A high performance tension control method and system for electrically powered rolling of very thin strip

Abstract

The application belongs to the technical field of control of ultra-thin strip rolling equipment, and discloses a high-performance tension control method and system for the process of electrically powered rolling of an ultra-thin strip. The method comprises the following steps: constructing a tension model of components in the electrically powered rolling ultra-thin strip system, determining a coarse adjustment open-loop transfer function and a fine adjustment open-loop transfer function of the rolling system based on factors affecting the change of the tension; based on the coarse adjustment open-loop transfer function, using a PID controller to control the movement of a motor to coarsely adjust the tension applied to the ultra-thin strip; based on the fine adjustment open-loop transfer function, using a fuzzy PID control strategy optimized by an improved grey wolf algorithm to control a cylinder to finely adjust the tension applied to the ultra-thin strip; collecting a tension output value, calculating the error and the error change rate between the output value and a target value, and controlling the tension through a control algorithm.

Description

Technical Field

[0001] This invention belongs to the field of control technology for ultra-thin strip rolling equipment, specifically relating to a high-performance tension control method and system for the process of electrically rolled ultra-thin strip. Background Technology

[0002] Precision ultra-thin strips occupy a key position in modern industrial fields due to their significant characteristics such as high precision, superior performance, corrosion resistance, and smooth surface. They are widely used in core fields such as high-end electronics, aerospace, and new energy, and are the core basic materials for high-end products such as micro-robots, flexible screens, printed circuits, and power batteries.

[0003] In the electro-rolling process of ultra-thin strip, tension stability is crucial to product quality. However, the tension in this process is highly susceptible to fluctuations due to multiple factors: First, the unwinding diameter is inversely proportional to the running speed during winding, and this dynamic change directly triggers tension fluctuations, leading to strip vibration. Second, the electro-energizing process alters the morphology and internal structure of the ultra-thin strip, and combined with the inherent characteristics of the strip itself, such as surface quality and residual stress, further exacerbates tension instability. These tension fluctuations not only cause tensile and plastic deformation of the strip during rolling but can also lead to serious production accidents such as strip stacking and breakage, significantly impacting production efficiency and product quality.

[0004] Practice shows that to ensure the quality stability of rolled thin strip, differentiated tension control strategies are required: constant tension control should be achieved during the energized rolling stage. Currently, commonly used tension control methods in rolling systems mainly include PID control, fuzzy control, and decoupling control. Although conventional PID control and fuzzy control can suppress tension fluctuations to some extent, they both have significant limitations: conventional PID control, due to its fixed parameters, is prone to oscillations in complex dynamic processes, failing to meet the high-precision real-time control requirements of the tension control system during energized rolling; the control performance of conventional fuzzy controllers is highly dependent on parameter settings, and deviations in parameter settings may fundamentally change their output characteristics. In practical applications, the parameters of fuzzy PID controllers are mostly determined based on experience, making it difficult to obtain the optimal parameter combination when facing different control systems, resulting in insufficient stability of the control effect; although decoupling control can approximately decompose the system into multiple independent single-variable subsystems, this method has high design complexity, strong dependence on the accuracy of the system model, and weak anti-interference ability, making it difficult to achieve the theoretical expected results in actual production environments.

[0005] Further analysis reveals that when the above three control methods are applied to the electric rolling scenario, changes in the target tension value will lead to a significant decrease in control performance due to the fixed gain parameter of the controller, making it impossible to meet the stringent requirements for tension control in the precision ultra-thin strip electric rolling process. Summary of the Invention

[0006] This invention aims to address the shortcomings of existing technologies and provides the following solutions:

[0007] A high-performance tension control method for the electro-rolling of ultra-thin strip includes the following steps:

[0008] A tension model of a component in an electrically rolled ultra-thin strip system is constructed. Based on the factors affecting tension changes, the coarse-tuning open-loop transfer function and the fine-tuning open-loop transfer function of the rolling system are determined.

[0009] Based on the coarse-tuning open-loop transfer function, and using a PID controller to control the motor movement, the tension applied to the ultra-thin strip is coarsely adjusted.

[0010] Based on the finely tuned open-loop transfer function, the cylinder is controlled by an optimized fuzzy PID control strategy using the improved Grey Wolf algorithm to finely tune the tension applied to the ultra-thin strip.

