Neural network-based output feedback control for reference tracking of underactuated surface vessels

This paper proposes an adaptive output feedback control for trajectory tracking of underactuated surface vessels (USVs). For the realistic dynamical model of USVs, we consider the USV model, where the mass and damping matrices are not diagonal. Moreover, except the mass matrix, the system parameters...

Full description

Saved in:
Bibliographic Details
Published in:Automatica (Oxford) 2017-03, Vol.77, p.353-359
Main Authors: Park, Bong Seok, Kwon, Ji-Wook, Kim, Hongkeun
Format: Article
Language:English
Subjects:
Adaptive observer
Input saturation
Neural network
Underactuated surface vessel
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes an adaptive output feedback control for trajectory tracking of underactuated surface vessels (USVs). For the realistic dynamical model of USVs, we consider the USV model, where the mass and damping matrices are not diagonal. Moreover, except the mass matrix, the system parameters and nonlinearities of the USV are all assumed to be unknown. Despite this uncertain circumstance, we develop an adaptive observer based on the neural networks to estimate the velocity data of USVs. Then, an output feedback control law is designed by simultaneously considering the input saturation and underactuated problems.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2016.11.024