An anti-interference control method for cold-rolled steel plate shape based on high-order sliding mode

Abstract

The present invention discloses a method for cold-rolled steel plate shape anti-interference control based on a high-order sliding mode, comprising: real-time acquisition of cold-rolled steel plate shape data during the rolling process, forming a cold-rolled steel plate shape error vector, constructing a cold-rolled steel plate shape lumped noise, and calculating the cold-rolled steel plate shape noise spectrum in real time through a sliding window Fourier transform; designing a filter cutoff frequency, using a high-pass filter and a low-pass filter respectively according to the filter cutoff frequency to separate the slow-varying noise and the fast-varying noise in the cold-rolled steel plate shape noise spectrum, and predicting the slow-varying noise at the next moment through the slow-varying noise; using a derivative observer of the high-order sliding mode to observe the cold-rolled steel plate shape error vector, and designing a high-order sliding mode control law based on the fast-varying noise and the predicted slow-varying noise; using the high-order sliding mode control law to control the input signal during the rolling process to achieve cold-rolled steel plate shape anti-interference control. The present invention dynamically compensates for interference during the steel plate rolling process, improving the stability and real-time performance of the anti-interference control.

Description

Technical Field

[0001] The present invention relates to the technical field of metal rolling automatic control, and in particular to a cold-rolled steel plate shape anti-interference control method based on a high-order sliding mode. Background Art

[0002] Cold-rolled steel flatness control technology is a core component of metal rolling automation. Its control accuracy directly determines the flatness and production efficiency of the strip. As high-end manufacturing continues to demand higher flatness quality, traditional control methods face significant technical bottlenecks in handling multivariable coupling and nonlinear disturbances.

[0003] The existing typical cold-rolled steel flatness control technologies and their limitations are analyzed as follows:

[0004] (1) Data-driven intelligent control methods: This type of method generates hierarchical control instructions by building a deep learning network to fuse multi-source monitoring data to optimize the actuator movement. Although it has certain accuracy advantages under steady-state conditions, its control effect is highly dependent on the completeness of offline training data. When instantaneous pressure fluctuations or sudden changes in the friction coefficient occur during the rolling process, the model generalization ability drops sharply. In addition, the complex computational structure of the neural network leads to real-time response delays, making it difficult to meet the dynamic adjustment requirements of high-speed rolling production lines.

[0005] (2) Static priority optimization algorithm: This method predefines the adjustment order of the actuators, such as tilt adjustment taking precedence over bending roll control, and uses a step-by-step calculation method to coordinate the actions of multiple mechanisms. Although it reduces the complexity of the control logic, the fixed priority strategy ignores the differences in the dynamic response characteristics of different actuators, such as the time delay difference between the hydraulic system and the mechanical device, resulting in a decrease in the efficiency of multi-mechanism coordination. At the same time, its optimization function does not integrate a real-time interference observation module, and its ability to suppress slow time-varying interference such as roll thermal deformation is limited.

[0006] (3) Iterative parameter self-learning strategy: The optimal control parameters are approached by cyclically correcting the efficiency factor. This method can theoretically improve the calculation accuracy of the adjustment amount. However, the parameter convergence process requires multiple rounds of iterative calculations, which can easily cause adjustment lag when the rolling schedule is frequently switched. What is more serious is that its self-learning mechanism is not coupled with online disturbance monitoring. When the rolling lubrication conditions suddenly change, it can easily cause overcompensation problems and aggravate plate shape oscillation.

[0007] In general, the existing cold-rolled steel plate shape control technology has the following common defects: (1) Weak dynamic disturbance suppression capability: Traditional methods rely on preset models or historical data. When the rolling conditions suddenly change, the control accuracy decreases due to insufficient model generalization capability. In addition, the real-time interference observation mechanism is not integrated, making it difficult to effectively suppress the coupling effects of multiple sources of dynamic interference such as roller thermal deformation and friction time variation on plate shape; (2) Imbalance between real-time control and accuracy: Optimization algorithms such as parameter self-learning require multiple iterative calculations, making it difficult to take into account both the rapid response requirements of high-speed rolling and the high-precision control requirements. Summary of the Invention

[0008] In response to the problems existing in the prior art, the present invention provides a cold-rolled steel plate shape anti-interference control method based on high-order sliding mode, which dynamically compensates for interference during the steel plate rolling process, improves the stability and real-time performance of anti-interference control, and ensures that the cold-rolled steel plate has higher quality.

