A tension self-adapting float roll control method and mechanism

Abstract

The application discloses a tension self-adaptive float roller control method, comprising the following steps: obtaining state data of a float roller; obtaining control error data of the float roller according to the state data of the float roller; using a fuzzy algorithm to obtain an adjustment coefficient according to the control error data of the float roller; using a PID controller to generate a float roller control signal according to the adjustment coefficient; and stably pulling the float roller according to the float roller control signal. The tension self-adaptive float roller control method actively measures the position and / or tension information of the float roller, and outputs the control signal of the float roller according to the position and / or tension information, so as to compensate the nonlinear tension change of the float roller in real time, and keep the tension of the float roller stable during system operation.

Description

Technical Field

[0001] This invention belongs to the field of tension control, specifically relating to a tension-adaptive floating roller control method and mechanism. Background Technology

[0002] Precise and stable tension control is crucial for improving foil production efficiency, enhancing product quality, and reducing strip breakage and wrinkling. However, during foil production, factors such as speed variations, roller manufacturing and assembly precision, winding and unwinding diameter variations, mechanical property changes, and roller runout all significantly impact the stability of tension control. The combined effect of these multiple tension-influencing factors leads to non-linear changes in the tension of the winding system. Furthermore, most current floating roller tension control methods are passive and suffer from the weight of the floating roller itself, lacking adaptive adjustment capabilities. Often, they fail to meet control requirements. Moreover, once the tension control system is designed, further improvements in tension control stability require the development of specific control components to ensure tension stability for a particular winding system. Designing different tension control mechanisms for different winding systems increases production costs. Summary of the Invention

[0003] To address the problems in the prior art, this invention proposes a tension-adaptive floating roller control method and mechanism to adapt to different winding systems, actively adjust the nonlinearly changing tension in the winding system in real time, and solve the problem of unstable tension in the winding system.

[0004] The technical solution of this invention is as follows:

[0005] A tension-adaptive floating roll control method includes the following steps:

[0006] S1. Obtain the status data of the floating roller;

[0007] S2. Obtain the control error data of the floating roller based on the status data of the floating roller;

[0008] S3. Use a fuzzy algorithm to obtain the adjustment coefficient based on the control error data of the floating roller;

[0009] S4. Use a PID controller to generate a floating roller control signal based on the adjustment coefficient;

[0010] S5. The floating roller is pulled with stable tension according to the control signal of the floating roller.

[0011] As a preferred embodiment, the status data of the floating roller includes the position data of the floating roller, and the floating roller control signal is used to control the traction motor.

[0012] As a preferred embodiment, the status data of the floating roller includes the floating roller tension data, and the floating roller control signal is used to control the proportional valve.

[0013] As a preferred embodiment, the status data of the floating roller includes the floating roller position data and the floating roller tension data, and the floating roller control signal is used to control the traction motor and the proportional valve.

[0014] As a preferred option, step S3 specifically involves: using a triangular membership function to perform fuzzy calculation on the control error data of the floating roller, and outputting the proportional coefficient of the PID controller as the adjustment coefficient.

[0015] On the other hand, the present invention also provides a tension-adaptive floating roller control mechanism, including a measurement buffer unit, a fuzzy calculation unit, a PID control unit, and a floating roller control unit;

[0016] The measurement buffer unit is used to acquire the status data of the floating roller and to acquire the control error data of the floating roller based on the status data of the floating roller;

[0017] The fuzzy calculation unit is used to obtain the adjustment coefficient based on the control error data of the floating roller using a fuzzy algorithm.

[0018] The PID control unit has a built-in PID controller, which is used to generate a floating roller control signal according to the adjustment coefficient;

[0019] The float roll control unit is used to stabilize the tension of the float roll according to the float roll control signal.

[0020] As a preferred embodiment, the measurement buffer unit includes a position sensor, and the floating roller control unit includes a traction motor.

[0021] As a preferred embodiment, the measurement buffer unit includes a force sensor, and the floating roller control unit includes a proportional valve.

[0022] As a preferred embodiment, the measurement buffer unit includes a position sensor and a force sensor, and the floating roller control unit includes a traction motor and a proportional valve.

