@inproceedings{e8cc967fca1b4acd89efa2e4a895e5f4,
title = "Feedforward-Control-Oriented Identification: A Robust Iterative Learning Approach",
abstract = "Feedforward controller plays an important role in the achievement of high servo performance in the field of industrial electromechanical systems. Although existing instrumental-variables-based iterative feedforward tuning (IFT) methods show a promising prospect on improving estimation accuracy, the robust approaches have not developed. In this paper, a robust IFT approach is proposed through a combination of instrumental-variables-based system identification and learning control. It transforms the feedforward controller design to a system identification problem, and then to a typical H2 state feedback problem. A simulation example is provided to confirm its superiority.",
keywords = "feedforward control, iterative learning control, motion control, system identification",
author = "Fazhi Song and Yang Liu and Qiao Zhu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 China Automation Congress, CAC 2021 ; Conference date: 22-10-2021 Through 24-10-2021",
year = "2021",
doi = "10.1109/CAC53003.2021.9728608",
language = "英语",
series = "Proceeding - 2021 China Automation Congress, CAC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4122--4127",
booktitle = "Proceeding - 2021 China Automation Congress, CAC 2021",
address = "美国",
}