@inproceedings{43410cf24d8049148235c1b68da59be5,
title = "Data-driven optimal preview output tracking of linear discrete-time systems",
abstract = "This paper is focused on an approach of designing an optimal preview output tracking controller for discrete-time systems with measurable data. It is assumed that the reference trajectory is previewable, i.e., some finite future as well as present and past values of the reference signal are known in advance. An augmented state-space system including the prior information in a preview horizon is firstly constructed. A linear quadratic regulator (LQR) representation is then developed by taking some manipulation on the original output tracking problem. Next, a Q-function based value Iteration (VI) algorithm is presented to obtain the optimal output tracking control gain using online measurable data. It's shown that the presented approach does not require a priori information of system dynamics and an initially stabilizing control is also no longer needed. Finally, a numerical simulation is carried, which shows the merits of the proposed scheme.",
keywords = "ARE, Output tracking control, Preview control, Reinforcement learning",
author = "Liu, \{Zhou Yang\} and Wu, \{Huai Ning\}",
note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",
year = "2019",
month = jul,
doi = "10.23919/ChiCC.2019.8865154",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "1973--1978",
editor = "Minyue Fu and Jian Sun",
booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",
address = "美国",
}