[0011] The tension output value is collected, the error and the rate of change of the error between the output value and the target value are calculated, and the tension is controlled by a control algorithm.

[0012] Preferably, the coarse-tuned open-loop transfer function is:

[0013] ;

[0014] Among them, G o (s) represents the coarse-tuned open-loop transfer function, f0 represents the tension-free forward slip coefficient, β0 represents the influence coefficient of tension on the forward slip coefficient, v represents the strip speed, A represents the cross-sectional area of ​​the strip, E represents the elastic modulus of the strip, l0 represents the length of the ultra-thin strip from the work roll exit to the winding mechanism, s represents the complex frequency variable, and K t T represents the gain coefficient of the coarse adjustment stage. t This represents the time constant of the coarse adjustment stage.

[0015] Preferably, the fine-tuned open-loop transfer function is:

[0016] ;

[0017] ;

[0018] ;

[0019] Among them, G t (s) represents the fine-tuned open-loop transfer function, G b (s) represents the transfer function of the floating roller system, G m (s) represents the cylinder transfer function, K x K represents the tension gain. valτ represents the proportional valve gain, c represents the damping, m represents the mass of the floating roller system, and τ represents the cylinder time constant.

[0020] Preferably, in the coarse adjustment, the speed difference between the winding mechanism motor and the work roll motor is controlled by the PID controller to adjust the tension.

[0021] Once the actual tension F in the coarse adjustment reaches 95%T ± 0.5N of the set tension T, the fine adjustment is performed by using the improved Grey Wolf algorithm-optimized fuzzy PID control strategy to control the position of the floating roller and adjust the tension.

[0022] The present invention also provides a high-performance tension control system for the process of electric rolling of ultra-thin strip, the control system applying the above-mentioned control method, including: a function calculation module, a coarse adjustment module, a fine adjustment module and a feedback control module;

[0023] The function calculation module is used to construct the tension model of the components in the electrically rolled ultra-thin strip system, and to determine the coarse-tuning open-loop transfer function and the fine-tuning open-loop transfer function of the rolling system based on the factors affecting the tension change.

[0024] The coarse adjustment module is based on the coarse adjustment open-loop transfer function and uses a PID controller to control the motor movement to coarsely adjust the tension applied to the ultra-thin strip.

[0025] The fine-tuning module, based on the fine-tuning open-loop transfer function, uses an improved gray wolf algorithm-optimized fuzzy PID control strategy to control the cylinder to fine-tune the tension applied to the ultra-thin strip.

[0026] The feedback control module is used to collect the tension output value, calculate the error and error rate between the output value and the target value, and perform tension control through a control algorithm.

[0027] Preferably, the coarse-tuned open-loop transfer function is:

[0028] ;

[0029] Among them, G o (s) represents the coarse-tuned open-loop transfer function, f0 represents the tension-free forward slip coefficient, β0 represents the influence coefficient of tension on the forward slip coefficient, v represents the strip speed, A represents the cross-sectional area of ​​the strip, E represents the elastic modulus of the strip, l0 represents the length of the ultra-thin strip from the work roll exit to the winding mechanism, s represents the complex frequency variable, and K t T represents the gain coefficient of the coarse adjustment stage. t This represents the time constant of the coarse adjustment stage.

[0030] Preferably, the fine-tuned open-loop transfer function is:

[0031] ;

[0032] ;

[0033] ;

[0034] Among them, G t (s) represents the fine-tuned open-loop transfer function, G b (s) represents the transfer function of the floating roller system, G m (s) represents the cylinder transfer function, K x K represents the tension gain. val τ represents the proportional valve gain, c represents the damping, m represents the mass of the floating roller system, and τ represents the cylinder time constant.

[0035] Preferably, in the coarse adjustment module, the PID controller is used to control the speed difference between the winding mechanism motor and the work roll motor to adjust the tension.

