WO2005072886A1

WO2005072886A1 - Control method and control device for a roll stand

Info

Publication number: WO2005072886A1
Application number: PCT/EP2005/000845
Authority: WO
Inventors: Mohieddine Jelali; Andreas Wolff; Ulrich Müller; Frank Gorgels; Roger Lathe; Gert Mücke
Original assignee: Betriebsforschungsinstitut VDEh-Institut für angewandte Forschung GmbH
Priority date: 2004-01-30
Filing date: 2005-01-28
Publication date: 2005-08-11
Also published as: ATE442918T1; DE502005008137D1; DE102004005011B4; EP1711283B1; EP1711283A1; DE102004005011A1

Abstract

According to the invention, the thickness, traction forces and flatness is controlled by means of a single control device as part of an integrated, model-predictive thickness, traction and flatness control operation. The integrated control device takes into account the influence the adjustment of the control variables has on the thickness, the traction force and the flatness of the rolled strip and optimises the modifications of the control variable in such a way that a selected quality of the thickness and the flatness control is obtained. The quality of the thickness and the quality of the flatness control, which are weighted differently, can effect the quality function of the control device. The use of the integrated control device improves the performance and the stability of the control device in relation to the control devices which are arranged in a separate manner from each other.

Description

"Control method and controller for a roll stand"

The invention relates to a control method and a controller for a roll stand, or for the roll stands of a rolling mill.

When rolling strips, in particular cold rolling, separate control systems are provided for regulating the flatness and the thickness of the strip at the outlet of the stand and for regulating the tension acting on the strip.

A control system for the strip flatness is described for example in EP 1 181 992 A2. There, the flatness of the strip is recorded using a measuring system and compared with the result of an explicit, linear or non-linear online profile and flatness model that takes into account all the essential variables involved in the rolling process (bending, swiveling, shifting, thermal crowning). The flatness error determined is broken down into orthogonal components for simplified optimization in the control system. This generation of the flatness error to be taken into account in the following regulation is designed as an event-triggered scanning system for taking into account flatness measuring systems with a variable sampling time. The flatness error determined is fed to a multivariable controller. The known control system also has a prediction of the controlled variable, which is included in the dynamic optimization and extends beyond the dead time. There is also a feedforward control that takes into account the properties of the incoming strip, the variation of the rolling force and thermal crowning. However, it has been shown that this control system does not meet the high quality requirements for the rolled product.

An integrated thickness and flatness control for 20-roll stands is described in Pu H., Nern H.-J., Roemer R., Nour Eidin HA, Kern P., Jelali M .: State- observer design and verification towards developing an integrated flatness - thickness control system for the 20 roll sendzimir düster mill, Proc. Intern. Conf. on Steel Rolling (Steel Rolling '98), 1998, The Iron and Steel Institute of Japan, Chiba, p. 124-129, and Pu H., Nern H.-J., Nour Eidin HA, Jelali M., Totz O., Kern P .: The Hardware-in-Loop simulations and online tests of an integrated thickness and flatness control system for the 20 rolls sendzimir cold rolling mill, Proc. Intern. Conf. on Modeling of Metal Rolling Processes, 1999, London, p. 208-216 and Pu H., Mikhailov L., Nern H.-J., Kern P., Nour Eidin HA: Optimal control of thickness and flatness for the 20 rolls Sendzimir cold rolling mill, Proc. IFAC World Congress, 1999, Beijing, China, p. 481-486. The control methods described there also cannot meet the high quality requirements for the rolled product.

Against this background, the object of the invention is to create an improved control method for a roll stand.

This object is solved by the subject matter of the independent claims. Advantageous refinements are specified in the subclaims.

The invention is based on the basic idea of regulating the thickness, tension and flatness with a single controller in the context of an integrated, model-predictive thickness, tension and flatness control. The integrated control takes into account the influence that the adjustment of manipulated variables has on the thickness, tension and flatness of the rolled strip and can optimize the manipulated variable change in such a way that a selected quality of the thickness control and the flatness control is achieved. The quality of the thickness control and the quality of the flatness control can be weighted differently in the quality function of the controller. The use of an integrated control leads to an improvement in performance and stability in the control compared to controls that are designed separately from one another. It was found that an interaction of the two control loops can be determined in particular due to the cross-coupling between thickness and flatness. In the case of thin strips in particular, the prescribed thickness tolerance and the desired flatness could not be achieved at the same time. In general, the flatness control was set less quickly to avoid the risk of instability. As a result, however, it cannot react to rapidly changing flatness errors, so that the flatness quality that can be achieved is severely limited. By taking the thickness control quality and the flatness control quality into account at the same time, a common controller can regulate both the prescribed thickness tolerance and the desired flatness.