[0009] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0010] A method for controlling the shape of cold-rolled steel plate based on a high-order sliding mode, comprising the following steps:

[0011] Step S1: collecting cold-rolled steel plate shape data in a rolling process in real time, determining the error between the collected cold-rolled steel plate shape data and the set cold-rolled steel plate shape data, and forming a cold-rolled steel plate shape error vector;

[0012] Step S2: The cold-rolled steel flatness error vector is combined with the flatness dynamics inverse equation to construct the cold-rolled steel flatness lumped noise;

[0013] Step S3: Calculate the cold-rolled steel plate shape noise spectrum in real time by Fourier transforming the cold-rolled steel plate shape lumped noise through a sliding window;

[0014] Step S4: Designing a filter cutoff frequency, and using a high-pass filter and a low-pass filter respectively to separate the slow-varying noise and the fast-varying noise in the cold-rolled steel plate shape noise spectrum according to the filter cutoff frequency;

[0015] Step S5: using the central difference method to estimate the derivative of the slowly varying noise, and combining the slowly varying noise to predict the slowly varying noise at the next moment;

[0016] Step S6: using a high-order sliding mode derivative observer to observe the cold-rolled steel flatness error vector, and designing a high-order sliding mode control law by combining the fast-varying noise and the predicted slow-varying noise;

[0017] Step S7: Use a high-order sliding mode control law to control the input signal during the rolling process to achieve anti-interference control of the cold-rolled steel plate shape.

[0018] Furthermore, the cold-rolled steel plate shape data includes: thickness, length and width of the cold-rolled steel plate.

[0019] Furthermore, the construction process of the cold-rolled steel plate lumped noise is as follows:

[0020]

[0021] in, express The lumped noise of cold-rolled steel plate at time express The cold rolled steel plate shape error vector at time , It represents the inverse equation of flatness dynamics determined by the set cold-rolled steel flatness data.

[0022] Furthermore, the calculation process of the cold-rolled steel plate noise spectrum is as follows:

[0023]

[0024] in, express The cold-rolled steel plate noise spectrum at the time, represents the integration variable, j represents the imaginary unit, Indicates the size of the sliding window.

[0025] Furthermore, when the frequency of the cold-rolled steel plate shape noise is greater than the filter cutoff frequency, a high-pass filter is used to separate the fast-varying noise in the cold-rolled steel plate shape noise spectrum; when the frequency of the cold-rolled steel plate shape noise is less than the filter cutoff frequency, a low-pass filter is used to separate the slow-varying noise in the cold-rolled steel plate shape noise spectrum.

[0026] Furthermore, the separation process of the slowly varying noise is as follows:

[0027]

[0028] in, express Slowly varying noise at any moment, represents the inverse Fourier transform, represents the filter cutoff frequency, Indicates that the filter cutoff frequency is A low-pass filter, , N represents the order of the low-pass filter, s represents the Laplace domain variable corresponding to the cold-rolled steel plate noise spectrum, They represent the gains under different low-pass filter orders.

[0029] Furthermore, the separation process of the fast-changing noise is as follows:

[0030]

[0031] in, express Fast-changing noise at all times, , M represents the order of the high-pass filter, Represent the gains under different high-pass filter orders.

[0032] Furthermore, the prediction process of the slowly varying noise at the next moment is:

[0033]

[0034] in, represents the difference step size, represents the predicted value of slowly varying noise at the next moment, express The derivative of .

[0035] Furthermore, step S6 includes the following sub-steps:

[0036] Step S6.1: Use a high-order sliding mode derivative observer to observe the cold-rolled steel flatness error vector and obtain an estimated value of the cold-rolled steel flatness error vector derivative. :

[0037]

[0038] in, t 0 represents the start time of the cold rolled steel plate shape anti-interference control method, denote the first observer gain parameter and the second observer gain parameter respectively, represents the dummy state variable of the derivative observer, represents a symbolic function;

[0039] Step S6.2: Define the sliding surface by the cold rolled steel flatness error vector and the estimated value of the cold rolled steel flatness error vector derivative ;

[0040] Step S6.3: Set the feedback gain, and design the integral continuous feedback gain based on the feedback gain, design the integral discontinuous feedback gain based on the mode of the separated fast-varying noise, and design a high-order sliding mode control law in combination with the predicted slow-varying noise.

