基于数据驱动子空间方法的多工况过程预测控制

项目名称： 基于数据驱动子空间方法的多工况过程预测控制

项目编号： No.61203070

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 杨华

作者单位： 中国海洋大学

项目金额： 24万元

中文摘要： 很多复杂非线性过程具有多工况特性，如间歇过程的过程变量相关关系并非随时间时刻变化，而是呈现出分时段的区域特性，这一特性使得难以把多工况过程当作一个MIMO系统进行建模并据此实现控制器的设计。因此，本项研究面向多工况间歇过程的优化控制需求，应用子空间方法实现对过程时变动态和演变规律的表征，提出数据驱动的多工况过程预测控制理论框架，实现对控制器设计复杂度的改善，以及对间歇过程控制精度的提高。包括如下内容：(1)基于子空间方法提出高维数据的展开预处理方式，支持对分析单元进行时间和批次的双向扩展，并实现对多工况间歇过程的降维处理；(2) 提出带区域遗忘因子的子空间方法，将过程分解成若干操作子时段，建立起基于多工况的过程分析和优化方法；(3)设计多工况过程的性能评价基准，基于灵敏度方法进行分片优化问题的求解，改善对间歇过程的跟踪和抗干扰控制能力，实现数据驱动的多工况过程预测控制器设计。

中文关键词： 多工况过程；数据驱动；海洋湍流；多尺度；层级结构

英文摘要： Multiphase operation mode with transitions from phase to phase is the important characteristics of many nonlinear processes. Batch processes，being an important kind of the multiphase mode process, play an important role in today's industrial manufacturing.The processes exhibit a number of characteristics that lead to interesting control problems. Due to the high dimensionality and complexity of batch processes and the quick product-to-market time required in contemporary industrial settings, it is difficult to create batch process models based on first-principles. Therefore, in this research, the applications of subspace methods, which require only process history data, have attracted research attention in predictive control of batch processes.First, a three-dimensional data matrix should be unfolded to a two-dimensional data matrix or split into several two-dimensional data matrices. The dynamics within each batch can be captured by subspace methods. Based on the dimensionality reduction of batch processes, the complexity of controller design can be relieved to some extent. Then, according to the important multiphase mode characteristic, the proposed research will present a new nonlinear system adaptive model subspace forgetting algorithm. To ensure both quality consistency of the manufactured products and safe

英文关键词： Multiphase mode process；Data driven；Ocean turbulence；Multi scale；Hierarchical structure