[0036] In the fine-tuning module, when the actual tension F in the coarse adjustment reaches 95%T±0.5N of the set tension T, the floating roller position is controlled by the improved Grey Wolf algorithm-optimized fuzzy PID control strategy to adjust the tension magnitude for fine-tuning.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] This invention optimizes the Grey Wolf algorithm using Logistic chaotic mapping, improving its global search capability and reducing the possibility of it getting trapped in local optima. The improved Grey Wolf algorithm is then used to optimize a fuzzy PID controller, reducing the problem of inaccurate parameter tuning in actual production. The tension control device of this invention can reduce tension fluctuations, and the combination of multiple sensors and coarse-fine tuning enables precise tension control. An RBF neural network model is established and trained on data obtained from the improved Grey Wolf algorithm. The trained model then interacts with a PLC to control the rolling operation, demonstrating excellent tension control performance in energized rolling and in environments with multiple disturbances in the workshop. Attached Figure Description

[0039] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are 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.

[0040] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0041] Figure 2This is a flowchart illustrating the optimization of a fuzzy PID controller using an improved gray wolf algorithm according to an embodiment of the present invention.

[0042] Figure 3 This is a diagram of the RBF neural network structure of an embodiment of the present invention;

[0043] Figure 4 This is an embodiment of the RBF neural network fuzzy PID control flow optimized based on the improved gray wolf algorithm of the present invention.

[0044] Figure 5 This is a schematic diagram of the tension control device according to an embodiment of the present invention;

[0045] Explanation of reference numerals in the attached figures:

[0046] 1. Unwinding roll; 2. Servo motor; 3. Tension sensor; 4. Cylinder; 5. Host computer; 6. Electro-proportional valve; 7. Support roll; 8. Rewinding roll; 9. Floating roll; 10. A / D conversion module; 11. PLC controller; 12. D / A conversion module; 13. Servo driver; 14. Laser positioner; 15. Work roll; 16. Grating ruler. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Example 1:

[0050] In this embodiment, as Figure 1 As shown, a high-performance tension control method for the electric rolling of ultra-thin strip includes the following steps:

[0051] S1. Construct a tension model for components in an electrically rolled ultra-thin strip system. Based on the factors affecting tension changes, determine the coarse-tuning open-loop transfer function and the fine-tuning open-loop transfer function of the rolling system.

[0052] In this embodiment, the method for constructing the coarse-tuned open-loop transfer function includes:

[0053] 1. Consider the transfer functions of the winding mechanism and servo motor 2 as a first-order inertial system:

[0054] ;

[0055] Among them, G d (s) represents the transfer function of the winding mechanism and the servo motor, T1 represents the mechanical time constant of the unwinding motor, and s represents the complex frequency variable.

[0056] 2. Consider the transfer function of the work roller motor as a first-order inertial system:

[0057] ;

[0058] Among them, G e (s) represents the transfer function of the work roll motor, and T2 represents the mechanical time constant of the work roll motor.

[0059] 3. The pre-tension acting on the strip is:

[0060] ;

[0061] ;

[0062] Where T represents the initial tension, A represents the cross-sectional area of ​​the strip, E represents the elastic modulus of the strip, l0 represents the length of the ultra-thin strip from the exit of work roll 15 to the winding mechanism, v2 represents the speed of the winding mechanism, and v f denoted by f, which represents the speed of the strip at the exit of the rolling deformation zone; f represents the forward slip coefficient of the strip at the exit of work roll 15; f0 represents the tension-free forward slip coefficient; and β0 represents the influence coefficient of tension on the forward slip coefficient.

[0063] Define the speed difference:

[0064] ;

[0065] Where v represents the strip speed. In the precision electro-rolling of ultra-thin strips, the slip coefficient has a significant impact on strip tension control:

[0066] ;

[0067] Taking the Laplace transform of the above equation, we get:

[0068] ;

[0069] The transfer function is obtained as follows:

[0070] ;

[0071] ;

[0072] ;

[0073] Among them, G o(s) represents the coarse-tuned open-loop transfer function, K t T represents the gain coefficient of the coarse adjustment stage. t This represents the time constant of the coarse adjustment stage.

[0074] In this embodiment, the method for fine-tuning the open-loop transfer function construction includes:

[0075] 1. Controller: Converts the set tension into current for the proportional valve.

[0076] Treat the current transfer function of the proportional valve as a proportional element:

[0077] ;

[0078] Where Gc(s) represents the proportional valve current transfer function, K val This indicates the gain of the proportional valve.

[0079] 2. Cylinder actuator: Converts the current transmitted by the proportional valve into the thrust of cylinder 4.