In addition, the final thickness, particularly in the case of very small strip thicknesses, such as occur, for example, in cold rolling, depends on the trains applied. The rolling forces also depend on the strip tension. This results in a strong coupling between tension, thickness and flatness control. According to the invention, these are taken into account in the one controller by the model used by the controller. As a result, as is conventional, the thickness and flatness control no longer have to be set more slowly, so that even rapid fluctuations in thickness and flatness can be regulated well.

For this purpose, input variables for the controller are generated as a function of the measured values of the measuring systems in the control method according to the invention. These input variables are used by the controller to generate at least one control signal for at least one control variable of the roll stand based on an integrated, model-predictive thickness, tension and flatness control. Separate measuring systems for the thickness, flatness and tension of the strip are preferably provided. However, measurement systems can also be used within the scope of this invention which simultaneously determine several sizes, for example the thickness and the flatness. The control variables of the rolling process are understood in particular to be roll bending, roll swiveling, roll shifting, roll cooling, in particular selective multi-zone cooling and also changing the shape of the backup roll.

The controller uses a prediction model to predictively predict the future system behavior. The controller is preferably an MPC controller (Model Predictive Control), which is embedded in an IMC structure (Infernal Model Control). For example, MPC controllers are well known from Camacho EF, Bordons C: Model Predictive Control, Springer, 1999, and Maciejowski JM: Predictive Control with Constraints, Prentice Hall, 2002, which is why they are fully relevant for the description of MPC controllers and their design reference is made to these publications. MC structures are particularly from Garcia C.E., Morari M .: Infernal model control. 1. A unifying review and some new results, Ind. Eng. Chem. Process Des. Dev. 21 (1982), p. 308-323, well known, which is why full reference is made to this publication for the description of IMC structures and their interpretation.

The controller preferably also includes an integrated thickness profile control. The manipulated variables that are used to control the thickness profile often correspond to those that are also used to control the thickness and flatness.

In a preferred embodiment, the controller is constructed as a multivariable controller with dynamic optimization, into which the thickness and the flatness are weighted differently. A dynamic optimization algorithm is preferably used to determine optimal actuator positions, taking into account predetermined manipulated variable restrictions of the manipulated and controlled variables. For example, the quality of the controller can be represented in the controller by the following cost function: J Süicke> Spianheit) ^- Soicke J Dicke ⁺ <§Planheit Α Flatness

In this merit function the quality functions for the thickness Joic go _ke and for the flatness Jpianneit by the weighting factors g _D icke and gi _a n _t _he i a weighted differently. For production reasons, for example, compliance with the thickness tolerance can be assigned a greater weight, i.e. a goic> gpi _a n _he i _t . Deviations from the target thickness are punished much more than deviations from the target flatness.

The quality functions for thickness and flatness can be represented as follows, for example:

1 part

^ Thickness = - ( ^Ä soll - ^A ist ( ^σ )) ÖDioke (ü ~ K ( ^σ )) 1 T

^• ^ flatness ⁼ ~ A (^ should ~ ^ is) openness (^ should ~~ ^ is)

Alternatively, the quality of the thickness control and the quality of the flatness control can be achieved by the following quality function:

HU ¹¹ lanheitC ") u Hst ~ ^" "Soll | - ^ tolmax '| ^σ ist ^- ' - '^" Soll | - ^{σ "} tolmax

The quadratic flatness error is minimized under the proviso that the thickness deviation must always be smaller than an upper bound. The solvability of this optimization problem can be guaranteed by suitable measures, for example with the help of a feasible SQP algorithm (see Maciejowski JM: Predictive Control with Constraints, Prentice Hall, 2002, to which reference is made in full for the solvability of the optimization problem becomes). In the optimization algorithms mentioned, the train to be taken into account is usually included in the thickness, since the thickness strongly depends on the train. However, optimization algorithms can also be set up that optimize the three sizes thickness, flatness and tension separately.

The prediction of the controlled variable is preferably included in the dynamic optimization. The prediction preferably goes beyond dead time compensation.

The controller preferably takes into account restrictions, in particular for the actuating signals of the manipulated variables and the systematic softening of the restrictions depending on their importance for the trouble-free operation of the roll stand. The restrictions are in particular absolute values and rates of change of the manipulated variables. This ensures the feasibility of the optimization problem. As a result, certain boundary conditions of the rolling stand can be met when rolling sheet metal. For example, for crown excenter of a Sendzimir mill stand, only certain maximum relative positions with each other are allowed. By taking such boundary conditions into account, the setting options of the roll stands, for example a Sendzimir stand, can be fully utilized.

