A tension control method and system in an automatic yarn splicing system for a dual-arm robot

Abstract

This invention relates to the field of automated control technology for textile machinery, specifically to a tension control method and system in an automatic yarn splicing system for a dual-arm robot. The method first acquires real-time yarn tension signals and yarn image data. Based on the image data, it calculates the yarn flow channel blockage rate and, according to this blockage rate and a preset aerodynamic interference model, calculates the aerodynamic interference force. Then, it decouples this interference force from the real-time tension signal to obtain pure mechanical tension. Subsequently, based on the pure mechanical tension, it calculates the tension error and its rate of change, and calculates the real-time variable damping coefficient and real-time variable stiffness coefficient based on this error and rate of change. Finally, based on the tension error, the real-time variable damping coefficient, and the real-time variable stiffness coefficient, it calculates the current moment's robotic arm end-effector position correction command using a preset discrete control law. This invention constitutes a precise, fast, and compliant tension control system, significantly improving the success rate and reliability of automatic splicing operations.

Description

Technical Field

[0001] This invention relates to the field of textile machinery automation control technology, specifically to a tension control method and system in a dual-arm robot yarn automatic splicing system. Background Technology

[0002] Yarn breakage is a common problem in ring spinning, and automated splicing robots are gradually replacing manual splicing tasks. Maintaining appropriate yarn tension is crucial for successful splicing during yarn traction and threading using a robotic arm. Current technologies typically rely on single-dimensional force sensors to detect yarn tension. However, in complex dynamic airflow environments, force sensor readings are severely affected by changes in airflow load within the yarn channel, leading to distorted tension signals that fail to reflect the true mechanical force between the yarn and the robotic arm. Furthermore, traditional control algorithms often exhibit response lag when facing sudden tension changes due to the inconsistency between the dimensions of force and velocity, failing to provide a rapid and gentle buffering response at the moment the yarn is about to break. Additionally, if the system stiffness switches directly to a high value when the protection mechanism is triggered, significant mechanical rebound impact can occur, potentially causing secondary damage or breakage of the yarn. These technical deficiencies collectively affect the reliability, stability, and efficiency of automated splicing systems. Summary of the Invention

[0003] To address the shortcomings of existing methods and the needs of practical applications, and in order to solve the aforementioned problems, this invention provides a tension control method in a dual-arm robot automatic yarn splicing system, comprising the following steps: The system acquires real-time yarn tension signals and yarn image data at the current moment. Based on the yarn image data, it calculates the blockage rate, which characterizes the degree of blockage in the yarn flow channel. Based on the blockage rate and a preset aerodynamic interference model, it calculates the corresponding aerodynamic interference force. It decouples the aerodynamic interference force from the real-time yarn tension signal to obtain pure mechanical tension. Based on the pure mechanical tension, it calculates the tension error and its rate of change. Based on the tension error and its rate of change, it calculates the real-time variable damping coefficient and the real-time variable stiffness coefficient. Based on the tension error, the real-time variable damping coefficient, and the real-time variable stiffness coefficient, it calculates the current moment's robotic arm end-effector position correction command using a preset discrete control law.

[0004] Optionally, calculating the blockage rate, which characterizes the degree of blockage in the yarn flow channel, based on the yarn image data includes the following steps: The yarn image data is segmented into cross-sectional regions to obtain yarn cross-sectional regions; the blockage rate is calculated based on the pixel area of ​​the yarn cross-sectional regions and the fixed pixel area of ​​the total cross-section of the flow channel.

[0005] Optionally, the preset aerodynamic disturbance model is a piecewise nonlinear model that satisfies: in, The aerodynamic interference force, Based on input air pressure The reference aerodynamic forces to be queried To the blocking rate Related nonlinear correction coefficients.

[0006] Optionally, the nonlinear correction coefficient The calculation rules are as follows: when hour, ; when hour, ; in, The preset critical blocking rate threshold, , , These are the preset model parameters.

