0006] Real-time sensing of the multi-dimensional state of the loom, the multi-dimensional state including at least: the current speed obtained from the loom spindle encoder, the current acceleration calculated from the current speed, and the current warp beam diameter estimated from the warp feed servo motor and the loom spindle rotation speed;

[0007] The inertia factor is calculated based on the current warp beam diameter; and a dynamic buffer width is calculated by combining the current acceleration with the inertia factor to define the speed range of the PID parameter transition as the operating conditions change.

[0008] Within the speed range of the PID parameter transition, a transition weight factor representing the current speed relative position is calculated; and based on the transition weight factor, the proportional gain is linearly weighted and fused, and the integral gain is subjected to transition region suppression processing.

[0009] The proportional gain after linear weighted fusion and the integral gain after transition region suppression are applied to the PID controller to calculate the final control output.

[0010] This invention constructs a transition zone that dynamically changes with the operating conditions by sensing multi-dimensional states in real time, and uses an asymmetric strategy to smooth the PID parameters, ultimately achieving control output. This solution fundamentally solves the problems of sudden jumps in control output and integral saturation caused by parameter steps in traditional gain-scheduled PID control, significantly improving the stability of warp tension during speed changes such as loom start-up and shutdown.

[0011] Preferably, the current acceleration calculated based on the current speed is specifically calculated by performing a first-order difference operation on the current speed; the current warp beam diameter estimated based on the warp feed servo motor and the loom spindle rotation speed is specifically estimated in real time by calculating the ratio of the encoder pulse count of the warp feed servo motor to the loom spindle rotation speed.

[0012] Preferably, the step of calculating the inertia factor based on the current warp beam diameter includes:

[0013] The normalized inertia factor is obtained by dividing the difference between the current warp beam diameter and the minimum diameter when the warp beam is empty by the difference between the initial diameter when the warp beam is full and the minimum diameter.

[0014] This invention uses a normalization operation to convert the physical diameter, which is difficult to use directly for control, into a standard control parameter, namely the inertia factor, which facilitates subsequent dynamic scheduling calculations.

[0015] Preferably, the calculation of a dynamic buffer width specifically includes:

[0016] The dynamic buffer width is obtained by multiplying the absolute value of the current acceleration, the basic acceleration buffer coefficient, and an amplification term related to the inertia factor.

[0017] This invention ensures that the buffer width is positively correlated with the severity of the system impact, thereby enabling the setting of different buffer lengths according to the operating conditions, and thus effectively improving the stability of the control.

[0018] Preferably, the amplification term related to the inertia factor is specifically: 1 plus the product of the inertia factor and the inertia influence coefficient.

[0019] This invention implements dual scheduling, which nonlinearly amplifies the dynamic buffer width when the system is at full speed or in an emergency stop, providing a more sufficient transition range for parameter smoothing.

[0020] Preferably, the calculation of the transition weighting factor characterizing the current velocity relative to the position includes:

[0021] The transition weight factor is obtained by dividing the difference between the current speed and the lower limit of the speed range by the difference between the dynamic upper limit of the speed range and the lower limit.

[0022] Wherein, the dynamic upper limit of the speed range is equal to the sum of the lower limit of the speed range and the dynamic buffer width.

[0023] Preferably, the step of linearly weighting and fusing the proportional gain according to the transition weight factor includes:

[0024] Using the aforementioned transition weighting factor, linear interpolation is performed on the proportional gain corresponding to the lower limit and the proportional gain corresponding to the upper limit of the speed range to obtain the proportional gain after linear weighted fusion.

[0025] Preferably, the transition region suppression processing of the integral gain includes:

[0026] The integral gain corresponding to the lower limit and the upper limit of the velocity range are linearly interpolated using the transition weight factor to obtain a basic integral gain; and a suppression term related to the sine function of the transition weight factor is subtracted from the basic integral gain to obtain the integral gain after the transition region suppression treatment.

[0027] Preferably, the suppression term related to the sinusoidal function of the transition weighting factor specifically comprises: a suppression coefficient and... The product of, where This refers to the transition weighting factor.

[0028] This invention utilizes the characteristic that the sine function is positive in the interval from zero to one to actively suppress the integral action in the transition region, while the suppression term is zero in the stable region, thus not affecting the steady-state accuracy.

[0029] Preferably, the suppression coefficient is half the sum of the integral gain calibration value corresponding to medium speed and the integral gain calibration value corresponding to high speed.

[0030] The present invention has the following beneficial effects:

[0031] This invention smooths the proportional gain and derivative gain, allowing them to change continuously within the transition region, thus completely eliminating parameter step problems, avoiding sudden jumps in control output, and reducing reverse impact on the feed motor.

