@inproceedings{2e97853874fc4aafbedfce0063e02127,
title = "Saturated kinetic control of autonomous surface vehicles based on neural networks",
abstract = "This paper investigates the saturated kinetic control of autonomous surface vehicles subject to unknown kinetics and limited control torques. The unknown kinetics stems from parametric model uncertainty, unmodelled hydrodynamics, and environmental forces due to wind, waves and ocean currents. By approximating the unknown kinetics using neural networks, a bounded kinetic control law is proposed based on a saturated function, with the main advantage being that the control input is known as a priori. The resulting closed-loop kinetic control system is proved to be input-to-state stable.",
keywords = "Autonomous surface vehicles, Neural networks, Saturated control, Unknown kinetics",
author = "Zhouhua Peng and Jun Wang and Dan Wang",
year = "2017",
month = may,
doi = "10.1007/978-3-319-59081-3\_12",
language = "English",
isbn = "9783319590806",
volume = "10262 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "93--100",
editor = "Fengyu Cong and Qinglai Wei and Andrew Leung",
booktitle = "Advances in Neural Networks - ISNN 2017",
address = "Germany",
note = "14th International Symposium on Neural Networks (ISNN 2017), ISNN 2017 ; Conference date: 21-06-2017 Through 26-06-2017",
}