Optimization-based feedforward control of the strip thickness profile in hot strip rolling. (April 2018)

• Record Type: Journal Article
• Authors: Prinz, K.; Steinboeck, A.; Kugi, A.
• Is Part Of: Journal of process control. Volume 64(2018)
• Page Start: 100
• Page End: 111
• Publication Date: 2018-04
• Subjects: Hot strip rolling -- Metals industry -- Thickness control -- Shape and profile control -- Model-based optimization -- Feedforward control; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2018.02.001
• Languages: English
• ISSNs: 0959-1524

Abstract

Graphical abstract: Highlights: A mathematical model for a hot strip tandem mill is developed and validated. A new optimization-based feedforward thickness controller is developed. A tailored numerically efficient solution of the optimization problem is presented. Simulation scenarios demonstrate the performance improvement of the proposed control concept. Promising results are obtained from a first installation of the control concept in the industrial plant. Abstract: A new feedforward control approach for the thickness profile of the strip in a tandem hot rolling mill is developed. In industry, the automatic gauge control (AGC) concept is widely used for thickness control. The AGC has the disadvantage that it does not consider known disturbances from upstream entities. This is why a number of disturbance feedforward control concepts have been proposed in the literature. These feedforward control strategies typically rely on linearized models and only provide symmetric control inputs for the mean thickness to the hydraulic adjustment system. In this work, an optimization-based feedforward controller for the lateral thickness profile is proposed that fully exploits all degrees of freedom available, i.e., the hydraulic cylinder positions and the bending forces at the drive side and at the operator side of the mill stand. Moreover, it is shown that by linearizing the mill stand model while keeping the nonlinearities from the roll gap model leads to a numerically efficient optimization problem, which is a good compromise between accuracy and computational efficiency. The feedforward controllers are further combined with the AGC in the feedback path in a two-degree-of-freedom controller structure to account for model-plant mismatch. Simulation results for a validated mathematical model and first experimental results from an industrial pilot installation show a significant benefit compared to the existing AGC without feedforward control.