Saturated kinetic control of autonomous surface vehicles based on neural networks

Zhouhua Peng, Jun Wang, Dan Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2017 - 14th International Symposium, ISNN 2017, Proceedings
EditorsFengyu Cong, Qinglai Wei, Andrew Leung
PublisherSpringer Verlag
Pages93-100
Number of pages8
ISBN (Print)9783319590806
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event14th International Symposium on Neural Networks, ISNN 2017 - Sapporo, Hakodate, and Muroran, Hokkaido, Japan
Duration: 21 Jun 201726 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10262 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Symposium on Neural Networks, ISNN 2017
Country/TerritoryJapan
CitySapporo, Hakodate, and Muroran, Hokkaido
Period21/06/1726/06/17

Keywords

  • Autonomous surface vehicles
  • Neural networks
  • Saturated control
  • Unknown kinetics

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