[0023] As a further preferred option, the position sensor is a tension position sensor, a potentiometer, or an angle sensor.

[0024] The beneficial effects of this invention include: The tension-adaptive floating roller control method provided by this invention actively measures the position and / or tension information of the floating roller and outputs a control signal for the floating roller based on the position and / or tension information. This is used to compensate for nonlinear tension changes in the floating roller in real time, thereby maintaining tension stability of the floating roller during system operation. Furthermore, the tension-adaptive floating roller control mechanism provided by this invention can be adapted to different winding systems in an assembled manner. Sensors are installed near the components to be measured in the winding system, and a traction motor and pressure roller are used to achieve corresponding negative feedback tension control based on the measured information, thereby solving the tension stability problem of the winding system. Moreover, the algorithm is simple and can be adapted to low-end controllers. Attached Figure Description

[0025] 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.

[0026] Figure 1 This is a flowchart of a tension-adaptive floating roller control method according to the present invention;

[0027] Figure 2 The graph of the membership function of e1(k) in the embodiment of the present invention;

[0028] Figure 3 The graph of the membership function of e2(k) in the embodiment of the present invention;

[0029] Figure 4 This is a graph of the membership function of embodiment K of the present invention;

[0030] Figure 5 This is a schematic diagram of fuzzy inference calculation according to an embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram of the structure of a winding system according to an embodiment of the present invention;

[0032] Figure 7 This is a schematic diagram of another winding system according to an embodiment of the present invention.

[0033] The components include: 1. Pull rope; 2. Guide roller; 3. Tension roller; 4. Floating roller; 5. Tension sensor; 6. Position sensor; 7. Guide roller; 8. Traction roller; 9. Pressure roller; 10. Mass block; 11. Swing rod.

[0034] Specific implementation examples

[0035] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0036] This application provides a tension-adaptive floating roller control method, the flowchart of which is shown below. Figure 1 As shown, the steps include:

[0037] S1. Obtain the status data of the floating roller;

[0038] S2. Obtain the control error data of the floating roller based on the state data of the floating roller;

[0039] S3. Using a fuzzy algorithm, obtain the adjustment coefficient based on the control error data of the floating roller;

[0040] S4. Using a PID controller, generate a floating roller control signal according to the adjustment coefficient;

[0041] S5. The floating roller is pulled with stable tension according to the floating roller control signal.

[0042] Specifically, in one embodiment of this application, in step S1, the state data of the floating roller includes the real-time position data of the floating roller. More specifically, this position data is obtained by measuring the floating roller using a tension position sensor, a potentiometer, or an angle sensor.

[0043] In step S2, the control error data is obtained by subtracting the preset position setting data from the real-time position data collected in step S1. Specifically, the error data may include an error signal e1(k), a deviation signal e2(k), and a correction signal e3(k). The error data at time k includes error signal e1(k) and error signal e2(k). Error signal e1(k) is the error value between the preset data and the measurement data at time k. Error signal e2(k) is the difference between the error data at time k and the error data at time (k-1). The correction signal e3(k) is the sum of the difference between the error at time k and twice the error at time (k-1) and the error at time (k-2).

[0044] In step S3, the fuzzy algorithm calculates the adjustment coefficient through fuzzification, fuzzy inference, and defuzzification processes. It primarily uses the error signal e1(k) and the deviation signal e2(k) to achieve real-time adjustment of the output adjustment coefficient. In this embodiment, the adjustment coefficient is specifically the proportional coefficient for subsequent PID control.

[0045] The fuzzy algorithm aims to reduce the proportional coefficient K appropriately when the acquired signal and the target signal value have a small deviation and a small increment, and increase the proportional coefficient K appropriately when the acquired signal and the target signal value have a large deviation and a large increment, thereby achieving rapid tension stabilization in subsequent floating roll control.

[0046] More specifically, the calculation rules of the above fuzzy algorithm are shown in the table below:

[0047]

[0048] Among them, NB, NS, ZZ, PS and PB represent negative large, negative small, zero, positive small and positive large, respectively.

