EP3332883B1 - Auf metalldickensteuerungsmodell basierender inferenzsensor - Google Patents

Auf metalldickensteuerungsmodell basierender inferenzsensor Download PDF

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Publication number
EP3332883B1
EP3332883B1 EP17204486.9A EP17204486A EP3332883B1 EP 3332883 B1 EP3332883 B1 EP 3332883B1 EP 17204486 A EP17204486 A EP 17204486A EP 3332883 B1 EP3332883 B1 EP 3332883B1
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EP
European Patent Office
Prior art keywords
sheet metal
mill
model
thickness
controller
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EP17204486.9A
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English (en)
French (fr)
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EP3332883A1 (de
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Paul McGahan
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Honeywell International Inc
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Honeywell International Inc
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
    • B21B37/16—Control of thickness, width, diameter or other transverse dimensions
    • B21B37/165—Control of thickness, width, diameter or other transverse dimensions responsive mainly to the measured thickness of the product
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
    • B21B37/58—Roll-force control; Roll-gap control
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B2261/00—Product parameters
    • B21B2261/02—Transverse dimensions
    • B21B2261/04—Thickness, gauge
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B2265/00—Forming parameters
    • B21B2265/12—Rolling load or rolling pressure; roll force
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B2275/00—Mill drive parameters
    • B21B2275/02—Speed
    • B21B2275/04—Roll speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B2275/00—Mill drive parameters
    • B21B2275/02—Speed
    • B21B2275/06—Product speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
    • B21B37/58—Roll-force control; Roll-gap control
    • B21B37/66—Roll eccentricity compensation systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B38/00—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product
    • B21B38/04—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product for measuring thickness, width, diameter or other transverse dimensions of the product
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
    • B21B—ROLLING OF METAL
    • B21B38/00—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product
    • B21B38/08—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product for measuring roll-force

Definitions

  • a rolling mill stand typically has four or more rolls mounted in a vertical plane with two smaller diameter work rolls supported between larger-diameter back-up rolls.
  • Centerline thickness (gage) deviation is arguably the most important Key Performance Indicator (KPI) in any metal rolling application (ferrous, non-ferrous metals, hot or cold rolling).
  • KPI Key Performance Indicator
  • DE102006008574 discloses the influence of roller excentricity on the thickness of a rolled material is suppressed by identifying roller excentricity using a process model, and determining a correction signal for a control unit. The excentricity is established by supplying the measured tensile force to the process model.
  • JPS5825807 discloses a method to enhance an accuracy of thickness controlling of feed-forward sheet, by obtaining a controlling gain, which makes the deviation of measured rolled sheet thickness minimum, to correct a controlling gain in predictive controlling, and correcting on line the excess or deficiency of controlling quantity due to the change of quality of a rolling material during rolling, and a rolling mill.
  • a rolled sheet metal mill controller for controlling thickness of sheet metal produced by rolls of the mill, the controller comprising one or more processors and code stored on media readable by the one or more processors to control the thickness of the produced sheet metal, the controller including an input coupled to receive multiple measured mill parameters including produced sheet metal thickness that is time delayed from the production of the sheet metal, multiple models of the sheet metal mill including a rolling model (500) with a corresponding input of roll torque, a gap control model for mill stretch with a corresponding input of rolling force wherein the mill stretch is a non-linear function of rolling force, and a main drive model with a corresponding input of roll speed, wherein the sheet metal thickness is modeled as an input varying delay, and at least one internal disturbance model based on one or more of the multiple measured parameters coupled to the input, a Kalman filter based on the multiple models, and an output coupled to control a gap between the rolls that produce the rolled sheet metal.
  • the controller comprising one or more processors and code stored on media readable by the one or more processors to
  • a method of programming a controller for a rolled sheet metal mill including obtaining a physical representation of the rolled sheet metal mill, identifying available measurements for generating inferential estimates of internal states of the rolled sheet metal mill, correlating key internal disturbances to the available measurements to model the rolled sheet metal mill including a rolling model (500) with a corresponding input of roll torque, a gap control model for mill stretch with a corresponding input of rolling force wherein the mill stretch is a non-linear function of rolling force, and a main drive model with a corresponding input of roll speed, generating a Kalman filter based on the model, and adding the Kalman filter to the controller such that the controller is programmed to provide closed loop control thickness of sheet metal produced by the sheet metal mill.
