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202401017.pdf
于瀛祯, 林娜, 池荣虎(青岛科技大学 自动化与电子工程学院, 山东 青岛 266061)
文章编号： 1672-6987（2024）01-0121-08； DOI： 10.16351/j.1672-6987.2024.01.017
摘要： 针对PID型迭代学习控制（iterative learning control, ILC）方法，提出了两种数据驱动自适应整定(data-driven adaptive tuning, DDAT)方法。首先采用紧格式迭代动态线性化(compact form iterative dynamic linearization,CFIDL)方法将原始的非线性系统转化为等价的线性数据模型，设计了一个目标函数来动态地调整PID型ILC的学习增益。其次，通过对设计的目标函数进行优化，提出了一种基于CFIDL的DDAT方法。该方法只使用实际的I/O数据，而不需要任何机理模型信息。进一步，引入偏格式迭代动态线性化(partial form iterative dynamic linearization, PFIDL)方法对结果进行扩展，提出了一种基于PFIDL的DDAT方法。所提出的两种DDAT方法都可以提高PID型ILC对不确定性的鲁棒性。最后，通过仿真验证了两种方法的有效性。
关键词： 数据驱动方法; 参数的自适应整定; 迭代学习控制； 优化
中图分类号： TP 13文献标志码： A
引用格式： 于瀛祯, 林娜, 池荣虎. 迭代学习控制器参数的数据驱动自适应整定方法［J］. 青岛科技大学学报(自然科学版), 2024, 45(1): 121-128.
YU Yingzhen, LIN Na, CHI Ronghu. Data-driven adaptive tuning of iterative learning control［J］. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2024， 45（1）： 121-128.
Data-Driven Adaptive Tuning of Iterative Learning Control
YU Yingzhen, LIN Na, CHI Ronghu
(College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China)
Abstract: In this paper, we propose two data-driven adaptive tuning (DDAT) approaches of PID-type ILC. First, we use a compact form iterative dynamic linearization (CFIDL) method to transfer the original nonlinear system into a equivalent linear data model, and we design an objective function to dynamically tune the learning gains of ILC law. Then, by optimizing the designed objective function, a CFIDL based DDAT method is proposed. This DDAT method only uses the real I/O data and doesn′t need to know any mathematical model information. On this basis, we introduce a partial form iterative dynamic linearization (PFIDL) method to extend the research results, and propose a PFIDL based DDAT method. Both the proposed DDAT methods can help the PID-type ILC have a better robustness against to the uncertainties. Finally, the effectiveness of the two proposed DDAT-based ILC methods is verified by the simulations.
Key words: data-driven methods; adaptive tuning of learning gains; iterative learning control; optimizing
收稿日期： 2022-12-15
基金项目： 国家自然科学基金项目（61833001,61873139）.
作者简介： 于瀛祯(1975-),男,博士,“泰山学者”特聘教授，博士生导师.
