基于混合集成建模的硅单晶直径自适应非线性预测控制  

Hybrid Integrated Modeling Based Adaptive Nonlinear Predictive Control of Silicon Single Crystal Diameter

作　　者：任俊超 刘丁[1,2] 万银 REN Jun-Chao;LIU Ding;WAN Yin(National&Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration,Xi'an University of Technology,Xi'an 710048;Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing,Xi'an 710048)

机构地区：[1]西安理工大学晶体生长设备及系统集成国家地方联合工程研究中心,西安710048 [2]陕西省复杂系统控制与智能信息处理重点实验室,西安710048

出　　处：《自动化学报》2020年第5期1004-1016,共13页Acta Automatica Sinica

基　　金：国家自然科学基金重点项目(61533014)资助。

摘　　要：大尺寸、电子级直拉硅单晶生长过程中物理变化复杂、多场多相耦合、模型不确定且存在大滞后和非线性等特性,因此如何实现硅单晶直径控制是一个具有理论意义和实际价值的问题.本文结合工程实际提出一种基于混合集成建模的晶体直径自适应非线性预测控制方法.首先,为了准确辨识晶体直径模型,提出基于互相关函数的时滞优化估计方法和基于Lipschitz商准则与模型拟合优度的模型阶次辨识方法;其次,基于"分而治之"原理构建晶体直径混合集成模型.其中,采用小波包分解(Wavelet packet decomposition,WPD)方法将原始数据分解成若干个子序列,以减少其非平稳性和随机噪声.极限学习机(Extreme learning machine,ELM)和长短时记忆网络(Long-short-term memory networks,LSTM)分别建立近似(低频)子序列和细节(高频)子序列的预测模型,最终晶体直径预测输出由各子序列的预测结果汇总而成;然后,针对晶体直径混合集成模型失配问题以及目标函数难以求解问题,提出一种基于蚁狮优化(Ant lion optimizer,ALO)的自适应非线性预测控制策略.最后,基于工程实验数据仿真分析,验证了所提建模及控制方法的有效性.Large-scale,electronic-grade Czochralski silicon single crystal growth process has complex physical changes,multi-field and multi-phase coupling,model uncertainty,and large lag and nonlinear characteristics.Therefore,how to control the silicon single crystal diameter is a problem of theoretical significance and practical value.Based on the engineering reality,this paper proposes a crystal diameter adaptive nonlinear predictive control method based on hybrid integrated modeling.Firstly,in order to accurately identify the crystal diameter model,a timedelay optimization estimation method based on cross-correlation function and a model order identification method based on Lipschitz quotient criterion and goodness-of-fit of the models are proposed;Secondly,based on the principle of"divide and conquer",a hybrid integrated model of crystal diameter is constructed.Here,wavelet packet decomposition(WPD)is used to decompose the raw data into several subsequences to reduce its non-stationarity and random noise.Extreme learning machines(ELM)and long-short-term memory networks(LSTM)establish prediction models of approximate(low-frequency)subsequences and detail(high-frequency)subsequences,respectively.The final crystal diameter prediction output is summarized by the prediction results of each subsequence;Then,in view of the mismatch of the crystal diameter hybrid integrated model and the difficulty of solving the objective function,a adaptive nonlinear predictive control strategy based on ant lion optimizer(ALO)is proposed.Finally,the effectiveness of the proposed modeling and control method is verified by the simulation analysis of engineering experimental data.

关 键 词：直拉硅单晶生长 直径控制 混合集成建模 模型辨识 自适应非线性预测控制 

分 类 号：TN304.12[电子电信—物理电子学] TP273[自动化与计算机技术—检测技术与自动化装置]