[0080] In the time domain, the dynamic relationship between cylinder 4 and the current thrust satisfies a first-order differential equation.

[0081] ;

[0082] Among them, F cyl I(t) represents the change in cylinder thrust over time, and I(t) represents the change in current input to the cylinder actuator over time. Performing a Laplace transform on the equation:

[0083] ;

[0084] Among them, F cyl I(s) represents the cylinder output thrust, and I(s) represents the complex frequency domain function of the input current to the cylinder actuator after Laplace transform. The cylinder transfer function is then obtained:

[0085] ;

[0086] Among them, G m (s) represents the cylinder transfer function, and τ represents the cylinder time constant.

[0087] 3. Floating Roller Mechanical System: Converts the thrust of cylinder 4 into the displacement of floating roller 9.

[0088] According to Newton's second law, the resultant force exerted by the floating roller 9 on the cylinder 4 is:

[0089] ;

[0090] ;

[0091] Among them, F 合 Let F represent the resultant force exerted by the floating roller on the cylinder, m represent the mass of the floating roller system, a represent the acceleration of the floating roller system, x represent the displacement of the floating roller system, and F represent the displacement of the floating roller system. 合 (t) represents the resultant force exerted by the floating roller on the cylinder as time changes, θ represents the angle between the vertical direction and the strip tension direction, and T(t) represents the tension at the current moment. The equilibrium equation of the mechanical roller system in the time domain is:

[0092] ;

[0093] Where c represents damping, and x(t) represents the displacement of the floating roller system as a function of time. Taking a Laplace transform of the equation:

[0094] ;

[0095] Among them, F 合 X(s) represents the complex frequency domain function of the resultant force exerted by the floating roller on the cylinder after Laplace transform, and X(s) represents the complex frequency domain function of the displacement of the floating roller system after Laplace transform. Therefore, the transfer function of the floating roller system is:

[0096] ;

[0097] Among them, G b (s) represents the transfer function of the floating roller system.

[0098] 4. Actual tension of the strip: Converting the displacement of the floating roller 9 into the actual tension of the entire system:

[0099] According to Hooke's Law:

[0100] ;

[0101] Where k represents the elastic modulus of the strip. This represents the total elongation of the strip. We obtain:

[0102] ;

[0103] Among them, K x This represents the tension gain. Based on the above formula, the fine-tuned open-loop transfer function is obtained:

[0104] ;

[0105] Among them, G t (s) represents the fine-tuned open-loop transfer function.

[0106] In this embodiment, the macroscopic and microscopic changes in the electro-rolling of ultra-thin strip involve the coupling effects of temperature field, electromagnetic field, deformation field, and force field, and are also affected by multiple factors such as stress distribution, current parameters, and Joule heating temperature rise. Different metallic materials have different properties, making the mechanism of electroplasticity very complex and still inconclusive. Therefore, the mechanism of tension variation is currently undetermined; thus, in this patent, it is treated as perturbations of different magnitudes to simulate its influence on tension during the ultra-thin strip rolling process.

[0107] S2. Based on the coarse adjustment of the open-loop transfer function, and using a PID controller to control the motor motion, the tension applied to the ultra-thin strip is coarsely adjusted.

[0108] In the coarse adjustment, the speed difference between the winding mechanism motor and the work roll motor is controlled by a PID controller to adjust the tension.

[0109] S3. Based on the fine-tuned open-loop transfer function, the fuzzy PID control strategy optimized by the improved gray wolf algorithm is used to control the cylinder 4 to fine-tune the tension applied to the ultra-thin strip.

[0110] Once the actual tension F in the coarse adjustment reaches 95%T ± 0.5N of the set tension T, the floating roller 9 is positioned and the tension is adjusted using a fuzzy PID control strategy optimized by the improved Grey Wolf algorithm for fine adjustment.