  • a rolling model 500
  • a gap control model for mill stretch with a corresponding input of rolling force wherein the mill stretch is a non-linear function of rolling force
  • main drive model with a corresponding input of roll speed
  • a rolled sheet metal mill controller including a processor, a sensor, and a memory device coupled to the processor and having a program stored thereon for execution by the program processor to receive an input of multiple measured mill parameters from the sensor including produced sheet metal thickness that is time delayed from the production of the sheet metal, process multiple models of the sheet metal mill including a rolling model (500) with a corresponding input of roll torque, a gap control model for mill stretch with a corresponding input of rolling force wherein the mill stretch is a non-linear function of rolling force, and a main drive model with a corresponding input of roll speed, wherein the sheet metal thickness is modeled as an input varying delay, and at least one internal disturbance model based on one or more of the multiple measured parameters coupled to the input, execute a Kalman filter based on the multiple models, and provide an output coupled to control a gap between the rolls that produce the rolled sheet metal.
  • a rolling model 500
  • a gap control model for mill stretch with a corresponding input of rolling force wherein the mill stretch is a non-line
  • the functions or algorithms described herein may be implemented in software in one embodiment.
  • the software may consist of computer executable instructions stored on computer readable media or computer readable storage device such as one or more non-transitory memories or other type of hardware based storage devices, either local or networked.
  • modules which may be software, hardware, firmware or any combination thereof. Multiple functions may be performed in one or more modules as desired, and the embodiments described are merely examples.
  • the software may be executed on a digital signal processor, ASIC, microprocessor, or other type of processor operating on a computer system, such as a personal computer, server or other computer system, turning such computer system into a specifically programmed machine.
  • a model based inferential sensor may be used in a controller for controlling sheet metal thickness utilizes internal disturbance modelling and compensation.
  • roll eccentricity is modeled and compensated for by a rolling model.
  • An HGC (hydraulic gap control)_model accounts for mill stretch, which is a non-linear function of rolling force.
  • a main drive model is used to model main drive dynamics. The models may be represented as a series of non-linear ordinary differential equations.
  • Thickness measurement which is available downstream from the mill stand is delayed, and may be modeled as a communication delay which is clearly varying with mill speed.
  • a Kalman Filter may be used for systems with uncertain parameters. Parameter uncertainty may be incorporated in the Kalman Filter description with covariance updated accordingly.
  • a physical model of the process under consideration may be combined with available process measurements to provide estimates of unmeasurable process parameters in order to control the thickness of metal produced via the mill stand.
  • a systematic approach to inferential sensor design considers the influence of time delays, unmeasureable internal disturbances and parameter uncertainties.
  • FIG. 1 A schematic of a metal rolling mill with thickness control is shown at 100 in FIG. 1 .
  • Incoming material of thickness H is provided by a roll 105 of material that reduced through a multiplicity of rolls 110, 115, 120, and 125 (referred to as a stand 130) turning at a known speed ⁇ r and collected by a roll 106.
  • the stand 130 is equipped with a gap positioning system 135, which may be mechanical, hydraulic or a combination of both and may be controlled by a feedback device 140.
  • the control objective is to regulate this outgoing thickness h as closely as possible to the target h ref .
  • Many different sensors 145 may be used to measure thickness, with common devices including x-ray type gauges.
  • the control problem is significantly complicated by the presence of a (varying) transportation delay between and exit thickness measurement device, sensor 145, and the stand 130.
  • This time varying transportation delay is characterized by the distance between stand centerline L and the stand speed ⁇ r . It is well known that such time delay can have a destabilizing effect on control behavior and therefore it is critical that the delay is considered at control design stage.