[0007] Optionally, calculating the real-time variable damping coefficient based on the tension error and the rate of change of the tension error includes the following steps: Based on the tension error and the rate of change of the tension error, a normalized dimensionless state vector is constructed; the distance weights between the dimensionless state vector and multiple preset anchor points are calculated, and based on each distance weight and the preset damping value corresponding to each anchor point.

[0008] Optionally, calculating the real-time variable stiffness coefficient based on the tension error and the rate of change of the tension error includes the following steps: Based on the tension error, the current cumulative position correction, and the preset safety threshold, a comprehensive safety index is calculated; the comprehensive safety index is then input into a preset continuous function to calculate the real-time variable stiffness coefficient.

[0009] Optionally, the preset continuous function is a Sigmoid function, and the real-time variable stiffness coefficient... satisfy: in, As a preset transition factor, Indicates the weak stiffness of the foundation. Indicates the safety lock-up stiffness. This indicates the overall safety indicators.

[0010] Optionally, the preset discrete control law is a semi-implicit forward Euler control law based on virtual inertia and sampling period, satisfying: , in, Indicates the current moment. Indicates the previous moment, Represents virtual inertia. Indicates the sampling period. The tension error at the current moment, The real-time variable damping coefficient is the value at the current moment. The real-time variable stiffness coefficient is the value at the current moment. The calculated speed correction command for the current moment. The command is a correction instruction for the current position of the robotic arm.

[0011] Optionally, it also includes system parameter configuration constraints, which require the sampling period of the discrete control system to be... Virtual inertia Maximum damping and maximum stiffness The following conditions must be met: and in, This is the maximum value that the real-time variable damping coefficient can achieve. This is the maximum value that the real-time variable stiffness coefficient can achieve.

[0012] Secondly, to efficiently execute the tension control method in the automatic yarn splicing system of a dual-arm robot provided by this invention, this invention also provides a tension control system in the automatic yarn splicing system of a dual-arm robot, comprising: an input device, an output device, a processor, and a memory, wherein the input device, output device, processor, and memory are interconnected, and the memory stores program instructions used for the tension control method in the automatic yarn splicing system of a dual-arm robot. The tension control system in the automatic yarn splicing system of a dual-arm robot of this invention has a compact structure and stable performance, and can stably execute the tension control method in the automatic yarn splicing system of a dual-arm robot provided by this invention, further improving the overall applicability and practical application capability of this invention.

[0013] The beneficial effects of this invention are as follows: First, by combining yarn image data with a preset aerodynamic interference model, airflow load interference can be accurately decoupled from sensor signals to obtain pure mechanical tension, thereby directly solving the problem of tension observation distortion in the prior art and providing accurate input for subsequent control. Second, by normalizing the tension error and its rate of change to a dimensionless state space and adaptively adjusting the damping coefficient based on the similarity between the current state and the preset ideal working condition anchor point, a unified representation and processing of physical quantities with different dimensions is achieved. This enables the controller to make a millisecond-level ultra-fast response to the impact precursor of drastic tension changes, and effectively overcomes the problem of response lag through high damping for flexible buffering. Third, by fusing position and tension information to calculate a comprehensive safety index and using a continuous function to smoothly adjust the stiffness coefficient, the system stiffness can smoothly transition from the base value to the safe value when the tension or position approaches the safety threshold, eliminating the mechanical rebound impact caused by sudden stiffness changes, achieving a smooth transition of protective actions, and avoiding secondary damage. These effects together constitute a precise, fast, and compliant tension control system, significantly improving the success rate and reliability of automatic splicing operations. Attached Figure Description

[0014] Figure 1 A flowchart of a tension control method in a dual-arm robot yarn automatic splicing system provided in an embodiment of the present invention; Figure 2 This is a framework diagram of a tension control system in an automatic yarn splicing system for a dual-arm robot, provided as an embodiment of the present invention. Detailed Implementation

[0015] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0016] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0017] Please see Figure 1 To address the above problems, this invention provides a tension control method in a dual-arm robot automatic yarn splicing system, such as... Figure 1 As shown, in one embodiment, the method includes the following steps: S1. Obtain the real-time yarn tension signal and yarn image data at the current moment, and calculate the blockage rate, which characterizes the degree of blockage in the yarn flow channel, based on the yarn image data.

