批量生产过程的抗扰辨识与带约束鲁棒迭代学习控制设计

项目名称： 批量生产过程的抗扰辨识与带约束鲁棒迭代学习控制设计

项目编号： No.61473054

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： 刘涛

作者单位： 大连理工大学

项目金额： 84万元

中文摘要： 针对化工批量生产过程存在连续性或周期性负载干扰的情况，研究抗扰辨识状态空间模型的方法，从而克服现有的模型辨识方法不适用于这样工况的缺点。对于存在非随机性负载干扰的情况，研究能够一致收敛估计对象状态空间或传递函数模型参数的在线递推辨识算法，并且探讨分段线性化辨识建模非线性生产过程的方法。对于带有输入和输出约束的批量生产过程，基于辨识得到的对象模型描述，研究能满足这些约束条件的迭代学习控制方法，以实现完全跟踪期望输出轨迹或最小误差跟踪性能，并且针对含有时滞响应以及时变性负载干扰的情况，研究具有最小保守性的鲁棒迭代学习控制方法。所得出的研究方法针对夹套式结晶反应釜和苯酚分馏装置等批量生产过程进行应用验证。本项目成果将给出抗扰辨识模型和带约束鲁棒迭代学习控制的一些新方法，进一步丰富现有的工业系统辨识和迭代学习控制理论，对于提高化工批量生产工艺和有关先进制造业的控制技术水平具有实际参考和应用价值。

中文关键词： 间歇过程；系统辨识；过程控制；迭代学习控制；鲁棒与预测控制

英文摘要： For chemical and industrial batch processes, state-space model identification methods will be studied to deal with the presence of continuous or periodic type load disturbances, so as to overcome the incapability of the existing model identification methods for such cases. For the presence of nonrandom type load disturbances, on-line recursive identification algorithms that can guarantee the uniform convergence on estimating the plant state-space or transfer function model parameters will be studied, and piecewise model identification methods will be explored for nonlinear processes. For batch processes subject to the plant input and output constraints, based on the identified plant model description, iterative learning control (ILC) methods that can comply with these constraints will be studied to realize perfect tracking of the desired output trajectory or the minimum-error tracking performance, and robust ILC methods of the minimum-conservativeness type will be studied for batch processes involved with time delay response and time-varying load disturbances. The developed methods will be applied for demonstration through batch processes such as jacketed crystallization reactors and phenol distillation apparatus etc. The project results will give some new methods relating to model identification against disturbance and robust ILC with constraints, which will further enrich the existing theories on industrial system identification and ILC, and have practical reference value and application merits to improve the control technology level for chemical and industrial batch production technologies and related state-of-the-art manufacturing industries.

英文关键词： Batch process;System identification;Process control;Iterative learning control;Robust and predictive control