[0111] In this embodiment, fine-tuning is affected by the magnitude and relative relationship of the variable factors of the fuzzy PID controller, which greatly influences the control performance. Therefore, an improved Grey Wolf algorithm is used to optimize it, such as... Figure 2 As shown, an improved Grey Wolf algorithm is written in MATLAB using an m-file. The initial population size, number of iterations, upper and lower limits of variables, and evaluation index function are determined. The evaluation index function uses system output error, controller input, overshoot, response time, and disturbance as variables. The optimal variable combination is obtained by measuring the value of the evaluation index function. In this invention, the initial population size for the variable factor combination is 30, and the number of iterations is 100. The m-file uses an interface function to interact with SIMULINK simulation data, returning the output error, controller input, overshoot, and response time to the evaluation index function for calculation, thereby achieving optimization and determining the optimal value of the variable factor. The interactive data method between MATLAB and SIMULINK reduces programming workload and makes the optimization process more intuitive. Then, an RBF neural network model is established and trained. The tension target value is used as the input to the RBF neural network, and the output is the optimal value of the variable factor under this tension. Finally, communication is established with the hardware PLC for data interaction, ensuring that the variable factor corresponding to different tension target values ​​always maintains the optimal value, improving the system's response speed and stability.

[0112] Based on the initial value determined by the open-loop transfer function of the rolling tension system, a fuzzy PID control strategy is constructed by incorporating fuzzy control technology to achieve real-time tuning of the originally set output parameters of the PID controller. This fuzzy control strategy comprises five core components, each with the following functions and implementation methods: Input quantization: The key parameters in tension control—the deviation e between the target tension value and the detected value, and the deviation change rate ec—are converted into quantized values ​​of the linguistic variables e and ec within the fuzzy domain using a quantization factor. Fuzzification: The degree to which the input quantities e and ec belong to each fuzzy subset of linguistic variables is determined based on the membership function curve. This strategy uses a triangular membership function and explicitly defines the fuzzy output domain as [-6, 6]. Fuzzy inference: Based on preset fuzzy control rules, logical deduction is performed on the fuzzified e and ec to generate fuzzy output results. Defuzzification: The centroid method is used to process the results obtained from fuzzy inference, converting the fuzzy output into explicit numerical values. Output quantization: The fuzzy output values ​​obtained after defuzzification are further converted into output parameters that meet actual control requirements using a scaling factor.

[0113] The search upper and lower bounds for the 5-dimensional parameters to be optimized, the gray wolf population size of 30, the maximum number of iterations of 100, and the fitness objective function based on the system response are set. A Logistic chaotic mapping with control parameter r=3.9 is used to generate the initial population. Differentiated initial values ​​are set for each parameter dimension. After 100 pre-iterations to skip transients, the mapping is applied to the parameter range to improve population diversity. Subsequently, the gray wolf optimization algorithm is used for iterative optimization. Alpha (optimal), Beta (second-best), and Delta (third-best) wolves are initially selected as elite guides. Global exploration is achieved by linearly decreasing parameter a from 2 to 0. To achieve dynamic balance with local development, each individual gray wolf, guided by elite wolves, updates its position using random coefficients A and C, and updates the elite individuals and records optimal parameters in real time. Iteration terminates when the maximum number of iterations and the Alpha fitness value ≤ 1e-7 threshold, outputting the 5-dimensional optimal parameters (Ke, Kec, Kp, Ki, Kd) corresponding to the Alpha wolf. By changing different tension values, disturbances, rolling speeds, and strip thicknesses, an improved gray wolf algorithm is used to optimize fuzzy PID calculations to obtain a set of optimal parameters. These optimal parameters are then used as the training set for the RBF neural network. Figure 3 As shown, a pre-trained RBF neural network is used for tension fine-tuning. RBF learns control laws through data-driven learning, achieving high-precision control for nonlinear systems in industry that are difficult to model precisely, without relying on complex modeling. On the other hand, the improved Grey Wolf algorithm parameters are optimized for offline execution, requiring only one execution during system initialization or significant changes in operating conditions. Subsequent real-time control relies on the lightweight computation of RBF, requiring low hardware computing power and easy deployment in PLCs in industrial settings, balancing control performance and engineering implementation costs.

