@inproceedings{d87b53f8732f4f12bdf85e0610c8edef,
title = "Model Predictive Tracking Control for USV with Model Error Learning",
abstract = "This paper is concerned with the learning-based model predictive control (MPC) for the trajectory tracking of unmanned surface vehicle (USV). The accuracy of system model has a significant influence on the control performance of MPC. However, the complex hydrodynamics and the complicated structure of USV make it difficult to capture the accurate system model. Therefore, we present a learning approach to model the residual dynamics of USV by using Gaussian process regression. The learned model is employed to compensate the nominal model for MPC. Simulation studies are carried out to verify the effectiveness of the proposed method.",
keywords = "Gaussian process regression, Model predictive control, Trajectory tracking, Unmanned surface vehicles",
author = "Siyu Chen and Huiping Li and Fei Li",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 2nd CAAI International Conference on Artificial Intelligence, CICAI 2022 ; Conference date: 27-08-2022 Through 28-08-2022",
year = "2022",
doi = "10.1007/978-3-031-20503-3_36",
language = "英语",
isbn = "9783031205026",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "451--461",
editor = "Lu Fang and Daniel Povey and Guangtao Zhai and Tao Mei and Ruiping Wang",
booktitle = "Artificial Intelligence - Second CAAI International Conference, CICAI 2022, Revised Selected Papers",
}