  • Control structure 200 includes a controller 210 coupled to a representation of a plant or stand 215 corresponding to the hydraulic or gap positioning system 135 and thickness sensor to control the provision of sheet metal with measured thickness h, with a thickness feedback summed at 220 with a desired or reference thickness provided as input to the controller 210.
  • PI proportional/integral
  • a controller 310 is coupled to a plant 315, which also provides sheet metal of thickness h.
  • the controller 310 is coupled to a model 320 of the plant, which models the plant to provide an expected thickness to both a delay block 325 and a summing block 330.
  • the delay block 325 provides a delay commensurate with timing of the measured thickness h and results in subtraction of the modeled thickness from the measured thickness at 335.
  • the result is feedback 340 that is also provided to the summing block 330 to provide a feedback value subtracted from a reference thickness at 345, which is then provided to the controller.
  • Inferential sensing is a commonly used technique in control engineering.
  • An inferential sensor uses information available from other measurements and process parameters to calculate an estimate of the quantity of interest.
  • Typical motivations for construction of an inferential sensor are replacement of costly or impractical physical instrumentation or improvement of control performance through estimation of key (unmeasureable) process parameters.
  • a Kalman Filter based on statistical inference, is a commonly used software algorithm for implementation of inferential sensing technology. In this case, a physical model of the process under consideration is combined with available process measurements to provide estimates of unmeasurable process parameters.
  • Inferential sensing technology is widely used and widely misunderstood in metal rolling applications. Since the British iron and steel research association (BISRA) gauge was reported in the 1950's as a means of avoiding the influence of the time delay from stand to gauge, inferential sensing technology has become standard in most metal rolling automation solutions.
  • BIOSRA British iron and steel research association
  • the estimated thickness is used in either one of two ways. Firstly, the estimated exit thickness can be used to construct a feedback loop, similar to that shown in FIG. 2 . This observer based controller is shown generally at 400 in FIG. 4 , in which the estimated thickness feedback is provided at 410. Error! Reference source not found.
  • the estimated thickness can be used to construct a feed-forward compensation, which is typically added to the compensation from the original feedback loop shown in FIG. 2 .
  • This thickness estimate can then be used in a similar way as described with respect to FIG. 4 .
  • internal disturbance modelling and compensation may be performed.
  • Unmeasured, yet observable disturbances are of significant importance to thickness control in metal rolling applications. These disturbances are referred to as internal due to the fact that they manifest themselves on the internal states of the plant/model as opposed to appearing at either input/output directly.
  • Reasons for such internal disturbances are typically non-uniform cylindrical grinding on the mill stand rolls, thermo-mechanical variation in the mill stand rolls and hydro-dynamic effects of Back-Up Roll bearings (depending on bearing type and construction).
  • roll eccentricity is modeled and compensated.
  • Roll eccentricity may be caused by grinding inaccuracies during the manufacture, or preparation of rolls for use, deviations between the axis of the roll barrel and the roll neck or by non-uniform thermal expansion.
  • a simple schematic of eccentricity on a roll is shown at 500 in FIG. 5 as an actual roll shape shown by solid line 510 and an ideal roll shape as shown by broken line 515 which are effectively coaxial.
  • the amount of eccentricity is shown at a maximum eccentricity, e, at 520 as a difference between the ideal and actual radius.
  • Eccentricity compensation techniques can be broadly classified into two categories; passive and active. Passive compensation simply attempts to remove the gain effect described above in a mill-stretch compensation loop. Active compensation however generates a signal in the position or force control loop of the hydraulic positioning system in order to reduce the periodic disturbances in the strip.
  • Design of inferential sensors for thickness control in metal rolling applications has been rather ad-hoc, utilizing a significant amount of engineer insight and experience.
  • a systematic approach may be used for inferential sensor design for thickness control in metal rolling applications as illustrated at 600 in a flow diagram of FIG. 6 .
  • the first step in the Inferential Sensor construction workflow is modelling of a mill stand area as indicated at 610. Although this is valid for any type of mill (single stand, reversing or tandem), embodiments are described related to a mill with the geometry shown in FIG. 1 .
  • Model components include a rolling model, an HGC model, and a main drive model.
