Ship's optimal autopilot with a multivariate auto-regressive exogenous model

被引：0

作者：

Nguyen, DH ^{[1
]}

Le, MD ^{[1
]}

Ohtsu, K ^{[1
]}

机构：

[1] Univ Tokyo Mercantile Marine, Ship Maneuvering Lab, Koto Ku, Tokyo 1358533, Japan

来源：

CONTROL APPLICATIONS OF OPTIMIZATION 2000, VOLS 1 AND 2 | 2000年

关键词：

auto-regressive models; identification algorithms; recursive least squares; ship control; LQG control and control applications;

D O I：

暂无

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 presents a new application of the linear quadratic gaussian (LQG) control algorithm linked to the recursive least squares (RLS) algorithm applied to a multivariate auto-regressive exogenous (MARX) model of ships to construct an autopilot for steering ships. Simulation performed for a training ship is described. As the first step of designing; a tracking system, the optimal autopilot with the MARX model was used to both keep and change the ship's course during full-scale experiments aboard the training ship. It has been found that the autopilot is robust and has good performance for steering ships. Copyright (C) 2000 IFAC.

引用

页码：277 / 282

页数：6

共 50 条

[31] Predicting heartbeat arrival time for failure detection over internet using auto-regressive exogenous model
Zhao, Haijun
Ma, Yan
Huang, Xiaohong
Su, Yujie
High Technology Letters, 2008, 14 (04) : 370 - 376
[32] Dual multivariate auto-regressive modeling in state space for temporal signal separation
Cheung, YM
Xu, L
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2003, 33 (03): : 386 - 398
[33] Auto-regressive Conditional Density Model for Wind Power Forecasting
Chen, Hao
Li, Fangxing
Wang, Yurong
2016 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PESGM), 2016,
[34] Research of Wind Power Prediction Based on the Auto-Regressive Model
Feng, L.
Liang, C. H.
Huang, H.
PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON POWER ELECTRONICS AND ENERGY ENGINEERING (PEEE 2015), 2015, 20 : 61 - 64
[35] Identification of Auto-Regressive Exogenous Hammerstein Models Based on Support Vector Machine Regression
Al-Dhaifllah, Mujahed
Westwick, David T.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2013, 21 (06) : 2083 - 2090
[36] ALARM: A LOGISTIC AUTO-REGRESSIVE MODEL FOR BINARY PROCESSES ON NETWORKS
Agaskar, Ameya
Lu, Yue M.
2013 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP), 2013, : 305 - 308
[37] A spatiotemporal auto-regressive moving average model for solar radiation
Glasbey, C. A.
Allcroft, D. J.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 2008, 57 : 343 - 355
[38] A non-linear auto-regressive exogenous method to forecast the photovoltaic power output
Louzazni, Mohamed
Mosalam, Heba
Khouya, Ahmed
Amechnoue, Khalid
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, 2020, 38
[39] An Auto-regressive Model for an Arterial Continuous Glucose Monitoring Sensor
Zhou, Tony
Dickson, Jennifer
Chase, J. Geoffrey
IFAC PAPERSONLINE, 2017, 50 (01): : 2064 - 2069
[40] Optimized Intelligent Auto-Regressive Neural Network Model (ARNN) for Prediction of Non-Linear Exogenous Signals
Lubna Farhi
Agha Yasir
Wireless Personal Communications, 2022, 124 : 1151 - 1167

← 1 2 3 4 5 →