The controller preferably uses an explicit, linear or non-linear, online-capable profile and flatness model that takes into account the essential variables and actuators involved in the rolling process, in particular roll bending, roll swiveling, roll shifting, roll cooling and / or the change in the support roll shape. The design of such models is, for example, from Berger B., Mücke G., Neuschütz E., Fleischer H. (1982) Regulation of flatness and tension distribution on a 20-roll cold rolling mill, BFI report No. 893 (final report of the ECSC research project No. 7210.EA 109); Jelali M. (2000) Explicit modeis of thickness profile and tension stress distribution for process control applications. Steel Research 71, No. 6 + 7, 228-232; Schneider A. (2000) Online Modeling and Optimization for a 20-high Rolling, Mill. Dissertation, University of Wuppertal well known, which is why full reference is made to these publications for the description of such models and their interpretation. In a preferred embodiment of the control method, an explicit, online-capable function model is also used, which calculates setpoints for the flatness control.

Simplified prediction models are preferably used in part in the controller, for example by linearizing and simplifying corresponding relationships of the complex model. The design of such models is, for example, from Jelali M. (2000) Explicit modeis of thickness profile and tension stress distribution for process control applications. Steel Research 71, No. 6 + 7, 228-232 well known, which is why full reference is made to these publications for the description of such models and their interpretation.

The control method preferably has at least one adaptation method for the online-compatible profile and flatness model, the online-compatible prediction model and / or a set-up model. The adaptation method preferably adapts selected parameters of the model components. These adaptation methods should preferably be designed to be robust against model structure errors. By adapting the models, changes in the dynamic behavior of the roll stand can be taken into account, such as those resulting from wear or the replacement of components. In this way, the adaptation process can adapt the setup models from stitch to stitch.

According to an advantageous embodiment of the control method according to the invention, an event-triggered scanning system is used which takes into account flatness measuring systems with variable scanning times. In order to keep the number of controlled variables as small as possible and thus to simplify the optimization problem, according to an advantageous embodiment of the control method, the flatness course determined from the measurements of the flatness measuring system is broken down into orthogonal components with the help of orthogonal function systems. In particular, the Chebyshew polynomial (for this, full reference is made to Press WH, Teukolsky SA, Vetterling WT, Flannery BP: Numerical Recipies in C, Cambridge University Press, 1992), the Gram polynomial (for this, Ralston is referred to in full) A., Rabinowitz P .: A first Course in Numerical Analysis, International series in pure applied mathematics, McGraw-Hill, 1978) or other orthogonal polynomials.

Interferences resulting from this can be compensated for by a feedforward control used in a preferred embodiment, which takes into account the properties of the incoming strip, the variation of the rolling force and / or the variation of the thermal crown. The interference can be compensated for in a separate module or integrated into the model predictive control.

The flatness measurement system is preferably used to estimate the flatness profile over the width of the strip on the basis of measurement results which are determined at different times and in particular at different points along the width of the strip. The current manipulated variables are preferably taken into account here. The flatness course is particularly preferably determined immediately after the next new measured value of a sensor is available. This produces a more up-to-date estimate of the flatness curve, which is not dependent on all measured values being available across the width of the strip. A switching Kaiman filter is preferably used to calculate the flatness during one revolution of the measuring roller. For the construction of a switching Cayman filter, please refer to the parallel application 103 06 837.6. This determination of the flatness course can also be used when using flatness measuring rolls, such as those used in cold rolling, the planning process can be determined promptly. Sensors are arranged on the flatness measuring roller distributed over the radius and the width. These provide information about the flatness of the strip present at the respective location at the respective measuring point in time, spaced from one another and with respect to the width position. By estimating the flatness course after the determination of each individual measured value, rapidly changing flatness changes can also be taken into account. In the case of measuring systems in which all measured values are initially determined over the width of the strip and a complete flatness distribution can only be determined after a complete rotation of the flatness measuring roller, especially in the case of rapidly changing flatness distributions, this leads to considerable measuring errors.

A controller according to the invention implements the previously described method steps and properties individually or in combination.

By integrating the thickness, tension and flatness control in one controller or by building a coordination component of these controls, performance deterioration and stability problems can be avoided. The coupling that exists between thickness, tension and flatness is taken into account by a decoupling matrix, which can be calculated from the inverse of the overall transmission matrix from the thickness and flatness control system.