  • the strip exit gauge depends on the roll gap s , which is controlled by the hydraulic capsule, and on the mill stretch.
  • the mill stretch is in a turn a non-linear function of the rolling force.
  • a typical example of a stretch curve for a rolling mill stand is shown at 700 in FIG. 7 shown as mill stretch versus roll force.
  • variable transport delay may be treated in the Kalman Filter implementation as a communication delay.
  • the delay is at least partially a result of mill speed variations. Slower mill speed results in a larger delay, and faster mill speed results in a shorter delay of the thickness measurement.
  • the user can select various internal disturbances to be estimated at 630, depending on the type of rolling application under consideration. Although this could in principle be extended to any type of internal disturbance model that affects exit gauge, currently selectable are
  • the selected internal disturbances are then used to model the mill stand at 640, utilizing the available data.
  • a Kalman filter is then generated based on the model at 650 and may be integrated into the controller at 660.
  • the controller with the Kalman filter may be parameterized and the corresponding mill stand run to produce sheet metal.
  • the available data may then be collected, including the actual sheet metal thickness measurements.
  • the model may be adjusted by adjusting Kalman filter parameters based on the collected data, and the mill stand run again to ensure the model based controller is providing sheet metal of desired thickness.
  • Robustness of inferential sensors may be strongly dependent on model quality.
  • An example of this is model dynamics mismatch resulting in fake artifacts in inferred values.
  • FIG. 8 is a block diagram illustrating a rolled sheet metal system generally at 800.
  • System 800 includes a mill 810 that utilizes multiple rollers to produce sheet metal of a desired thickness from sheet metal stock.
  • Multiple sensors 815 provide measured data regarding the system 800, such as actual sheet metal thickness produced, roller speeds, roller forces and other available measurements.
  • the measured data is received at an input 820 of a controller 825.
  • Controller 825 includes one or more processors and code stored on media readable by the one or more processors to control the thickness of the produced sheet metal.
  • Controller 825 includes the input 820 that receives at least a measurement of sheet metal thickness that is time delayed from the production of the sheet metal.
  • the controller also includes multiple models 830, 835 of the sheet metal mill.
  • the sheet metal thickness is modeled as a communication delay.
  • the communication delay is a function of a variable transport delay input since the thickness cannot be directly measured between the rollers.
  • At least one of the models comprises an internal disturbance model based on one or more of the multiple measured data received at the input 820.
  • the models are used to form a Kalman filter 840.
  • An output 845 is coupled to the mill 810 to control a gap between the rolls that produce the rolled sheet metal.
  • the multiple models include one or more of a rolling model with a corresponding input of roll torque, gap control model with a corresponding input of rolling force, and a main drive model with a corresponding input of roll speed.
  • One or more internal disturbances that are modeled are selected from the group consisting of backup and work roll eccentricity, work roll thermal crown, work roll mechanical wear, and backup roll bearing flotation.
  • the Kalman filter may include filter parameters adjusted as a function of measured mill parameter values from operation of the sheet metal mill.
  • FIG. 9 is a block schematic diagram illustrating one example of thickness control for roll eccentricity compensation generally at 900.
  • a mill is illustrated in block form at 910 including multiple rollers and hydraulic gap control (HGC) to change a gap between rollers that changes the thickness of an input sheet of metal 915 from a source roll of metal 920.
  • the output sheet of metal 922 with changed thickness is collected on a collection roller 925.
  • the thickness of the output sheet of metal is measured by a thickness sensor 930, positioned downstream from the mill 910.
  • HGC hydraulic gap control
  • the thickness measurement or exit gage in mm, h is provided to a Kalman Filter (KF) 935, which also receives measured parameters s, F, and v via 940 measured by corresponding sensors of the mill 910.
  • S is the HGC position, or gap in mm.
  • F is the rolling load in tons, and v is the mill speed in m/minute.
  • the thickness estimation, ⁇ is provided to a summing junction 945 where it is combined with a reference thickness, href, corresponding to the desired thickness of the output sheet 922 and provided to a proportional/integral (PI) regulator 950 implementing a combination of proportional and integral control, C(s), to provide a control signal to a summing junction 955.