[0018] First, the real-time yarn tension signal and yarn image data are acquired. The real-time yarn tension signal is collected by a force sensor installed on the yarn path, and the yarn image data is acquired by an industrial camera aligned with the cross-section of the yarn flow channel. Based on the yarn image data, the blockage rate, which characterizes the degree of blockage in the yarn flow channel, is calculated. This includes the following sub-steps: The yarn image data is segmented into a cross-sectional region, for example, by defining a region of interest (ROI) and using the Otsu adaptive thresholding algorithm to separate the yarn cross-section from the background, obtaining the yarn cross-section region; then, the pixel area of ​​this yarn cross-section region is used as the basis for the calculation. With a known total cross-section of the flow channel and a fixed pixel area Calculate the blocking rate The calculation formula is: .

[0019] S2. Based on the blockage rate and the preset aerodynamic interference model, calculate the corresponding aerodynamic interference force.

[0020] In this embodiment, the preset aerodynamic disturbance model is a piecewise nonlinear model, which is expressed as: .in, For aerodynamic interference force, Based on input air pressure The reference aerodynamic force can be obtained by changing the input air pressure under no-load (no yarn) conditions. It also records force sensor readings for offline calibration and establishes a lookup table; To be related to blocking rate Related nonlinear correction coefficients.

[0021] For nonlinear correction coefficients In one specific embodiment, the calculation rule follows: when the blocking rate satisfies hour, When the blocking rate meets hour, .in, The preset critical blockage rate threshold is usually set between 0.6 and 0.7, representing the turning point where aerodynamic forces reach their peak. , , The model parameters are preset and can be obtained by simulating different blockage rates in the flow channel using a standard cylinder and recording the corresponding aerodynamic changes for fitting. This segmented model can accurately reflect the physical phenomenon that the airflow is enhanced when the flow channel is partially blocked and attenuated when it is excessively blocked.

[0022] S3. Decouple the aerodynamic interference force from the real-time tension signal of the yarn to obtain the pure mechanical tension, and calculate the tension error and its rate of change based on the pure mechanical tension.

[0023] The calculated aerodynamic interference force is directly subtracted from the real-time yarn tension signal. Pure mechanical tension can then be obtained. ,Right now .

[0024] Based on the obtained pure mechanical tension and the preset target tension Calculate tension error and its rate of change The rate of change can be obtained through difference calculation, for example... .

[0025] S4. Calculate the real-time variable damping coefficient and the real-time variable stiffness coefficient based on the tension error and the rate of change of the tension error.

[0026] Calculating the real-time variable damping coefficient includes the following sub-steps: First, construct a normalized dimensionless state vector. Specifically, the maximum permissible tension error is preset. and the maximum permissible rate of change of tension error The dimensionless state vector is constructed as follows: .

[0027] This step unifies errors and error rates of change with different physical dimensions onto the same scale, solving the problem that they cannot be directly calculated using Euclidean distance.

[0028] Next, the dimensionless state vector is calculated. With multiple preset anchor points Distance weights between .

[0029] Distance weight The calculation formula is: ,in The preset kernel width parameter is used to adjust the smoothness and sensitivity of state switching.

[0030] Then, based on the distance weights and each anchor point Corresponding preset damping value The real-time variable damping coefficient is calculated by weighted average. ,satisfy: .

[0031] In a preferred embodiment, a plurality of preset anchor points Includes: First anchor point Its coordinates are, for example, This corresponds to the tension relaxation state (the error is positive and the change is gradual), and is associated with the first preset damping value. This value is small to allow the robotic arm to pull back quickly; second anchor point Its coordinates are, for example, This corresponds to the tension impact state (the error is negative and rapidly increases in the negative direction), and is associated with the second preset damping value. ,in High damping is used to quickly absorb impact energy; third anchor point Its coordinates are, for example, This corresponds to the steady state of tension and is associated with the third preset damping value. ,in This provides appropriate damping to maintain stability. The coordinates of these anchor points can be obtained by offline acquisition of actual operational data (such as relaxation and breakage processes) and subsequent cluster analysis.

