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Adaptive Control of Meniscus Velocity in Continuous Caster based on NARX Neural Network Model

机译：基于NARX神经网络模型的连铸机弯月面速度自适应控制

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Meniscus velocity in continuous casting is critical in determining the quality of the steel. Due to the complex nature of the various interacting phenomena in the process, designing model-based controllers can prove to be a challenge. In this paper a NARX neural network model is trained to describe the complex relationship between the applied current to an Electromagnetic Brake (EMBr) and the measured meniscus velocity. The data for the model is obtained using a laboratory scale continuous casting plant. Adaptive Model Predictive Control (MPC) was used to deal with the non-linearity of the model by adapting the prediction model to the different operating conditions. The controller uses the EMBr as an actuator to keep the meniscus velocity within the optimum range, and reject disturbances that occur during the casting process such as changing the casting speed.
机译：连铸中的弯月面速度对于确定钢的质量至关重要。由于过程中各种相互作用现象的复杂性，设计基于模型的控制器可能被证明是一项挑战。在本文中，训练了NARX神经网络模型来描述施加到电磁制动器（EMBr）的电流和测得的弯液面速度之间的复杂关系。该模型的数据是使用实验室规模的连续铸造厂获得的。自适应模型预测控制（MPC）用于通过使预测模型适应不同的运行条件来处理模型的非线性。控制器使用EMBr作为执行器，以将弯月面速度保持在最佳范围内，并消除铸造过程中发生的干扰，例如改变铸造速度。

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