[0041] Furthermore, the high-order sliding mode control law Designed to:

[0042]

[0043] in, represents feedback gain; represents the integral continuous feedback gain, ; represents the integral discontinuous feedback gain, , express Model.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The cold-rolled steel plate shape anti-interference control method based on high-order sliding mode of the present invention adopts a high-order sliding mode control law, and compensates the slow-varying interference in the control law through the predicted slow-varying noise, so that the slow-varying interference compensation accuracy is significantly improved, meeting the high-precision steel plate rolling requirements. At the same time, the integral discontinuous feedback gain is designed by separating the fast-varying noise, thereby enhancing the robustness of the anti-interference control, improving the control accuracy of the cold-rolled steel plate shape anti-interference, reducing the demand for computing resources for the cold-rolled steel plate shape anti-interference control, and improving computing efficiency and real-time performance; and, the cold-rolled steel plate shape anti-interference control method of the present invention can adopt continuous control input to compensate for disturbances, reduce the jitter caused by sliding mode control, improve the stability of the cold-rolled steel plate shape anti-interference control, and ensure the quality of the cold-rolled steel plate. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 Schematic diagram of the cold-rolled steel plate shape anti-interference control method based on high-order sliding mode of the present invention;

[0047] Figure 2 Schematic diagram comparing the flatness error of the cold-rolled steel flatness anti-interference control method based on high-order sliding mode of the present invention and the traditional sliding mode anti-interference control method, wherein: Figure 2 (a) is a schematic diagram of the flatness error using the traditional sliding mode anti-disturbance control method. Figure 2 (b) is a schematic diagram of the flatness error of the cold-rolled steel flatness anti-interference control method based on high-order sliding mode of the present invention. DETAILED DESCRIPTION

[0048] The technical solution of the present invention will be further explained below with reference to the accompanying drawings.

[0049] like Figure 1 Schematic diagram of the cold-rolled steel plate shape anti-interference control method based on high-order sliding mode of the present invention, the cold-rolled steel plate shape anti-interference control method comprises the following steps:

[0050] Step S1: Real-time collection of cold-rolled steel plate shape data during the rolling process, including the thickness, length and width of the cold-rolled steel plate, and determination of the error between the collected cold-rolled steel plate shape data and the set cold-rolled steel plate shape data to form a cold-rolled steel plate shape error vector.

[0051] Step S2: In order to analyze the interference in the cold-rolled steel rolling process and design an anti-interference control algorithm, the cold-rolled steel flatness error vector is combined with the flatness dynamics inverse equation to construct the cold-rolled steel flatness lumped noise ,in, express The lumped noise of cold-rolled steel plate at time express The cold rolled steel plate shape error vector at time , It represents the inverse equation of flatness dynamics determined by the set cold-rolled steel flatness data.

[0052] Step S3: Calculate the cold-rolled steel plate noise spectrum in real time by Fourier transforming the cold-rolled steel plate noise through sliding window ,in, express The cold-rolled steel plate noise spectrum at the time, represents the integration variable, j represents the imaginary unit, Indicates the size of the sliding window.

[0053] Step S4: Design a filter cutoff frequency. Based on the filter cutoff frequency, use a high-pass filter and a low-pass filter to separate the slow-varying noise and fast-varying noise from the cold-rolled steel flatness noise spectrum, respectively. This avoids the problem of using a sliding mode to completely suppress all noise, resulting in a large sliding mode gain and increased costs. Specifically, when the frequency of the cold-rolled steel flatness noise is greater than the filter cutoff frequency, use a high-pass filter to separate the fast-varying noise from the cold-rolled steel flatness noise spectrum. When the frequency of the cold-rolled steel flatness noise is less than the filter cutoff frequency, use a low-pass filter to separate the slow-varying noise from the cold-rolled steel flatness noise spectrum. In one embodiment of the present invention, the filter cutoff frequency is 1 Hz.

[0054] The separation process of the slowly varying noise in the present invention is as follows:

[0055]

[0056] in, express Slowly varying noise at any moment, represents the inverse Fourier transform, represents the filter cutoff frequency, Indicates that the filter cutoff frequency is A low-pass filter, , N represents the order of the low-pass filter, N ≥4; s represents the Laplace domain variable corresponding to the cold-rolled steel plate noise spectrum, Represent the gains under different low-pass filter orders.