  • PI proportional/integral
  • Summing junction 550 combines the control signal with a feedforward eccentricity estimation ê from KF 935 to provide a position adjustment signal to the mill 910 to control the gap.
  • control of the gap is based on estimates of both the thickness at the time the metal sheet is run through the roller gap and an estimate of feed forward eccentricity, both of which are provided by KF 935.
  • FIG. 10 is a block schematic diagram illustrating an alternative example of thickness control for roll eccentricity compensation generally at 1000.
  • the elements of FIG. 10 have reference numbers corresponding to those of FIG. 9 .
  • the difference is that the summing junction 945 in FIG. 10 receives the measured thickness, h, direction from sensor 930 as opposed to receiving it from KF 935. Otherwise operations performed by thickness control 1000 are the same as those performed by thickness control 900.
  • FIG. 11 is a block schematic diagram of a computer system 1100 to implement the controller and methods according to example embodiments. All components need not be used in various embodiments.
  • One example computing device in the form of a computer 1100 may include a processing unit 1102, memory 1103, removable storage 1110, and non-removable storage 1112.
  • the example computing device is illustrated and described as computer 1100, the computing device may be in different forms in different embodiments.
  • the computing device may instead be a smartphone, a tablet, smartwatch, or other computing device including the same or similar elements as illustrated and described with regard to FIG. 11 .
  • Devices such as smartphones, tablets, and smartwatches are generally collectively referred to as mobile devices.
  • the various data storage elements are illustrated as part of the computer 1100, the storage may also or alternatively include cloud-based storage accessible via a network, such as the Internet.
  • Memory 1103 may include volatile memory 1114 and non-volatile memory 1108.
  • Computer 1100 may include - or have access to a computing environment that includes - a variety of computer-readable media, such as volatile memory 1114 and non-volatile memory 1108, removable storage 1110 and non-removable storage 1112.
  • Computer storage includes random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM) & electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD ROM), Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices capable of storing computer-readable instructions for execution to perform functions described herein.
  • RAM random access memory
  • ROM read only memory
  • EPROM erasable programmable read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • flash memory or other memory technologies
  • Computer 1100 may include or have access to a computing environment that includes input 1106, output 1104, and a communication connection 1116.
  • Output 1104 may include a display device, such as a touchscreen, that also may serve as an input device.
  • the input 1106 may include one or more of a touchscreen, touchpad, mouse, keyboard, camera, one or more device-specific buttons, one or more sensors integrated within or coupled via wired or wireless data connections to the computer 1100, and other input devices.
  • the computer may operate in a networked environment using a communication connection to connect to one or more remote computers, such as database servers, including cloud based servers and storage.
  • the remote computer may include a personal computer (PC), server, router, network PC, a peer device or other common network node, or the like.
  • the communication connection may include a Local Area Network (LAN), a Wide Area Network (WAN), cellular, WiFi, Bluetooth, or other networks.
  • Computer-readable instructions stored on a computer-readable storage device are executable by the processing unit 1102 of the computer 1100.
  • a hard drive, CD-ROM, and RAM are some examples of articles including a non-transitory computer-readable medium such as a storage device.
  • the terms computer-readable medium and storage device do not include carrier waves.
  • a computer program 1118 may be used to cause processing unit 1102 to perform one or more methods or algorithms described herein.
  • Example 1 includes a rolled sheet metal mill controller for controlling thickness of sheet metal produced by rolls of the mill, the controller comprising one or more processors and code stored on media readable by the one or more processors to control the thickness of the produced sheet metal, the controller including an input coupled to receive multiple measured mill parameters including produced sheet metal thickness that is time delayed from the production of the sheet metal, multiple models of the sheet metal mill, wherein the sheet metal thickness is modeled as an input varying delay, and at least one internal disturbance model based on one or more of the multiple measured parameters coupled to the input, a Kalman filter based on the multiple models, and an output coupled to control a gap between the rolls that produce the rolled sheet metal.
