Model-based control of the thickness profile in hot strip rolling
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Beschreibung
This thesis deals with the industrial process of hot strip rolling, where steel strips are deformed in several consecutive rolling mill stands to a certain desired target thickness. The aim of this work is to analyze existing control methods and to investigate new strategies to improve the accuracy and uniformity of the thickness profiles of the finished products.
A detailed mathematical model of the considered mill stand is developed and validated. The simulation model yields the evolution of the exit thickness profile and of the roll force based on the entry properties of the strip and the inputs for the mill stand setup. The objectives and the general possibilities for thickness control in hot strip rolling are discussed. The available actuators and measurements as well as typical disturbances are explained. The standard control structure, the AGC/HGC loop, is described and used as benchmark for the developed feedforward control concepts. The feedforward control concepts are based on a static mathematical model of the exit thickness profile. Three feedforward control approaches are discussed: for the average thickness in lateral direction, for the asymmetries in the lateral direction (the wedge-shape of the exit thickness profile), and for the entire shape of the exit thickness profile. In the latter case, the whole thickness profile is considered in an objective function of an opimization-based algorithm. The control concepts are validated and compared in simulations. The feedforward control strategy enhanced by an adaptive strategy using model parameters that are estimated online. In pilot installations, proposed control concepts have been implemented and are now used in permanent long-time operation.
Autor*in
Katharina Prinz
Themen in »Model-based control of the thickness profile in hot strip rolling«
Adaptive control Control system analysis Disturbance feedforward control Heavy-plate rolling Hot strip rolling Metals industry Model-based optimization Parameter identification