[0114] S4. Acquire the tension output value, calculate the error and rate of change of error between the output value and the target value, and perform tension control through a control algorithm, such as... Figure 4 As shown, the workflow of the "coarse-fine" composite tension control system is as follows: First, the coarse-fine control (top left corner of the diagram) quickly establishes the tension to near the "set tension T". Then, the fine-fine system is activated: During the offline optimization phase, the "improved gray wolf algorithm" module iteratively optimizes for different operating conditions (such as "simulating a disturbance-induced power-on environment") to find the optimal parameter combination for the fuzzy PID controller (including quantization factors Ke0, Kec0 and PID parameters Kp0, Ki0, Kd0). This optimal data is used to train the "RBF neural network". During the online real-time control phase, the computationally intensive gray wolf algorithm is no longer run; instead, the trained "RBF neural network" module takes its place. Based on the feedback from the current "set tension T" and "actual tension F", it outputs the optimal parameters in real-time and quickly, dynamically adjusting the "fuzzy controller" and "PID controller" to precisely control the "controlled object" and achieve high-precision tension tracking.

[0115] Example 2:

[0116] In this embodiment, a high-performance tension control system for the process of electrically rolled ultra-thin strip includes: a function calculation module, a coarse adjustment module, a fine adjustment module, and a feedback control module.

[0117] The function calculation module is used to construct the tension model of the components in the electrically rolled ultra-thin strip system. Based on the factors affecting the tension change, it determines the coarse-tuning open-loop transfer function and the fine-tuning open-loop transfer function of the rolling system.

[0118] The coarse-tuned open-loop transfer function is:

[0119] ;

[0120] Among them, G o (s) represents the coarse-tuned open-loop transfer function, f0 represents the tension-free forward slip coefficient, β0 represents the influence coefficient of tension on the forward slip coefficient, v represents the strip speed, A represents the cross-sectional area of ​​the strip, E represents the elastic modulus of the strip, l0 represents the length of the ultra-thin strip from the exit of work roll 15 to the winding mechanism, s represents the complex frequency variable, and K t T represents the gain coefficient of the coarse adjustment stage. t This represents the time constant of the coarse adjustment stage.

[0121] The fine-tuned open-loop transfer function is as follows:

[0122] ;

[0123] ;

[0124] ;

[0125] Among them, G t (s) represents the fine-tuned open-loop transfer function, G b (s) represents the transfer function of the floating roller system, G m (s) represents the cylinder transfer function, K x K represents the tension gain. val τ represents the proportional valve gain, c represents the damping, m represents the mass of the floating roller system, and τ represents the cylinder time constant.

[0126] The coarse adjustment module is based on the coarse adjustment open-loop transfer function and uses a PID controller to control the motor movement to coarsely adjust the tension applied to the ultra-thin strip.

[0127] In the coarse adjustment module, a PID controller is used to control the speed difference between the winding mechanism motor and the work roll motor to adjust the tension.

[0128] The fine-tuning module, based on the fine-tuning open-loop transfer function, uses an improved gray wolf algorithm-optimized fuzzy PID control strategy to control cylinder 4 to fine-tune the tension applied to the ultra-thin strip.

[0129] In the fine-tuning module, when the actual tension F in the coarse-tuning reaches 95%T±0.5N of the set tension T, the floating roller 9 is positioned and the tension is adjusted using a fuzzy PID control strategy optimized by the improved Grey Wolf algorithm for fine-tuning.

[0130] The feedback control module is used to collect the tension output value, calculate the error and the rate of change of error between the output value and the target value, and perform tension control through the control algorithm.

[0131] Example 3:

[0132] In this embodiment, due to the symmetrical arrangement of the rolling mill, only the winding section is described here. This embodiment provides a tension control device based on a control method of one embodiment, such as... Figure 5As shown, it includes: unwinding roller 1, servo motor 2, tension sensor 3, cylinder 4, host computer 5, electro-proportional valve 6, support roller 7, winding roller 8, floating roller 9, A / D conversion module 10, PLC controller 11, D / A conversion module 12, servo driver 13, laser positioner 14, work roller 15, and grating ruler 16. The four support rollers 7 consist of two fixed support rollers on the far left and far right, which support the two upper floating rollers 9 to ensure that the thin strip always coincides with the contact line of the two working rollers 15 when entering and exiting the rolling mill, so as to ensure that the tension at the inlet and outlet of the rolled thin strip remains unchanged. The two upper support rollers 7 are floating rollers 9, which are fixed on an acrylic plate and connected to a grating ruler 16. The low-friction cylinder 4 is fixed on the back of the support plate and has a buffering and shock absorption function to suppress the fluctuation of the thin strip during machine operation. The acrylic plate is connected to the piston rod of the cylinder 4. The piston rod moves up and down to control the up and down movement of the floating rollers 9, thereby controlling the tension. There is a laser positioner 14 at the bottom of the cylinder 4 to measure the position of the floating rollers 9. The support roller 7 at the center of the front is connected to the tension sensor 3 to measure the tension in real time.

