Minimum time ship maneuvering method using neural network and nonlinear model predictive compensator

被引：35
|
作者：
Mizuno, Naoki
Kuroda, Masaki
Okazaki, Tadatsugi
Ohtsu, Kohei
机构：
[1] Nagoya Inst Technol, Shouwa Ku, Nagoya, Aichi 4668555, Japan
[2] Natl Maritime Res Inst, Mitaka, Tokyo, Japan
[3] Tokyo Univ Marine Sci & Technol, Koto Ku, Tokyo, Japan
来源：
CONTROL ENGINEERING PRACTICE | 2007年 / 15卷 / 06期
关键词：
ship control; minimum time control; neural network; nonlinear model predictive control; receding horizon control;
D O I：
10.1016/j.conengprac.2007.01.002
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a new minimum time ship maneuvering method using neural network (NN) and nonlinear model predictive compensator is proposed. In this proposed method the NN is used for interpolating the precomputed minimum time solution for real time situations and the nonlinear dynamical model of a ship is used for compensating the control error caused by some modeling errors, disturbances and so on. The introduction of the nonlinear model into the online control system is inspired by the idea that since the nonlinear dynamical model of a ship has been constructed for the off-line numerical computation of the optimal solutions, it could also be used to enhance online control performance. In order to investigate this method, simulation studies and actual sea tests were carried out using a training ship Shioji Maru. The results showed that the system gives approximate solutions in a short computing time and good tracking performance in the actual sea trials. (c) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码：757 / 765
页数：9
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