间歇反应过程基于二维系统理论的迭代学习预测控制研究

项目名称： 间歇反应过程基于二维系统理论的迭代学习预测控制研究

项目编号： No.61473162

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 熊智华

作者单位： 清华大学

项目金额： 81万元

中文摘要： 间歇反应过程的先进控制对于提高生化制药等行业自动化水平具有重要的意义。针对反应过程机理复杂、干扰因素多、质量难控等问题，本项目拟采用二维系统理论研究基于线性时变扰动模型的迭代学习预测控制方法。间歇过程具有时间与批次的二维系统特征，而传统迭代学习控制仅在批次方向分析，本项目将从二维系统Roesser模型描述的系统动态响应出发，用方程解析系统状态演化和跟踪误差传递，从而更清晰地揭示迭代学习预测控制的收敛条件，进而研究预测控制与迭代学习控制两种方法有机结合的统一框架，揭示多种迭代学习控制方法的内在关系。为了克服初始条件偏差、反应参数扰动等问题，分析二维系统动态响应描述下的初始条件和扰动传递过程，研究鲁棒迭代学习预测控制算法。针对反应产物数据有限、单点或多点产品质量跟踪等问题，采用分片线性方法建立产品质量预报模型，研究产品质量的点对点跟踪迭代学习控制方法，形成较系统的迭代学习预测控制理论和方法。

中文关键词： 迭代学习控制；预测控制；间歇过程；二维系统理论

英文摘要： It is of great significance to develop the advanced control technologies of product quality of batch processes for improving the level of production automation. To address the problems of complex mechanism, diverse interference factors and difficult quality control in the batch processes, iterative learning predictive control (ILPC) based on the linear time-varying perturbation (LTVP) model is theoretically studied by using two-dimensional system theory in this project. The batch processes have the feature of two-dimensional system in both batch and time domains, but traditional iterative learning control is studied only in the batch domain. From the perspective of the dynamic responses described by Roesser model in the two-dimensional system, the evolution rules of system states and tracking errors can be derived theoretically and the convergence conditions of the ILPC method can be revealed more clearly. Furthermore, the uniform framework of combining iterative learning control with predictive control is proposed, and the internal relationships among various kinds of iterative learning control methods can be also revealed. To overcome the problems of initial condition errors and reaction parameter fluctuations, the transmission principles of initial condition and reaction parameter are described on the basis of the dynamic responses of the two-dimensional system, and then robust iterative learning predictive control (RILPC) can be developed. To address the problems of finite operation data from the product quality and tracking quality at one and more times, piece-wise linear method is used to build the predictive model of product quality, and then point-to-point iterative learning control of product quality is studied. Finally the systemic theory and methodology of the iterative learning predictive control will be realized in the project.

英文关键词： Iterative learning control;Model predictive control;Batch process;two-dimensional system theory