[0057] The separation process of fast-changing noise in the present invention is as follows:

[0058]

[0059] in, express Fast-changing noise at all times, , M represents the order of the high-pass filter, M ≥4; Represent the gains under different high-pass filter orders.

[0060] Step S5: Using the low-frequency characteristics of the slowly varying noise, the derivative of the slowly varying noise is estimated using the central difference method. , and combined with the slowly varying noise to predict the slowly varying noise at the next moment , to achieve efficient feedforward compensation without observer and reduce computational complexity, where represents the difference step size, << .

[0061] Step S6: Use a high-order sliding mode derivative observer to observe the cold-rolled steel plate shape error vector, and design a high-order sliding mode control law by combining fast-varying noise and predicted slow-varying noise. The predicted slow-varying noise is used to compensate for the slow-varying interference in the control law, which significantly improves the accuracy of slow-varying interference compensation and meets the requirements of high-precision steel plate rolling. At the same time, the integral discontinuous feedback gain is designed by separating the fast-varying noise to enhance the robustness of the anti-interference control, improve the control accuracy of the cold-rolled steel plate shape anti-interference, reduce the demand for computing resources for the cold-rolled steel plate shape anti-interference control, and improve computing efficiency and real-time performance. Specifically including the following sub-steps:

[0062] Step S6.1: Use a high-order sliding mode derivative observer to observe the cold-rolled steel flatness error vector and obtain an estimated value of the cold-rolled steel flatness error vector derivative. :

[0063]

[0064] in, t 0 represents the start time of the cold rolled steel plate shape anti-interference control method, denote the first observer gain parameter and the second observer gain parameter respectively, and ; represents the dummy state variable of the derivative observer, represents the symbolic function, for n dimensional vector Each dimension of When , the value is 1; when When , the value is 0; when When , the value is -1; In a technical solution of the present invention, , , L Indicates the set length of cold-rolled steel plate;

[0065] Step S6.2: Define the sliding surface by the cold rolled steel flatness error vector and the estimated value of the cold rolled steel flatness error vector derivative ;

[0066] Step S6.3: Set the feedback gain and design the integral continuous feedback gain based on the feedback gain. Design the integral discontinuous feedback gain based on the separated fast-varying noise mode. Combined with the predicted slow-varying noise, design the high-order sliding mode control law. :

[0067]

[0068] in, is the feedback gain, ; represents the integral continuous feedback gain, ; Indicates the integral discontinuous feedback gain. In order to ensure the stability of high-order sliding mode and suppress fast-changing noise, it is required , express Model.

[0069] Step S7: Use a high-order sliding mode control law to control the input signal during the rolling process to achieve anti-interference control of the cold-rolled steel plate shape.

[0070] Figure 2 (a) is a schematic diagram of the flatness error using the traditional sliding mode anti-disturbance control method. It can be seen that the steel plate always has high-frequency vibration during the rolling process, resulting in a large flatness error. Figure 2 (b) is a schematic diagram of the plate shape error of the cold-rolled steel plate shape anti-interference control method based on high-order sliding mode of the present invention. It can be seen that the plate shape error of the steel plate approaches 0 during the rolling process, that is, the cold-rolled steel plate shape anti-interference control method of the present invention achieves efficient compensation and suppression of disturbances through slow-changing noise compensation and fast-changing noise suppression, thereby improving the quality of steel plate rolling.

[0071] In one technical solution of the present invention, a computer-readable storage medium is further provided, storing a computer program, wherein the computer program enables a computer to execute the anti-interference control method for cold-rolled steel plate shape based on high-order sliding mode.

[0072] In one technical solution of the present invention, an electronic device is also provided, comprising: a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the anti-interference control method for cold-rolled steel plate shape based on high-order sliding mode is implemented.

