@inproceedings{bd372217d7e8439aace9e592e5aa4046,
title = "Data-based Control Design for Learning Systems",
abstract = "This paper aims at presenting a data-based control design method for iterative learning control (ILC) systems such that the perfect tracking objective can be achieved without any model information. By only utilizing the input and output data collected in the test iterations, the trackability property of the given desired reference can be validated, which guarantees the existence of the desired input generating the desired reference for any ILC system with linear dynamics. Moreover, the idea of the observer design is leveraged to develop an ILC updating law only based on the collected input and output data. Thanks to the data-based ILC updating law, the perfect tracking objective is realized for ILC systems subject to any trackable desired reference despite the generally required full rank condition, where any knowledge of the model information is never needed.",
keywords = "Data-based control design, iterative learning control, perfect tracking, trackability",
author = "Yuxin Wu and Deyuan Meng",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022 ; Conference date: 03-08-2022 Through 05-08-2022",
year = "2022",
doi = "10.1109/DDCLS55054.2022.9858580",
language = "英语",
series = "Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1212--1217",
editor = "Mingxuan Sun and Zengqiang Chen",
booktitle = "Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022",
address = "美国",
}