Model-based control methods do not produce satisfactory control results with the batch process control of Czochralski (CZ) silicon monocrystalline with complex nonlinearity, large delay, and time-varying dynamics. Therefore, this paper proposes a data-driven model-free adaptive iterative learning control method (MFAILC) to achieve precision control of the batch process. Firstly, to improve the accuracy of the data-driven model, a novel deep learning model for crystal growth process is established by combining a stacked autoencoder (SAE) and a long short-term memory network (LSTM) to extract the working condition information and the dynamic timing features in process data. Traditional model-based control methods are limited by the difficulties in modeling and by the unmodeled dynamics. So, to overcome this problem, the heater controller and the crystal puller controller are designed, based on the iterative dynamic linearization technology, to ensure that the silicon monocrystalline batch manufacturing process always maintains precision control. Also, the discrete-time extended state observer (ESO) is introduced to compensate for the influence of disturbances, to improve the robustness of the control system. Finally, the efficacy of the proposed method is verified by applying it for the predictive modeling and batch control of thermal field temperature and crystal diameter during crystal growth.

中文翻译：

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直拉硅单晶间歇过程的无模型自适应迭代学习控制方法


对于具有复杂非线性、大延迟和时变动态的直拉单晶硅的批量过程控制，基于模型的控制方法不能产生令人满意的控制结果。因此，本文提出一种数据驱动的无模型自适应迭代学习控制方法（MFAILC）来实现批处理过程的精确控制。首先，为了提高数据驱动模型的准确性，结合堆叠式自动编码器（SAE）和长短期记忆网络（LSTM）建立了一种新颖的晶体生长过程深度学习模型，提取工况信息并进行分析。过程数据中的动态计时特征。传统的基于模型的控制方法受到建模困难和未建模动态的限制。因此，为了克服这一问题，设计了基于迭代动态线性化技术的加热器控制器和拉晶机控制器，以确保单晶硅批量制造过程始终保持精确控制。同时引入离散时间扩展状态观测器（ESO）来补偿扰动的影响，提高控制系统的鲁棒性。最后，通过将其应用于晶体生长过程中热场温度和晶体直径的预测建模和批量控制，验证了该方法的有效性。