[0032] Furthermore, this invention employs an asymmetric strategy, using a sine function to dynamically suppress integral gain within the transition region, preventing the erroneous accumulation of the integral term when the speed changes drastically, effectively avoiding integral saturation, and ensuring that the control output does not go out of control.

[0033] Furthermore, the present invention calculates the buffer width based on acceleration and inertia indices under operating conditions, which enables the system to adapt to different operating conditions and improves the applicability of the system. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating the steps of an adaptive control method for warp tension in a loom provided in an embodiment of the present invention.

[0035] Figure 2 This is a schematic diagram of the device operating status provided in an embodiment of the present invention;

[0036] Figure 3 A schematic diagram of the proportional gain provided for an embodiment of the present invention;

[0037] Figure 4 A schematic diagram of the integral gain provided for an embodiment of the present invention;

[0038] Figure 5 This is an example diagram of the control output provided in an embodiment of the present invention. Detailed Implementation

[0039] Please see Figure 1 The diagram illustrates a flowchart of an adaptive control method for warp tension in a loom, as provided in Embodiment 1. The method includes the following steps:

[0040] S1: Real-time sensing of the multi-dimensional state of the loom, the multi-dimensional state including at least: the current speed obtained from the loom spindle encoder, the current acceleration calculated from the current speed, and the current warp beam diameter estimated from the warp feed servo motor and the loom spindle rotation speed.

[0041] Specifically, the current speed is obtained in real time through the encoder of the loom spindle.

[0042] The first-order difference operation is performed on the collected current velocity time series data to calculate the current acceleration, which reflects the degree of drastic velocity change.

[0043] Using a kinematic ratio algorithm, the current warp beam diameter is obtained by performing proportional calculations based on the real-time collected loom spindle rotation speed and the number of encoder pulses of the warp feed servo motor, combined with preset machine parameters such as reduction ratio, encoder resolution, and fabric density.

[0044] Figure 1 This is a schematic diagram of the equipment's operating status, including the equipment's operating status during the start-up and shutdown phases.

[0045] S2: Calculate the inertia factor based on the current warp beam diameter; and combine the current acceleration with the inertia factor to calculate a dynamic buffer width to define the speed range of the PID parameter transition as the operating conditions change.

[0046] It should be noted that to address the parameter step problem, a transition region needs to be introduced to allow for a smooth transition within the parameter range. However, a fixed transition region cannot adapt to different operating conditions; therefore, different transition regions need to be set according to different operating conditions. For example, the system response is slow at full shaft speed, requiring a larger transition region to reduce control oscillations during parameter transitions. Similarly, the system experiences significant impact during emergency stops, also necessitating a larger transition region to reduce control oscillations during parameter transitions.

[0047] S20: Calculate the inertia factor based on the current warp beam diameter.

[0048] It should be noted that since system inertia affects system responsiveness, and thus the time required to smooth out control fluctuations, the system inertia must be analyzed first in order to set a suitable transition zone.

[0049] Preferably, as an example, calculating the inertia factor based on the current warp beam diameter includes:

[0050] The inertia factor is obtained by applying a normalization formula to the current warp beam diameter. The specific relationship is as follows:

[0051]

[0052] in, The inertia factor at the current moment; This is the current warp beam diameter; This is the initial diameter when the axis is full; for example, the initial diameter when the axis is full is 1. This is the minimum diameter when the shaft is empty. This value is a preset parameter. For example, the minimum diameter when the shaft is empty is 0.2.

[0053] Understandably, the larger the current warp beam diameter, the closer the system is to a full warp beam state, and the greater the system inertia. Conversely, the smaller the current warp beam diameter, the closer the system is to an empty warp beam state, and the smaller the system inertia.

[0054] S21: Combine the current acceleration with the inertia factor to calculate a dynamic buffer width to define the speed range of the PID parameters that change with the operating conditions.

[0055] It should be noted that since the speed fluctuations of the system and the speed itself affect the vibration intensity of the system, which in turn affects the time required for vibration to subside, the speed fluctuations of the system also need to be considered in order to set a suitable transition zone.

[0056] Preferably, as an example, and combining the current acceleration with the inertia factor, a dynamic buffer width is calculated to define the speed range of the PID parameter transition as the operating conditions change, including:

[0057] The dynamic buffer width is calculated by using the inertia factor and the current acceleration. The design is based on the principle that the buffer width is positively correlated with both acceleration and inertia.