[0049] The membership function of the above fuzzy algorithm uses the trigonometric membership function, and its function graph is as follows: Figure 2-4 As shown, where, Figure 2 The figure shows the membership function of e1(k). Figure 3 The figure shows the membership function of e2(k). Figure 4 The figure shows the membership function of K.

[0050] The computational diagram of this fuzzy inference is as follows: Figure 5 As shown, according to the calculation rules of the fuzzy algorithm described above, the directional influence of the proportional coefficient K on the change of error value is as follows:

[0051] When the difference between the set data and the measured data decreases, the output proportionality coefficient K decreases.

[0052] When the difference between the set data and the measured data increases, the output proportional coefficient K increases.

[0053] The defuzzification process of the above fuzzy algorithm can be implemented using the centroid method, thereby defuzzifying the fuzzy signal PB obtained by the fuzzy algorithm and obtaining the specific value of the proportional coefficient K.

[0054] In step S4, the PID controller corrects the error based on the control error data obtained in step S2 and the adjustment coefficient obtained in step S3. Specifically, the PID controller is composed of a single-neuron PID unit, and its input data includes the error signal e1(k), the deviation signal e2(k), the correction signal e3(k), and the proportional coefficient K.

[0055] Where e1(k) is the error between the set value and the current measured value at time k, e2(k) is the difference between the error at time k and the error at time k-1, and e3(k) is the difference between the error at time k and twice the error at time k-1, plus the error at time k-2.

[0056] The specific formulas for calculating these coefficients are as follows:

[0057]

[0058] Using the above formula, the single-neuron PID unit of the PID controller takes the weighted output signal K value and position data as input, and then outputs a corresponding control signal. This signal is used to correct the control error and deviation of the floating roller, so that the tension of the floating roller can be quickly stabilized during the traction process.

[0059] In step S5, the control signal output by the PID controller is used as the control signal for the traction motor. In this embodiment, since the state data of the floating roller in step S1 only uses the real-time position data of the floating roller, the control signal output by the PID controller in step S5 is only used for the traction motor and to control the position of the traction motor to pull the floating roller.

[0060] In another embodiment of this application, in step S1, the state data of the floating roller only uses the tension data of the floating roller. This tension data is sequentially processed by error calculation in step S2, fuzzy calculation in step S3, and then by step S4. The proportional coefficient obtained in step S3 and the tension signal in step S1 are calculated by the PID controller and output as a control signal for the proportional valve. Then, in step S5, the control signal is used to control the operation of the proportional valve.

[0061] In another embodiment of this application, a dual closed-loop control method is used: In step S1, the state data of the floating roller simultaneously uses the position data and tension data of the floating roller. These position data and tension data are sequentially processed by error calculation in step S2, fuzzy calculation in step S3, and then by step S4. The proportional coefficient obtained in step S3 and the position data and tension data in step S1 are calculated by the PID controller and then the control signals of the traction motor and the proportional valve are simultaneously output. Finally, in step S5, these two control signals are used to control the operation of the traction motor and the proportional valve.

[0062] The above embodiments select a proportional valve and a traction motor as the control signal execution components, or simultaneously select both a proportional valve and a traction motor as the control signal execution components, to adapt to different winding systems. When the control signal execution component is a proportional valve, the proportional valve directly executes the control signal output by the single-neuron PID model. The proportional valve drives the pressure roller according to the control signal, and the pressure roller abuts against the traction roller, thereby displacing the traction roller and driving the float roller to move, causing the float roller to adjust its position and ultimately maintain it within a preset range. When the control signal execution component is a traction motor, the operating parameters of the winding system, i.e., the motor speed value, are superimposed on the control signal output by the single-neuron PID model to obtain the final control signal. According to the control signal, the motor drives the pressure roller, thereby driving the traction roller to displace, and the traction roller causes the float roller to adjust its position and ultimately maintain it within a preset range.

[0063] These control methods, combining fuzzy logic and single-neuron PID algorithms, exhibit good adaptability and strong robustness. They can also be flexibly and selectively applied, suitable for tension stability control in most winding equipment. They can quickly resolve tension instability issues in existing equipment under the influence of nonlinear friction, and customers do not need to make any mechanical modifications to their existing equipment.