  • the controller comprising one or more processors and code stored on media readable by the one or more processors to control the thickness of the produced sheet metal
  • the controller including an input coupled to receive multiple measured mill parameters including produced sheet metal thickness that is time delayed from the production of the sheet metal, multiple models of the sheet metal mill, wherein the sheet metal thickness is modeled as an input varying delay,
  • Example 2 includes the rolled sheet metal mill of example 1 wherein the multiple models include a rolling model with a corresponding input of roll torque.
  • Example 3 includes the rolled sheet metal mill of any of examples 1-2 wherein the multiple models include a gap control model with a corresponding input of rolling force.
  • Example 4 includes the rolled sheet metal mill of any of examples 1-3 wherein the multiple models include a main drive model with a corresponding input of roll speed.
  • Example 5 includes the rolled sheet metal mill of any of examples 1-4 wherein the communication delay is a function of a variable transport delay input.
  • Example 6 includes the rolled sheet metal mill of any of examples 1-5 wherein one or more internal disturbances that are modeled are selected from the group consisting of backup and work roll eccentricity, work roll thermal crown, work roll mechanical wear, and backup roll bearing flotation.
  • Example 7 includes the rolled sheet metal mill of any of examples 1-6 wherein the Kalman filter is based on the models and is robust to known parametric uncertainties.
  • Example 8 includes the rolled sheet metal mill of any of examples 1-7 wherein the Kalman filter includes filter parameters adjusted as a function of measured mill parameter values from operation of the sheet metal mill.
  • a method of programming a controller for a rolled sheet metal mill including obtaining a physical representation of the rolled sheet metal mill, identifying available measurements for generating inferential estimates of internal states of the rolled sheet metal mill, correlating key internal disturbances to the available measurements to model the rolled sheet metal mill, generating a Kalman filter based on the model, and adding the Kalman filter to the controller such that the controller is programmed to provide closed loop control thickness of sheet metal produced by the sheet metal mill.
  • Example 10 includes the method of example 9 and further comprising testing the controller as a function of operation of the rolled sheet metal mill.
  • Example 11 includes the method of example 10 and further comprising adjusting Kalman filter parameters responsive to the testing.
  • Example 12 includes the method of any of examples 10-11 wherein the multiple models include a rolling model with a corresponding input of roll torque.
  • Example 13 includes the method of any of examples 10-12 wherein the multiple models include a gap control model with a corresponding input of rolling force.
  • Example 14 includes the method of any of examples 10-13 wherein the multiple models include a main drive model with a corresponding input of roll speed.
  • Example 15 includes the method of any of examples 10-14 wherein the communication delay is a function of a variable transport delay input.
  • Example 16 includes the method of any of examples 10-15 wherein one or more internal disturbances that are modeled are selected from the group consisting of backup and work roll eccentricity, work roll thermal crown, work roll mechanical wear, and backup roll bearing flotation.
  • a rolled sheet metal mill controller including a processor, a sensor, and a memory device coupled to the processor and having a program stored thereon for execution by the program processor to receive an input of multiple measured mill parameters from the sensor including produced sheet metal thickness that is time delayed from the production of the sheet metal, process multiple models of the sheet metal mill, wherein the sheet metal thickness is modeled as an input varying delay, and at least one internal disturbance model based on one or more of the multiple measured parameters coupled to the input, execute a Kalman filter based on the multiple models, and provide an output coupled to control a gap between the rolls that produce the rolled sheet metal.
  • Example 18 includes the controller of example 17 wherein the multiple models include a rolling model with a corresponding input of roll torque, a gap control model with a corresponding input of rolling force, and a main drive model with a corresponding input of roll speed.
  • the multiple models include a rolling model with a corresponding input of roll torque, a gap control model with a corresponding input of rolling force, and a main drive model with a corresponding input of roll speed.
  • Example 19 includes the controller of any of examples 17-18 wherein the communication delay is a function of a variable transport delay input.
  • Example 20 includes the controller of any of examples 17-19 wherein one or more internal disturbances that are modeled are selected from the group consisting of backup and work roll eccentricity, work roll thermal crown, work roll mechanical wear, and backup roll bearing flotation.