[0133] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for high performance tension control in an electrically powered strip rolling process, characterized by, The method comprises the following steps: A tension model of components in the system is constructed, and a coarse open-loop transfer function and a fine open-loop transfer function of the rolling system are determined based on factors affecting tension change; The tension applied to the ultra-thin strip is coarsely adjusted by using a PID controller to control motor movement based on the coarse open-loop transfer function; The tension applied to the ultra-thin strip is finely adjusted by using a fuzzy PID control strategy optimized by an improved grey wolf algorithm based on the fine open-loop transfer function; An error and an error change rate between an output value and a target value are calculated based on the output value, and tension control is performed by using a control algorithm; The coarse open-loop transfer function is: ； wherein G o (s) represents a coarse tuning open loop transfer function, f0represents a pre-slip coefficient without tension, β0represents a coefficient of the effect of tension on the pre-slip coefficient, v represents the speed of the strip, A represents the cross-sectional area of the strip, E represents the elastic modulus of the strip, l0represents the length of the very thin strip from the exit of the working rolls to the winding mechanism, s represents a complex frequency variable, K t represents a gain coefficient of the coarse tuning link, T t represents a time constant of the coarse tuning link; The fine open-loop transfer function is: ； ； ； where G t (s) represents the fine tuning open loop transfer function, G b (s) represents the float roll system transfer function, G m (s) represents the air cylinder transfer function, K x represents the tension gain, K val represents the proportional valve gain, c represents the damping, m represents the mass of the float roll system, and τ represents the air cylinder time constant; In the coarse adjustment, the speed difference between the winding mechanism motor and the work roll motor is controlled by using the PID controller to adjust the tension size; When the actual tension F in the coarse adjustment reaches 95% T ± 0.5 N of the set tension T, the tension size is adjusted by using the fuzzy PID control strategy optimized by the improved grey wolf algorithm to control the floating roll position for the fine adjustment.

2. A high performance tension control system for electrically powered rolling of an extremely thin strip, said control system applying the control method of claim 1, characterized in that, The method comprises: a function calculation module, a coarse adjustment module, a fine adjustment module, and a feedback control module; The function calculation module is used to construct a tension model of components in the system, and a coarse open-loop transfer function and a fine open-loop transfer function of the rolling system are determined based on factors affecting tension change; The coarse adjustment module is used to coarsely adjust the tension applied to the ultra-thin strip by using a PID controller to control motor movement based on the coarse open-loop transfer function; The fine adjustment module is used to finely adjust the tension applied to the ultra-thin strip by using a fuzzy PID control strategy optimized by an improved grey wolf algorithm based on the fine open-loop transfer function; The feedback control module is used to calculate an error and an error change rate between an output value and a target value based on the output value, and perform tension control by using a control algorithm; The coarse open-loop transfer function is: ； wherein G o (s) represents a rough tuning open loop transfer function, f0represents a pre-slip coefficient without tension, β0represents a coefficient of the effect of tension on the pre-slip coefficient, v represents the speed of the strip, A represents the cross-sectional area of the strip, E represents the elastic modulus of the strip, l0represents the length of the very thin strip from the exit of the working rolls to the winding mechanism, s represents a complex frequency variable, K t represents a gain coefficient of the rough tuning link, T t represents a time constant of the rough tuning link; The fine open-loop transfer function is: ； ； ； where G t (s) represents the fine tuning open loop transfer function, G b (s) represents the floatation roll system transfer function, G m (s) represents the air cylinder transfer function, K x represents the tension gain, K val represents the proportional valve gain, c represents the damping, m represents the mass of the floatation roll system, and τ represents the air cylinder time constant; In the coarse adjustment, the speed difference between the winding mechanism motor and the work roll motor is controlled by using the PID controller to adjust the tension size; When the actual tension F in the coarse adjustment reaches 95% T ± 0.5 N of the set tension T, the tension size is adjusted by using the fuzzy PID control strategy optimized by the improved grey wolf algorithm to control the floating roll position for the fine adjustment.

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