[0032] Calculating the real-time variable stiffness coefficient includes the following sub-steps: First, based on tension error Current cumulative position correction amount And preset safety thresholds, to calculate comprehensive safety indicators Comprehensive safety indicators The calculation formula is: ,in, This is the current cumulative position correction amount. Preset position safety boundaries to prevent the robotic arm from overtraveling; For pure mechanical tension, The preset tension safety threshold (e.g., 80% of the yarn breaking strength). It is a very small positive number, used to prevent the denominator from being zero.

[0033] This indicator integrates two risks: position exceeding limits and tension exceeding limits, taking the larger of the two values ​​as the dominant risk. Then, the comprehensive safety indicators... The input is given to a preset continuous function, and the real-time variable stiffness coefficient is calculated. The range of this continuous function is within the preset base stiffness. With preset safety stiffness between.

[0034] In one specific embodiment, the preset continuous function is the Sigmoid function, and the real-time variable stiffness coefficient is... The calculation formula is: .in, The preset transition factor controls the stiffness from the base value. (e.g., 20 N / m) transition to a safe value (e.g., 800 N / m) smoothness and speed.

[0035] when At that time, the system is in the safe zone and the stiffness is close to... The system is compliant; when When the system approaches or exceeds the safety boundary, the stiffness increases smoothly to... This creates a "flexible virtual wall" to limit further movement and avoids mechanical rebound impact caused by a sudden change in stiffness.

[0036] S5. Based on the tension error, the real-time variable damping coefficient, and the real-time variable stiffness coefficient, calculate the current position correction command of the robotic arm end effector using a preset discrete control law.

[0037] In this embodiment, the preset discrete control law is a semi-implicit forward Euler control law based on virtual inertia and sampling period, satisfying: , in, Indicates the current moment. Indicates the previous moment, Represents virtual inertia. Indicates the sampling period. The tension error at the current moment, The real-time variable damping coefficient is the value at the current moment. The real-time variable stiffness coefficient is the value at the current moment. The calculated speed correction command for the current moment. The command is a correction instruction for the current position of the robotic arm.

[0038] This control law implements a virtual mass-damped-spring (impedance) model in the discrete domain, and its parameters... and It changes in real time, thus dynamically adjusting the dynamic characteristics of the robotic arm's end effector.

[0039] To ensure the global stability of the discrete control system under real-time parameter changes, system parameter configuration constraints are also set, requiring the sampling period of the discrete control system to be... Virtual inertia Maximum damping and maximum stiffness The following conditions must be met: in, This represents the maximum value that the real-time variable damping coefficient can achieve. This represents the maximum value that the real-time variable stiffness coefficient can achieve. For example, if we take... , , , Substitute and verify: and The conditions were met, ensuring the absolute stability of the system within a 1ms control cycle.

[0040] It should be noted that the specific implementation methods described above, such as image processing, numerical simulation, and the construction and training of machine learning models, can all be accomplished by the processor by calling the corresponding computer program instructions stored in memory. Those skilled in the art can implement the above functions using algorithms and tools known in the prior art, according to actual needs.

[0041] Please see Figure 2 In an embodiment, to efficiently execute the tension control method in the automatic yarn splicing system of a dual-arm robot provided by the present invention, the present invention also provides a tension control system in the automatic yarn splicing system of a dual-arm robot, comprising: an input device 1, an output device 2, a processor 3, and a memory 4, wherein the input device 1, output device 2, processor 3, and memory 4 are interconnected, and the memory 4 stores program instructions for executing the steps of the tension control method in the automatic yarn splicing system of the dual-arm robot. The tension control system in the automatic yarn splicing system of the dual-arm robot of the present invention has a compact structure and stable performance, and can stably execute the tension control method in the automatic yarn splicing system of the dual-arm robot of the present invention, further improving the overall applicability and practical application capability of the present invention.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention.

