@inproceedings{c9170de378d1466eb74d1634cd40838a,
title = "Data-driven online adaptive optimal control for linear systems with completely unknown dynamics",
abstract = "This paper develops a novel method to address the optimal control problem of systems with unknown dynamics. An adaptive identifier is first constructed based the the vectorization operator and Kronecker products, where we can reconstruct the unknown system dynamics based on the measurable input and output data. A recently proposed adaptive law is used to guarantee the convergence of the identifier parameters. Then, a data-driven technology is applied to online solve the derived algebraic Riccati equation (ARE). For this purpose, we apply the Kronecker's products on the ARE such that another adaptive law is employed to online estimate the parameters involved in the ARE with guaranteed convergence. Simulation results are given to illustrate the effectiveness of the proposed method.",
keywords = "Adaptive control, Data-driven control, Optimal control, System identification",
author = "Jun Zhao and Jing Na and Guanbin Gao and Yuyao Xiao and Zhuoyue Song",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019 ; Conference date: 24-05-2019 Through 27-05-2019",
year = "2019",
month = may,
doi = "10.1109/DDCLS.2019.8908902",
language = "English",
series = "Proceedings of 2019 IEEE 8th Data Driven Control and Learning Systems Conference, DDCLS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "557--562",
booktitle = "Proceedings of 2019 IEEE 8th Data Driven Control and Learning Systems Conference, DDCLS 2019",
address = "United States",
}