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Claims (11)

  1. Walzblechwalzwerk (910) mit Steuerung (900) zum Steuern der Dicke eines Blechs (922), das durch Walzen des Walzwerks (910) hergestellt wird, wobei die Steuerung (900) einen oder mehrere Prozessoren (1102) und Code (1118) umfasst, der auf Medien (1103) gespeichert ist, die von dem einen oder den mehreren Prozessoren (1102) gelesen werden können, um die Dicke des hergestellten Blechs (922) zu steuern, wobei die Steuerung (900) umfasst:
    eine Eingabe (940), die zum Empfangen mehrerer gemessener Walzparameter, einschließlich der produzierten Blechdicke (930), gekoppelt ist, der gegenüber der Herstellung des Blechs (922) zeitverzögert ist;
    mehrere Modelle (935) des Blechwalzwerks (910), einschließlich eines Walzmodells (500) mit einer entsprechenden Eingabe des Walzdrehmoments, eines Spaltregelungsmodells mit einer entsprechenden Eingabe der Walzkraft und eines Hauptantriebsmodells mit einer entsprechenden Eingabe der Walzgeschwindigkeit, wobei die Blechdicke als eine veränderliche Eingabeverzögerung modelliert wird, und mindestens ein internes Störungsmodell, das auf einem oder mehreren der mehreren gemessenen Parameter basiert, die mit der Eingabe (940) gekoppelt sind;
    ein Kalman-Filter (935), der auf den mehreren Modellen basiert; und
    eine Ausgabe (955), die zur Steuerung eines Spaltes zwischen den Walzen (910), die das gewalzte Blech produzieren, gekoppelt ist.
  2. Walzblechwalzwerk (910) nach Anspruch 1, wobei eine oder mehrere interne Störungen, die modelliert werden (935), aus der Gruppe ausgewählt sind, die aus der Stütz- und Arbeitswalzenexzentrizität, der thermischen Bombierung der Arbeitswalze, dem mechanischem Verschleiß der Arbeitswalze und der Flotation der Stützwalzenlager besteht.
  3. Walzblechwalzwerk (910) nach Anspruch 1, wobei das Kalman-Filter (935) Filterparameter einschließt, die in Abhängigkeit von gemessenen Walzparameterwerten aus dem Betrieb des Blechwalzwerks eingestellt werden.
  4. Walzblechwalzwerk (910) nach Anspruch 1, wobei die Steuerung (900) umfasst:
    einen Prozessor (1102); einen Sensor (930); und
    eine Speichervorrichtung (1103), die mit dem Prozessor (1102) gekoppelt ist und ein darauf gespeichertes Programm (1118) zur Ausführung durch den Programmprozessor (1102) aufweist zum:
    Empfangen der Eingabe (940);
    Verarbeiten mehrerer Modelle (935);
    Ausführen des Kalman-Filters (935) basierend auf den mehreren Modellen; und
    Bereitstellen der Ausgabe (955).
  5. Verfahren zum Programmieren einer Steuerung (900) für ein Walzblechwalzwerk (910), wobei das Verfahren umfasst:
    Erhalten (610) einer physikalischen Darstellung des Walzblechwalzwerks;
    Identifizieren (620) verfügbarer Messungen zum Erzeugen von Inferenzschätzungen interner Zustände des Walzblechwalzwerks;
    Korrelieren (630) wichtigster interner Störungen mit den verfügbaren Messungen zum Modellieren des Walzblechwalzwerks, wobei das Modell ein Walzmodell (500) mit einer entsprechenden Eingabe des Walzdrehmoments, ein Spaltregelungsmodell mit einer entsprechenden Eingabe der Walzkraft und ein Hauptantriebsmodell mit einer entsprechenden Eingabe der Walzgeschwindigkeit einschließt;
    Erzeugen (650) eines Kalman-Filters (935) basierend auf dem Modell; und
    Hinzufügen (660) des Kalman-Filters (935) zu der Steuerung (900), sodass die Steuerung (900) so programmiert ist, dass sie eine geschlossene Regelschleife für die Dicke des vom Blechwalzwerk (910) hergestellten Blechs (922) bereitstellt.