[0058] For example, the dynamic buffer width satisfies the following relationship:

[0059]

[0060] in, This is the dynamic buffer width; The base acceleration buffer coefficient is a preset value used to balance the contribution of acceleration to the calculation of dynamic buffer width. For example, the base acceleration buffer coefficient is set to 5. This is the inertia influence coefficient, which is a preset value used to balance the contribution of the inertia factor to the calculation of the dynamic buffer width. For example, the inertia influence coefficient is set to 1.5. This is the absolute value of the current acceleration; This is the inertia factor.

[0061] Understandably, the more violent the start-stop, the greater the speed fluctuation, and the more violent the system vibration, thus requiring a longer buffer to smooth out the system fluctuation; the greater the system inertia, the slower the system response, thus requiring a longer buffer to smooth out the system fluctuation.

[0062] Obtain the termination speed of a smooth transition , It is the starting velocity of the high-speed stable region.

[0063] A dynamic transition zone was constructed that varies in real time with the current acceleration and the current warp beam diameter. ,in This is the termination speed in the medium-speed range.

[0064] The dynamic transition zone is used as the speed range for PID parameter transition.

[0065] S3: Within the speed range of the PID parameter transition, calculate the transition weight factor representing the relative position of the current speed; and perform linear weighted fusion of the proportional gain according to the transition weight factor, and perform transition region suppression processing on the integral gain.

[0066] It should be noted that in order to prevent system oscillations caused by parameter step changes, the parameters need to be smoothed in the dynamic transition range.

[0067] It should be further clarified that the effects of step changes in proportional gain and integral gain differ. A step change in proportional gain mainly causes response jitter; while a step change in integral gain can easily lead to integral saturation in dynamic processes. Therefore, different smoothing strategies must be employed for them.

[0068] Preferably, as an example, within the speed range of the PID parameter transition, a transition weight factor characterizing the relative position of the current speed is calculated; and based on the transition weight factor, the proportional gain is linearly weighted and fused, and the integral gain is subjected to transition region suppression processing, including:

[0069] First, the transition weight factor is calculated using a linear normalization formula based on the current speed's relative position within the dynamic transition zone.

[0070] For example, the transition weight factor satisfies the following relationship:

[0071]

[0072] in, Transitional weighting factor; Current speed; This is the lower limit of the dynamic transition zone; This represents the upper limit of the dynamic transition zone.

[0073] Then, using the transition weight factor, the proportional gain is processed by a linear weighted fusion formula to obtain the fused proportional gain.

[0074] It should be noted that since the proportional term in the PID algorithm changes linearly, the proportional gain can be smoothed in a linear manner.

[0075] For example, the fused proportional gain satisfies the following relationship:

[0076]

[0077] in, This represents the proportional gain after fusion. and The preset proportional gain calibration values ​​are for the medium speed zone and the high speed zone, respectively. For example, the preset proportional gain calibration values ​​for the medium speed zone and the high speed zone are 0.8 and 1.5, respectively.

[0078] It is understandable that by using the relative proportional relationship of speed in the dynamic transition region, the proportional gain is smoothed proportionally, thereby ensuring a smooth transition of the proportional gain in the dynamic transition region and preventing system oscillations caused by the switching of the proportional gain coefficient.

[0079] Figure 3 The diagram illustrates the proportional gain, with the red line representing the proportional gain of the traditional method and the green line representing the proportional gain of this scheme. The diagram shows that the proportional gain of the traditional method changes abruptly during the device startup and shutdown phases, while the proportional gain of this scheme changes smoothly.

[0080] Then, by using a transition weighting factor, the integral gain is linearly smoothed while a suppression term is added to obtain the suppressed integral gain.

[0081] It should be noted that, in order to prevent the accumulation of errors from causing integral saturation, it is necessary to suppress the accumulation of errors caused by speed fluctuations while performing linear smoothing.

[0082] For example, the suppressed integral gain satisfies the following relationship:

[0083]

[0084] in, The real-time integral gain after suppression; These are the integral gain calibration values ​​for the medium-speed and high-speed regions, respectively. For example, the integral gain calibration values ​​for the medium-speed and high-speed regions are 1.5 and 1, respectively. This is the inhibition coefficient, used to control the inhibition intensity. This value is a preset value; for example, the inhibition coefficient... .

[0085] Understandable It is a linear smoothing term of the integral gain. This is a suppression term. When or hour, This suppression term is ineffective. When hour, A positive value enables dynamic suppression. This allows for no suppression in the stable region and dynamic suppression in the transition region.

[0086] Furthermore, by using a sine function to dynamically suppress the integral effect in the transition region, error accumulation is prevented when the speed changes drastically, thus avoiding integral saturation.

[0087] Figure 4 The diagram illustrates the integral gain. The red line represents the integral gain of the traditional method, while the green line represents the integral gain of this scheme. The diagram shows that the integral gain of the traditional method exhibits abrupt changes, while the integral gain of this scheme changes smoothly. Furthermore, the integral gain of this scheme is smaller than that of the traditional method, indicating a significant suppression effect.

