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

被引：232

作者：

Park, Bong Seok ^{[1
]}

Kwon, Ji-Wook ^{[2
]}

Kim, Hongkeun ^{[3
]}

机构：

[1] Kongju Natl Univ, Div Elect Elect & Control Engn, Cheonan 31080, South Korea

[2] Yujin Robot Co Ltd, Seoul 08589, South Korea

[3] Korea Univ Technol & Educ, Sch Mechatron Engn, Cheonan 31253, South Korea

来源：

AUTOMATICA | 2017年 / 77卷

基金：

新加坡国家研究基金会;

关键词：

Underactuated surface vessel; Adaptive observer; Neural network; Input saturation; GLOBAL TRACKING; STABILIZATION; SHIP; SYSTEMS;

D O I：

10.1016/j.automatica.2016.11.024

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：353 / 359

页数：7

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