Real-time neural-network midcourse guidance

被引：27

作者：

Song, EJ ^{[1
]}

Tahk, MJ ^{[1
]}

机构：

[1] Korea Adv Inst Sci & Technol, Div Aerosp Engn, Dept Mech Engn, Taejon 305701, South Korea

来源：

CONTROL ENGINEERING PRACTICE | 2001年 / 9卷 / 10期

关键词：

midcourse guidance; suboptimal guidance; neural networks; feedback form; optimal trajectory;

D O I：

10.1016/S0967-0661(01)00058-2

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The approximation capability of artificial neural networks has been applied to the midcourse guidance problem to overcome the difficulty of deriving an on-board guidance algorithm based on optimal control theory. This approach is to train a neural network to approximate the optimal guidance law in feedback form using the optimal trajectories computed in advance, Then the trained network is suitable for real-Lime implementation as well as generating suboptimal commands. In this paper, the advancement of the neural-network approach to the current level from the design procedure to the three-dimensional flight is described. (C) 2001 Published by Elsevier Science Ltd.

引用

页码：1145 / 1154

页数：10

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