[0064] Another aspect of this application provides a tension-adaptive floating roller control mechanism, including a measurement buffer unit, a fuzzy calculation unit, a PID control unit, and a floating roller control unit;

[0065] The measurement buffer unit is used to acquire the status data of the floating roller and to acquire the control error data of the floating roller based on the status data of the floating roller;

[0066] The fuzzy calculation unit is used to obtain the adjustment coefficient based on the control error data of the floating roller using a fuzzy algorithm.

[0067] The PID control unit has a built-in PID controller, which is used to generate a floating roller control signal according to the adjustment coefficient;

[0068] The float roll control unit is used to stabilize the tension of the float roll according to the float roll control signal.

[0069] The measurement buffer unit is used to execute step S1 in the above method embodiment to obtain the state data of the floating roller, and to obtain the control error data of the floating roller based on the state data of the floating roller. In this embodiment, the state data of the floating roller is specifically the position data of the floating roller.

[0070] The fuzzy calculation unit is used to execute steps S2 and S3 in the above method embodiments, including obtaining control error data by subtracting the pre-set position setting data from the real-time position data collected by the above measurement buffer unit, and then using a fuzzy algorithm to obtain the adjustment coefficient based on the control error data of the floating roller. Specifically, the fuzzy calculation unit calculates the adjustment coefficient through fuzzification, fuzzy inference, and defuzzification processes, and mainly adjusts the output adjustment coefficient in real time based on the error signal e1(k) and the deviation signal e2(k).

[0071] The PID control unit is used to execute step S4 in the above method embodiment, correcting the error based on the control error data obtained by the fuzzy calculation unit and the adjustment coefficient obtained by the measurement buffer unit. Specifically, the PID controller is composed of a single-neuron PID unit, and its input data includes the error signal e1(k), the deviation signal e2(k), the correction signal e3(k), and the proportional coefficient K.

[0072] The float roll control unit executes step S5 by adjusting the float roll in the winding system according to the control signal, keeping the float roll within a preset position range. Specifically, it receives the control signal sent by the PID control unit and controls the float roll according to the control signal to achieve tension balance during traction.

[0073] In this embodiment, the floating roller traction unit includes only a traction motor. Specifically, the control signal executed by the traction motor comes from a PID control unit, and the input parameters of the PID control unit come from a position sensor and a fuzzy computing unit.

[0074] Specifically, position sensors can be potentiometers, angle sensors, or stretch position sensors.

[0075] As an example, this embodiment also provides a winding system operated by a tension-adaptive floating roller control mechanism, the structural schematic of which is shown below. Figure 6 As shown, it includes a pull rope 1, a guide roller 2, a tension roller 3, a floating roller 4, a guide roller 5, a traction roller 6, and a pressure roller 9. The pull rope 1 is wound around the guide roller 2, the tension roller 3, the floating roller 4, the guide roller 7, and the traction roller 8 in sequence. A position sensor 6 is installed near the floating roller 4, and the pressure roller 9 abuts against the traction roller 8.

[0076] The winding system also includes a rocker arm 11 and a mass block 10. One end of the rocker arm 11 is connected to the float roller 4, and the other end is connected to the mass block 10. The mass block 10 on the left side is used to counteract the gravity of the float roller 4 on the right side. A position sensor 6 is installed in the middle of the rocker arm 11. The middle position of the rocker arm 11 can be fixed and rotated. The position sensor 6 is an angle sensor. The angle sensor installed on the rocker arm 11 measures the change angle of the rotation of the rocker arm 11 to obtain the displacement of the float roller 4 moving up and down, and completes the measurement of the position information of the float roller 4, realizing the acquisition of the position data of the float roller 4.

[0077] When there is no foil on the device, the weight of the foil-feeding roller is adjusted to eliminate its gravity and maintain its balanced position. When the tension is stable, the force of the cylinder is balanced with the tension on the foil, and the floating roller will remain in the middle position. When the tension on the foil changes, the force of the cylinder will become unbalanced with the tension, at which point the swing arm will rotate around the pivot, and the angle sensor 6 will send a corresponding change signal.