[0073] In the embodiments disclosed herein, computer storage media may be tangible media that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. Computer storage media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination of the foregoing. More specific examples of computer storage media may include electrical connections based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0074] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0075] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A cold-rolled steel plate shape anti-interference control method based on high-order sliding mode, characterized in that: The steps include: Step S1: collecting cold-rolled steel plate shape data in a rolling process in real time, determining the error between the collected cold-rolled steel plate shape data and the set cold-rolled steel plate shape data, and forming a cold-rolled steel plate shape error vector; Step S2: Combine the cold-rolled steel flatness error vector with the flatness dynamics inverse equation to construct the cold-rolled steel flatness lumped noise: in, express The lumped noise of cold-rolled steel plate at time express The cold rolled steel plate shape error vector at time , It represents the inverse equation of flatness dynamics determined by the set cold-rolled steel flatness data; Step S3: Calculate the cold-rolled steel plate noise spectrum in real time by Fourier transforming the cold-rolled steel plate noise through sliding window: in, express The cold-rolled steel plate noise spectrum at the time, represents the integration variable, j represents the imaginary unit, Indicates the size of the sliding window; Step S4: Designing a filter cutoff frequency, and using a high-pass filter and a low-pass filter respectively to separate the slow-varying noise and the fast-varying noise in the cold-rolled steel plate shape noise spectrum according to the filter cutoff frequency; when the frequency of the cold-rolled steel plate shape noise is greater than the filter cutoff frequency, using a high-pass filter to separate the fast-varying noise in the cold-rolled steel plate shape noise spectrum; when the frequency of the cold-rolled steel plate shape noise is less than the filter cutoff frequency, using a low-pass filter to separate the slow-varying noise in the cold-rolled steel plate shape noise spectrum; Step S5: using the central difference method to estimate the derivative of the slowly varying noise, and combining the slowly varying noise to predict the slowly varying noise at the next moment; Step S6: using a high-order sliding mode derivative observer to observe the cold-rolled steel flatness error vector, and designing a high-order sliding mode control law by combining the fast-varying noise and the predicted slow-varying noise; Step S7: Use a high-order sliding mode control law to control the input signal during the rolling process to achieve anti-interference control of the cold-rolled steel plate shape.

2. The method for anti-interference control of cold-rolled steel plate shape based on high-order sliding mode according to claim 1, characterized in that: The cold-rolled steel plate shape data includes: the thickness, length and width of the cold-rolled steel plate.

3. The method for anti-interference control of cold-rolled steel plate shape based on high-order sliding mode according to claim 1, characterized in that: The separation process of the slowly varying noise is as follows: in, express Slowly varying noise at any moment, represents the inverse Fourier transform, represents the filter cutoff frequency, Indicates that the filter cutoff frequency is A low-pass filter, , N represents the order of the low-pass filter, N ≥4, s represents the Laplace domain variable corresponding to the cold-rolled steel plate noise spectrum, Represent the gains under different low-pass filter orders.

4. The method for anti-interference control of cold-rolled steel plate shape based on high-order sliding mode according to claim 3, characterized in that: The separation process of the fast-changing noise is as follows: in, express Fast-changing noise at all times, Indicates that the filter cutoff frequency is A high-pass filter, , M represents the order of the high-pass filter, M ≥4, Represent the gains under different high-pass filter orders.

5. The method for anti-interference control of cold-rolled steel plate shape based on high-order sliding mode according to claim 4, characterized in that: The prediction process of the slowly varying noise at the next moment is: in, represents the difference step size, represents the predicted value of slowly varying noise at the next moment, express The derivative of .

6. The method for anti-interference control of cold-rolled steel plate shape based on high-order sliding mode according to claim 5, characterized in that: Step S6 includes the following sub-steps: Step S6.1: Use a high-order sliding mode derivative observer to observe the cold-rolled steel flatness error vector and obtain an estimated value of the cold-rolled steel flatness error vector derivative. : in, t 0 represents the start time of the cold rolled steel plate shape anti-interference control method, denote the first observer gain parameter and the second observer gain parameter respectively, and , represents the dummy state variable of the derivative observer, represents a symbolic function; Step S6.2: Define the sliding surface by the cold rolled steel flatness error vector and the estimated value of the cold rolled steel flatness error vector derivative ; Step S6.3: Set the feedback gain, and design the integral continuous feedback gain based on the feedback gain, design the integral discontinuous feedback gain based on the mode of the separated fast-varying noise, and design a high-order sliding mode control law in combination with the predicted slow-varying noise.

7. The method for anti-interference control of cold-rolled steel plate shape based on high-order sliding mode according to claim 6, characterized in that: The high-order sliding mode control law Designed to: in, is the feedback gain, ; represents the integral continuous feedback gain, ; represents the integral discontinuous feedback gain, , express Model.

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