@inproceedings{f49739f8e28a4c698e2de7d686b255fe,
title = "Learning-based Event-triggered Adaptive Optimal Output Regulation of Linear Discrete-time Systems",
abstract = "In this paper, a data-driven event-triggered output-feedback control approach is proposed to solve the problem of adaptive optimal output regulation for uncertain discrete-time linear systems when only the output information is available. A crucial strategy is to develop a novel co-design scheme for the event-triggering mechanism and the data-driven optimal controller. Theoretical analysis and an application to a LCL coupled inverter-based distributed generation system demonstrate the effectiveness of the proposed learning-based, event-triggered, adaptive optimal controller design with output-feedback.",
keywords = "Adaptive Dynamic Programming, Event-Triggered Control, Output Regulation",
author = "Fuyu Zhao and Weinan Gao and Tengfei Liu and Jiang, {Zhong Ping}",
note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grants 61633007 and U1911401, and in part by the U.S. National Science Foundation under Grant EPCN-1903781. Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021 ; Conference date: 14-05-2021 Through 16-05-2021",
year = "2021",
month = may,
day = "14",
doi = "10.1109/DDCLS52934.2021.9455453",
language = "English (US)",
series = "Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1516--1521",
editor = "Mingxuan Sun and Huaguang Zhang",
booktitle = "Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021",
}