Derivation of a Mathematical Model for Strip Walking in Hot Strip Finishing Mills

Strip walking is an unstable lateral movement in a hot strip finishing mill that causes the strip to crash into the side guides, resulting in a tail pinch where the strip is rolled in a folded state. Strip walking should be appropriately controlled to prevent tail pinches, and an adequate mathematical model is essential to design the strip steering control. In this paper, the traditional model and a recently proposed model representing strip walking are numerically compared, and the latter is shown to characterize the details of the actual phenomenon. Such a mathematical model derivation makes it possible to use model predictive control to optimally control the strip walking, considering the rate limit constraint of the actuator. However, model predictive control requires time-consuming optimization computation. Thus, control performance deteriorates with longer control periods. In this paper, we propose a new model prediction control method to reduce optimization computation time with discretized control input that enables fast discrete optimization instead of slow continuous optimization. It is also shown that fixing the misalignment between the roll and steel strip centers is insufficient to prevent the tail pinch. Moreover, it is vital to suppress the rotational motion of the steel strip by using numerical simulations.

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熱間仕上圧延における鋼板蛇行制御のための詳細モデルとオブザーバによるモデル予測制御

YAMAMOTO,

MATSUDA,

MORISHITA

et al. 2024

Transactions of the JSME (In Japanese)

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In a hot strip finishing mill, an unstable lateral steel strip movement called strip walking causes the strip to crash into side guides placed at the entrance of the stand mill, resulting in a tail pinch where the strip is pressed into a folded state. Therefore, strip walking should appropriately be suppressed to prevent tail pinches, requiring adequate strip steering control. To this end, various strip steering control methods have been proposed. Among them, model predictive control, which explicitly considers the motion of a steel strip and constraints on the actuator's speed, is expected to be of great practical use. However, the disadvantages of this control are that the model's accuracy affects control results, state variables must be estimated, and its optimization computation is time-consuming. Hence, this paper proposes a method to overcome such disadvantages by adopting a model describing adequately strip walking, model predictive control using discretized inputs, and a deadbeat current observer to estimate state variables. Subsequently, we demonstrated the effectiveness of the proposed method using several numerical simulations together with setting discrete values for the discretized inputs and setting weights in the cost function to be minimized in the model predictive control. In conclusion, it is clarified here that the selection of a set of discretized control inputs, which maximize the leveling speed in minimum time and the proper selection of weights in the evaluation function are crucial for the practical use of model predictive control to prevent strip walking.

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Derivation of a Mathematical Model for Strip Walking in Hot Strip Finishing Mills

Cited by 3 publications

References 6 publications

Temporal online self-learning stochastic configuration networks: A study on strip deviation prediction

Temporal online self-learning stochastic configuration networks: A study on strip deviation prediction

Improvement of a Mathematical Model and Model Predictive Control for Strip Steering Control in Hot Strip Finishing Mill

熱間仕上圧延における鋼板蛇行制御のための詳細モデルとオブザーバによるモデル予測制御

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