The invention is explained in more detail below with reference to a drawing. An embodiment is shown in this drawing. It shows the structure of the control method according to the invention.

The control structure shown, as can be used, for example, for a cold rolling mill (rolling mill) 1, has above all a flatness measuring system 2, a multivariable controller 3 (MPC module thickness and flatness) and a draft control 4. A flatness measuring system 2 can be arranged both in front of and behind the scaffold 1 so that the scaffold 1 can be used in reversing operation. The rolling stand 1 is used in this exemplary embodiment for rolling very thin strips. There is a strong link between the influencing variables thickness and tension on the rolling result.

As shown, the flatness deviation is determined by means of a flatness measuring system at the scaffold outlet. The flatness measuring system is preferably based on a flatness measuring roller. This measures the belt tension discretely at individual measuring points distributed over the measuring roller width and the measuring roller circumference. The flatness course (flatness distribution) is estimated directly from the individual measurement results. The estimated flatness course is broken down into orthogonal (independent) components. The type of decomposition used is changed depending on the type of flatness error that occurs in order to describe the flatness error with as few components as possible and thus to simplify the optimization problem.

The orthogonal components determined in this way are compared with values provided by an online-capable model of the system. The resulting difference is used as a controlled variable and fed to the multivariable controller 3. There is a comparison with a target flatness curve broken down into independent components. The multivariable controller consists of an online-capable model and dynamic optimization, taking into account manipulated variable restrictions and the predicted process variable curve.

From the input variable, the controller determines control signals for roll bending, roll swiveling, axial shifting of the rolls, as well as for multi-zone cooling and, if necessary, a change in the support roll shape. To take into account the influence of the rolling force, the properties of the incoming strip (for example, one measured in the flatness, and the thermal crowning), a disturbance variable is applied to compensate for these influences. In addition, starting from the desired flatness curve, a pilot control is carried out, which is likewise introduced into the actuating signals determined by the multivariable controller.

Depending on the strip thickness of the strip to be rolled, the controller selects a primary manipulated variable via which it preferably influences the rolling process. For strip thicknesses below a specified size, the train is used as the primary manipulated variable. The pitch force and the pitch position of the rollers are then treated as an additional secondary manipulated variable.

In order to compensate for changes in the dynamic behavior, which can be caused by wear, the replacement of components of the roll stand and changes in the material properties of the roll stand, for example, the models are adapted online while rolling a single strip (in-bar adaptation). In addition, there is an adaptation from federal to federal government. The in-bar adaptation compensates for relatively rapid changes, for example caused by changes in the temperature of the belt, while the bundle-to-bundle adaptation compensates for wear-related changes.

An event generator allows the use of an event-triggered scanning system to take into account flatness measuring systems with variable scanning times.

Claims

claims

1. Control method for a roll stand with measuring systems for detecting the thickness, tension and flatness and a controller, characterized in that input variables for the controller are generated as a function of the measured values of the measuring systems and that the controller has at least one control signal for at least one control variable of the Roll stand generated due to an integrated, model-predictive thickness, tension and flatness control.

2. Control method according to claim 1, characterized by a multivariable controller which has a dynamic optimization into which the thickness and the flatness are weighted differently.

3. Control method according to claim 2, characterized in that a prediction of the control variables is included in the dynamic optimization.

4. Control method according to claim 2 or 3, characterized in that the restrictions and the systematic softening of the restrictions are included in the dynamic optimization.

5. Control method according to one of claims 1 to 4, characterized in that the controller uses an explicit, linear or non-linear, online-capable profile and flatness model.

6. Control method according to one of claims 1 to 5, characterized by at least one simplified prediction model.

7. Control method according to one of claims 5 or 6, characterized by an adaptation method for the online-capable profile and plan unit model, the online-capable prediction model and / or a set-up model.

8. Control method according to one of claims 1 to 7, characterized by a feedforward control which compensates for the disturbances resulting from the properties of the incoming strip, the variation of the rolling force and / or thermal bombing.

9. Control method according to one of claims 1 to 8, characterized in that the flatness course determined from a measurement of the flatness measuring system is broken down into orthogonal components with the aid of orthogonal function systems.

10. Control method according to one of claims 1 to 9 with a method for determining the flatness during the rotation of a measuring roller by means of measurements which are offset with respect to one another transversely to the strip running direction, with the following steps: determining a measured value at at least one measuring point, calculating the characterizing values a flatness curve equation from the measured value (s) determined by means of a switching caiman filter.

11. Controller for a roll stand, which implements the control steps of a control method according to one of claims 1 to 10.