@inproceedings{be06153e1cad470484d1e9db7c3951ee,
title = "Cooperative optimal output regulation of multi-agent systems using adaptive dynamic programming",
abstract = "This paper proposes a novel solution to the adaptive optimal output regulation problem of continuous-time linear multi-agent systems. A key strategy is to resort to reinforcement learning and approximate/adaptive dynamic programming. A data-driven, non-model-based algorithm is given to design a distributed adaptive suboptimal output regulator in the presence of unknown system dynamics. The effectiveness of the proposed computational control algorithm is demonstrated via cooperative adaptive cruise control of connected and autonomous vehicles.",
author = "Weinan Gao and Jiang, {Zhong Ping} and Lewis, {Frank L.} and Yebin Wang",
note = "Publisher Copyright: {\textcopyright} 2017 American Automatic Control Council (AACC).; 2017 American Control Conference, ACC 2017 ; Conference date: 24-05-2017 Through 26-05-2017",
year = "2017",
month = jun,
day = "29",
doi = "10.23919/ACC.2017.7963356",
language = "English (US)",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2674--2679",
booktitle = "2017 American Control Conference, ACC 2017",
}