[0078] After the angle sensor detects the position data of the floating roller 4, it transmits the data to the PID control unit for information processing. The fuzzy calculation unit first calculates the control error data between the actual measured data and the set data of the floating roller 4 position. The error signals e1(k) and e2(k) in the control error data are converted into fuzzy signals in the fuzzy calculation unit according to the fuzzy control design rules. Then, the fuzzy signals are defuzzified using the centroid method and converted into proportional coefficient K. The PID control unit takes the proportional coefficient K and the position data as inputs and outputs a corresponding control signal. The traction motor drives the pressure roller 9 according to this control signal. The traction roller 8 comes into contact with the pressure roller 9, thereby displacing the traction roller 8 and moving the floating roller 4, so that the floating roller 4 adjusts its position and is ultimately maintained within the preset range.

[0079] Another embodiment of this application also provides a tension-adaptive floating roller control mechanism, wherein,

[0080] The measurement buffer unit is used to execute step S1 in the above method embodiment to obtain the state data of the floating roller, and to obtain the control error data of the floating roller based on the state data of the floating roller. In this embodiment, the state data of the floating roller is specifically the tension data of the floating roller.

[0081] The fuzzy calculation unit is used to execute steps S2 and S3 in the above method embodiments, including obtaining control error data by subtracting the pre-set tension setting data from the real-time tension data collected by the above-mentioned measurement buffer unit, and then using a fuzzy algorithm to obtain the adjustment coefficient based on the control error data of the floating roller. Specifically, the fuzzy calculation unit calculates the adjustment coefficient through fuzzification, fuzzy inference, and defuzzification processes, and mainly adjusts the output adjustment coefficient in real time based on the error signal e1(k) and the deviation signal e2(k).

[0082] The PID control unit is used to execute step S4 in the above method embodiment, correcting the error based on the control error data obtained by the fuzzy calculation unit and the adjustment coefficient obtained by the measurement buffer unit. Specifically, the PID controller is composed of a single-neuron PID unit, and its input data includes the error signal e1(k), the deviation signal e2(k), the correction signal e3(k), and the proportional coefficient K.

[0083] The float roll control unit executes step S5 by adjusting the float roll in the winding system according to the control signal, keeping the float roll within a preset tension range. Specifically, it receives the control signal sent by the PID control unit and controls the float roll according to the control signal to achieve tension balance during traction.

[0084] In this embodiment, the floating roller traction unit includes only a proportional valve. Specifically, the control signal executed by the proportional valve comes from the PID control unit, and the input parameters of the PID control unit come from the tension sensor and the fuzzy computing unit.

[0085] As an example, this embodiment also provides a winding system operated by a tension-adaptive floating roller control mechanism, the structural schematic of which is shown below. Figure 7 As shown, the assembly includes a pull rope 1, a guide roller 2, a tension roller 3, a floating roller 4, a guide roller 5, a traction roller 8, and a pressure roller 9. The pull rope 1 is wound sequentially around the guide roller 2, tension roller 3, floating roller 4, guide roller 7, and traction roller 8. The floating roller 4 is also connected to a tension sensor 5, while the pressure roller 9 abuts against the traction roller 8. In this embodiment, the tension sensor 5 is a low-friction cylinder.

[0086] The low-friction cylinder 5 detects the tension data of the floating roller 4 and transmits it to the PID control unit for information processing. The fuzzy calculation unit first calculates the control error data between the actual measured tension data and the set tension data of the floating roller 4. The error signals e1(k) and e2(k) in the control error data are converted into fuzzy signals in the fuzzy calculation unit according to the fuzzy control design rules. Then, the fuzzy signals are defuzzified using the centroid method and converted into proportional coefficient K. The PID control unit takes the proportional coefficient K and the position data as inputs and outputs a corresponding control signal. The proportional valve drives the pressure roller 9 according to this control signal. The traction roller 8 abuts against the pressure roller 9, thereby displacing the traction roller 8 and driving the floating roller 4 to move, so that the floating roller 4 adjusts its position and is ultimately maintained within the preset range.

[0087] Another embodiment of this application also provides a tension-adaptive floating roller control mechanism, wherein,

[0088] The measurement buffer unit is used to execute step S1 in the above method embodiment to obtain the state data of the floating roller, and to obtain the control error data of the floating roller based on the state data of the floating roller. In this embodiment, the state data of the floating roller includes both the position data and tension data of the floating roller.