Claims

1. A tension control method in a dual-arm robot automatic yarn splicing system, characterized in that, Includes the following steps: Acquire the real-time yarn tension signal and yarn image data at the current moment, and calculate the blockage rate, which characterizes the degree of blockage in the yarn flow channel, based on the yarn image data; Based on the blockage rate and the preset aerodynamic interference model, the corresponding aerodynamic interference force is calculated. The aerodynamic interference force is decoupled from the real-time tension signal of the yarn to obtain the pure mechanical tension. Based on the pure mechanical tension, the tension error and its rate of change are calculated. Calculate the real-time variable damping coefficient and the real-time variable stiffness coefficient based on the tension error and the rate of change of the tension error. Based on the tension error, the real-time variable damping coefficient, and the real-time variable stiffness coefficient, the current position correction command of the robotic arm end effector is calculated using a preset discrete control law.

2. The tension control method in the dual-arm robot yarn automatic splicing system according to claim 1, characterized in that, The calculation of the blockage rate, which characterizes the degree of blockage in the yarn flow channel, based on the yarn image data includes the following steps: The yarn image data is segmented into cross-sectional regions to obtain the yarn cross-sectional regions; The blocking rate is calculated based on the pixel area of ​​the yarn cross-section region and the fixed pixel area of ​​the total cross-section of the flow channel.

3. The tension control method in the dual-arm robot yarn automatic splicing system according to claim 1, characterized in that, The preset aerodynamic disturbance model is a piecewise nonlinear model that satisfies: in, The aerodynamic interference force, Based on input air pressure The reference aerodynamic forces to be queried To the blocking rate Related nonlinear correction coefficients.

4. The tension control method in the dual-arm robot yarn automatic splicing system according to claim 3, characterized in that, The nonlinear correction coefficient The calculation rules are as follows: when hour, ; when hour, ; in, The preset critical blocking rate threshold, , , These are the preset model parameters.

5. The tension control method in the dual-arm robot yarn automatic splicing system according to claim 1, characterized in that, The step of calculating the real-time variable damping coefficient based on the tension error and the rate of change of the tension error includes the following steps: Based on the tension error and the rate of change of the tension error, a normalized dimensionless state vector is constructed. Calculate the distance weights between the dimensionless state vector and multiple preset anchor points, based on each distance weight and the preset damping value corresponding to each anchor point.

6. The tension control method in the dual-arm robot yarn automatic splicing system according to claim 1, characterized in that, The step of calculating the real-time variable stiffness coefficient based on the tension error and the rate of change of the tension error includes the following steps: Based on the tension error, the current cumulative position correction amount, and the preset safety threshold, a comprehensive safety index is calculated. The comprehensive safety index is input into a preset continuous function to calculate the real-time variable stiffness coefficient.

7. The tension control method in the dual-arm robot yarn automatic splicing system according to claim 6, characterized in that, The preset continuous function is the Sigmoid function, and the real-time variable stiffness coefficient... satisfy: in, As a preset transition factor, Indicates the weak stiffness of the foundation. Indicates the safety lock-up stiffness. This indicates the overall safety indicators.

8. The tension control method in the dual-arm robot yarn automatic splicing system according to claim 1, characterized in that, The preset discrete control law is a semi-implicit forward Euler control law based on virtual inertia and sampling period, satisfying: ， in, Indicates the current moment. Indicates the previous moment, Represents virtual inertia. Indicates the sampling period. The tension error at the current moment, The real-time variable damping coefficient is the value at the current moment. The real-time variable stiffness coefficient is the value at the current moment. The calculated speed correction command for the current moment. The command is a correction instruction for the current position of the robotic arm.

9. The tension control method in the dual-arm robot yarn automatic splicing system according to claim 1, characterized in that, It also includes system parameter configuration constraints, which require the sampling period of the discrete control system. Virtual inertia Maximum damping and maximum stiffness The following conditions must be met: and in, This is the maximum value that the real-time variable damping coefficient can achieve. This is the maximum value that the real-time variable stiffness coefficient can achieve.

10. A tension control system in a dual-arm robot yarn automatic splicing system, characterized in that, The tension control system in the dual-arm robot automatic yarn splicing system includes: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory stores program instructions, which are used to execute the tension control method in the dual-arm robot automatic yarn splicing system according to any one of claims 1-9.

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