Optimization-based feedforward control of the strip thickness profile in hot strip rolling

Prinz, Katharina; Steinboeck, Andreas; Kugi, Andreas

doi:10.1016/j.jprocont.2018.02.001

Cited by 39 publications

(11 citation statements)

References 22 publications

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“…We assume small bite angles α d , so that sin(α) ≈ α and cos(α) ≈ 1− α 2 2 holds. Using these approximations together with the relation x = R wr sin(α) simplifies (2) to the form…”

Section: Roll Gap Geometrymentioning

confidence: 99%

“…As a result, the requirements for the mathematical models of the hot strip finishing mill also increase. In particular, the roll gap model is very important because an accurate prediction of the roll force or the forward slip is required for several applications, e.g., process planning, calculation and adjustment of nominal operating points, observers for non-measurable quantities, and advanced model-based control concepts (see, e.g., [1,2]). In order to improve the prediction accuracy, it is essential to correctly capture the influence of various process parameters, such as rolling speed or friction between work rolls and material.…”

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confidence: 99%

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Asymmetric hydrodynamic roll gap model and its experimental validation

Müller

Steinboeck

Prinz

et al. 2018

Int J Adv Manuf Technol

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In tandem hot strip rolling mills, different friction between the rolls and the strip material on the upper and lower strip surface can occur due to asymmetric surface temperatures or different conditions of oil lubrication. To capture these effects, this paper presents a hydrodynamic roll gap model with asymmetric friction. Based on similarities between the rolled material and viscous fluids, fluid mechanics theory is used to derive this model. Due to the nature of this model, the influence of the rolling speed is inherently taken into account, which allows an accurate prediction of the rolling force and the forward slip. As an analytic solution for the hydrodynamic roll gap model is available, it is well suited for online applications in rolling plants. For validation of the proposed model, an experiment with asymmetric work roll roughness was performed. A specimen of steel strip with copper pins inserted was repeatedly rolled to visualize the material flow inside the roll gap for multiple passes. The resulting deformed copper pins were cut out of the strip and show good agreement with the deformation profiles calculated by the developed model.

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Section: Roll Gap Geometrymentioning

confidence: 99%

mentioning

confidence: 99%

Asymmetric hydrodynamic roll gap model and its experimental validation

Müller

Steinboeck

Prinz

et al. 2018

Int J Adv Manuf Technol

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show abstract

“…Wang et al proposed a new multivariable optimization algorithm with global convergence for a cold rolling mill flatness control [3]. Prinza et al developed a new feedforward control approach for the thickness profile of the strip in a tandem hot rolling mill [4]. However, they relied too much on the computing power of the controller.…”

Section: Introductionmentioning

confidence: 99%

Collaboration Strategy Based on Conflict Resolution for Flatness Actuator Group

Yan

Wang

et al. 2021

Mathematical Problems in Engineering

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During the flatness control process, there are frequently some uncoordinated regulating behaviors in the flatness actuator group. This has a bad influence on the flatness control accuracy and the flatness control efficiency. Therefore, a collaboration strategy based on conflict resolution for the flatness actuator group has been proposed in this paper. First of all, the feature of flatness measurement value is extracted through establishing the actual flatness condition discriminating factor. After that, the coordination cooperation that is appropriate to the actual flatness condition is developed for the flatness actuator group. Finally, the optimal adjustment of the actuator population is solved by the coordinated algorithm of Topkis-Veinott and genetic algorithm collaborative optimization. The collaboration strategy proposed in this paper has been successfully applied to a flatness control system of a 1450 mm five-stand cold rolling mill.

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“…In [12,13], feedforward control has achieved excellent results in machine tool control. In [14], the hot rolling mill is well controlled by the feedforward controller. But feedforward control is difficult to design because the precise mathematical model of the controlled plant is usually difficult to obtain.…”

Section: Introductionmentioning

confidence: 99%

Position Control of Magnetic Levitation Ball Based on an Improved Adagrad Algorithm and Deep Neural Network Feedforward Compensation Control

Wei

Huang

2020

Mathematical Problems in Engineering

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To control the position of the magnetic levitation ball more accurately, this paper proposes a deep neural network feedforward compensation controller based on an improved Adagrad optimization algorithm. The control structure of the controller consists of a deep neural network identifier, a deep neural network feedforward compensator, and a PID controller. First, the dynamic inverse model of the magnetic levitation ball is established by the deep neural network identifier which is trained online based on the improved Adagrad algorithm, and the trained network parameters are dynamically copied to the deep neural network feedforward compensator. Then, the position control of the magnetic levitation ball system is realized by the output of the feedforward compensator and the PID controller. Simulations and experiments illustrate that the accuracy of the deep network feedforward compensation control based on an improved Adagrad algorithm is higher, and its control system shows good dynamic and static performance and robustness to some extent.

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Optimization-based feedforward control of the strip thickness profile in hot strip rolling

Cited by 39 publications

References 22 publications

Asymmetric hydrodynamic roll gap model and its experimental validation

Asymmetric hydrodynamic roll gap model and its experimental validation

Collaboration Strategy Based on Conflict Resolution for Flatness Actuator Group

Position Control of Magnetic Levitation Ball Based on an Improved Adagrad Algorithm and Deep Neural Network Feedforward Compensation Control

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