[0089] The fuzzy calculation unit executes steps S2 and S3 in the above method embodiments, including subtracting the pre-set tension setting data from the real-time tension data collected by the measurement buffer unit, and simultaneously subtracting the pre-set position setting data from the real-time position data collected by the measurement buffer unit to obtain control error data. Then, a fuzzy algorithm is used to obtain the adjustment coefficient based on the control error data of the floating roller. Specifically, the fuzzy calculation unit calculates the adjustment coefficient through fuzzification, fuzzy inference, and defuzzification processes, mainly adjusting the output adjustment coefficient in real time based on the error signal e1(k) and the deviation signal e2(k).

[0090] The PID control unit is used to execute step S4 in the above method embodiment, correcting the error based on the control error data obtained by the fuzzy calculation unit and the adjustment coefficient obtained by the measurement buffer unit. Specifically, the PID controller is composed of a single-neuron PID unit, and its input data includes the error signal e1(k), the deviation signal e2(k), the correction signal e3(k), and the proportional coefficient K.

[0091] The float roll control unit executes step S5 by adjusting the float roll in the winding system according to the control signal, keeping the float roll within a preset tension range. Specifically, it receives the control signal sent by the PID control unit and controls the float roll according to the control signal to achieve tension balance during traction.

[0092] In this embodiment, the floating roller traction unit includes only a proportional valve. Specifically, the control signal executed by the proportional valve comes from the PID control unit, and the input parameters of the PID control unit come from the position sensor, tension sensor, and fuzzy computing unit.

[0093] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A tension-adaptive floating roller control method, characterized in that, Including the following steps: S1. Obtain the status data of the floating roller, the status data including the floating roller position data and the floating roller tension data; S2. Obtain the control error data of the floating roller based on the state data of the floating roller; S3. Using a fuzzy algorithm, obtain the adjustment coefficient based on the control error data of the floating roller; S4. Using a PID controller, a floating roller control signal is generated according to the adjustment coefficient. The floating roller control signal is used to control the traction motor and the proportional valve. S5. Stable tension is used to pull the floating roller according to the floating roller control signal; Step S5 specifically includes: When the control signal execution component is a traction motor, the final control signal is obtained by superimposing the operating parameters of the winding system, i.e. the motor speed value, on the floating roller control signal. According to the control signal, the motor drives the pressure roller, thereby driving the traction roller to move. The traction roller adjusts the position of the floating roller and ultimately keeps it within the preset range.

2. The tension-adaptive floating roller control method as described in claim 1, characterized in that, Step S3 specifically involves: using a trigonometric membership function to perform fuzzy calculation on the control error data of the floating roller, and outputting the proportional coefficient of the PID controller as the adjustment coefficient.

3. A tension-adaptive floating roller control mechanism, characterized in that, Includes a measurement buffer unit, a fuzzy computing unit, a PID control unit, and a floating roller control unit; The measurement buffer unit is used to acquire the state data of the floating roller and to acquire the control error data of the floating roller based on the state data of the floating roller. The measurement buffer unit includes a position sensor and a force sensor. The fuzzy calculation unit is used to obtain the adjustment coefficient based on the control error data of the floating roller using a fuzzy algorithm. The PID control unit has a built-in PID controller, which is used to generate a floating roller control signal according to the adjustment coefficient; The floating roller control unit is used to stabilize the tension of the floating roller according to the floating roller control signal. The floating roller control unit includes a traction motor and a proportional valve. The step of stabilizing the tension of the floating roller according to the floating roller control signal includes: When the control signal execution component is a traction motor, the final control signal is obtained by superimposing the operating parameters of the winding system, i.e. the motor speed value, on the floating roller control signal. According to the control signal, the motor drives the pressure roller, thereby driving the traction roller to move. The traction roller adjusts the position of the floating roller and ultimately keeps it within the preset range.

4. The tension-adaptive floating roller control mechanism as described in claim 3, characterized in that, The position sensor is a tension position sensor, a potentiometer, or an angle sensor.

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