[0088] S4: Apply the proportional gain after linear weighted fusion and the integral gain after transition region suppression processing to the PID controller to calculate the final control output.

[0089] Preferably, as an example, the proportional gain after linear weighted fusion and the integral gain after transition region suppression are applied to the PID controller to calculate the final control output, including:

[0090] The combined proportional gain and the suppressed integral gain are substituted into the standard calculation of the PID controller to obtain the final control output, which is then sent to the servo motor for execution.

[0091] Understandably, because the proportional gain after fusion changes continuously and the integral gain after suppression is suppressed in the transition region, the final control output eliminates step jitter and avoids integral saturation, thus achieving smooth control of warp tension during high-speed start-up and shutdown of the loom.

[0092] Figure 5 The diagram illustrates the control output. The red line represents the control output of the traditional method, while the green line represents the control output of this scheme. As can be seen from the diagram, the control output of the traditional method exhibits abrupt changes, while the control output of this scheme does not.

[0093] This concludes the embodiment.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for adaptive control of warp tension in a loom, characterized in that, include: Real-time sensing of the multi-dimensional state of the loom, the multi-dimensional state including at least: the current speed obtained from the loom spindle encoder, the current acceleration calculated from the current speed, and the current warp beam diameter estimated from the warp feed servo motor and the loom spindle rotation speed; The inertia factor is calculated based on the current warp beam diameter; and a dynamic buffer width is calculated by combining the current acceleration and the inertia factor to define the speed range of the PID parameter transition as the operating conditions change, including: multiplying the absolute value of the current acceleration, the basic acceleration buffer coefficient, and an amplification term related to the inertia factor to obtain the dynamic buffer width; Within the speed range of the PID parameter transition, a transition weight factor characterizing the relative position of the current speed is calculated; and based on the transition weight factor, the proportional gain is linearly weighted and fused, including: using the transition weight factor, linearly interpolating the proportional gain corresponding to the lower limit and the proportional gain corresponding to the upper limit of the speed range to obtain the calibration values ​​of the proportional gain in the medium-speed and high-speed ranges, and obtaining the linearly weighted and fused proportional gain based on the calibration values ​​of the proportional gain in the medium-speed and high-speed ranges; and performing transition region suppression processing on the integral gain; The proportional gain after linear weighted fusion and the integral gain after transition region suppression are applied to the PID controller to calculate the final control output.

2. The adaptive control method for warp tension of a loom according to claim 1, characterized in that, The current acceleration calculated based on the current speed is specifically calculated by performing a first-order difference operation on the current speed; the current warp beam diameter estimated based on the warp feed servo motor and the loom spindle rotation speed is specifically estimated in real time by calculating the ratio of the encoder pulse count of the warp feed servo motor to the loom spindle rotation speed.

3. The adaptive control method for warp tension of a loom according to claim 1, characterized in that, The calculation of the inertia factor based on the current warp beam diameter includes: The normalized inertia factor is obtained by dividing the difference between the current warp beam diameter and the minimum diameter when the warp beam is empty by the difference between the initial diameter when the warp beam is full and the minimum diameter.

4. The adaptive control method for warp tension of a loom according to claim 3, characterized in that, The amplification term related to the inertia factor is specifically: the product of the inertia factor and the inertia influence coefficient plus 1.

5. The adaptive control method for warp tension of a loom according to claim 1, characterized in that, The calculation of the transition weighting factor characterizing the current velocity relative to the position includes: The transition weight factor is obtained by dividing the difference between the current speed and the lower limit of the speed range by the difference between the dynamic upper limit of the speed range and the lower limit. Wherein, the dynamic upper limit of the speed range is equal to the sum of the lower limit of the speed range and the dynamic buffer width.

6. The adaptive control method for warp tension of a loom according to claim 5, characterized in that, The transition region suppression processing of the integral gain includes: The integral gain corresponding to the lower limit and the upper limit of the velocity range are linearly interpolated using the transition weight factor to obtain a basic integral gain; and a suppression term related to the sine function of the transition weight factor is subtracted from the basic integral gain to obtain the integral gain after the transition region suppression treatment.

7. The adaptive control method for warp tension of a loom according to claim 6, characterized in that, The suppression term related to the sine function of the transition weighting factor specifically includes: the suppression coefficient and... The product of, where This refers to the transition weighting factor.

8. The adaptive control method for warp tension of a loom according to claim 7, characterized in that, The suppression coefficient is half the sum of the integral gain calibration value corresponding to medium speed and the integral gain calibration value corresponding to high speed.

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