  6. Verfahren nach Anspruch 5, ferner umfassend die Prüfung der Steuerung (900) in Abhängigkeit vom Betrieb des Walzblechwalzwerks (910).
  7. Verfahren nach Anspruch 6, ferner umfassend das Einstellen der Parameter des Kalman-Filters (935) als Reaktion auf die Prüfung.
  8. Verfahren nach einem der Ansprüche 6 bis 7, wobei die Kommunikationsverzögerung von der variablen Transportverzögerungseingabe abhängig ist.
  9. Verfahren nach einem der Ansprüche 6 bis 7, wobei die eine oder die mehreren internen Störungen, die modelliert werden, aus der Gruppe ausgewählt werden, die aus einer Stütz- und Arbeitswalzenexzentrizität, der thermischen Bombierung der Arbeitswalze, dem mechanischen Verschleiß der Arbeitswalze und der Flotation der Stützwalzenlager besteht.
  10. Computerlesbares Medium mit Befehlen zur Ausführung durch einen Prozessor, um Arbeitsgänge zum Steuern eines Walzblechwalzwerks durchzuführen, wobei die Arbeitsgänge umfassen:
    Erhalten (610) einer physikalischen Darstellung des Walzblechwalzwerks;
    Identifizieren (620) verfügbarer Messungen zum Erzeugen von Inferenzschätzungen interner Zustände des Walzblechwalzwerks;
    Korrelieren (630) wichtigster interner Störungen mit den verfügbaren Messungen zum Modellieren des Walzblechwalzwerks, wobei das Modell ein Walzmodell (500) mit einer entsprechenden Eingabe des Walzdrehmoments, ein Spaltregelungsmodell mit einer entsprechenden Eingabe der Walzkraft und ein Hauptantriebsmodell mit einer entsprechenden Eingabe der Walzgeschwindigkeit einschließt;
    Erzeugen (650) eines Kalman-Filters (935) basierend auf dem Modell; und
    Hinzufügen (660) des Kalman-Filters (935) zu der Steuerung (900), sodass die Steuerung (900) so programmiert ist, dass sie eine geschlossene Regelschleife für die Dicke des vom Blechwalzwerk (910) hergestellten Blechs (922) bereitstellt.
  11. Computerlesbares Medium nach Anspruch 10, ferner umfassend die Prüfung der Steuerung (900) in Abhängigkeit vom Betrieb des Walzblechwalzwerks (910).
EP17204486.9A 2016-12-09 2017-11-29 Auf metalldickensteuerungsmodell basierender inferenzsensor Active EP3332883B1 (de)

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EP3936248B1 (de) 2020-07-07 2023-10-25 Primetals Technologies Germany GmbH Walzen unter berücksichtigung von frequenzverhalten
EP3974073B1 (de) 2020-09-28 2023-07-19 Primetals Technologies Germany GmbH Walzen unter berücksichtigung von frequenzverhalten
CN115990615B (zh) * 2021-10-20 2024-04-05 宁德时代新能源科技股份有限公司 辊压机的控制方法、装置及辊压机
CN116393522B (zh) * 2023-03-27 2024-03-22 西安艾蒙希科技有限公司 开口式四立柱二十辊轧机厚度处理系统

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JPS5825807A (ja) * 1981-08-10 1983-02-16 Nippon Steel Corp 帯板圧延板厚制御方法
JPS61193716A (ja) * 1985-02-25 1986-08-28 Nippon Steel Corp ストリツプ圧延板厚制御方法
US6263714B1 (en) * 1999-12-27 2001-07-24 Telepro, Inc. Periodic gauge deviation compensation system
SE529454C2 (sv) * 2005-12-30 2007-08-14 Abb Ab Förfarande och anordning för trimning och styrning
DE102006008574A1 (de) * 2006-02-22 2007-08-30 Siemens Ag Verfahren zur Unterdrückung des Einflusses von Walzenexzentrizitäten

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