Neural network control for air-to-air missiles with thrust vectoring

被引：0

作者：

Dong, C. ^{[1
]}

Jing, S. ^{[1
]}

Wang, Q. ^{[1
]}

Zhang, M. ^{[1
]}

机构：

[1] Department of Automatic Control, College of Automation, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

来源：

Xitong Fangzhen Xuebao / Journal of System Simulation | 2001年 / 13卷 / 05期

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摘要：

The advanced air-to-air missiles possess the characteristics of maneuverability, agility and accurate guidance performance by adopting thruster vector control. Because the neural network control has strong self-learning ability and adaptability to system nonlinear variations, it has significant advantages in the control of air-to-air missiles with thruster vectoring. After modeling of the air-to-air missiles with thruster vectoring, two neural network control methods for the air-to-air missiles are presented. One of them with two networks, dynamic inversion learning structure, is given to design an autopilot for the missile. In order to improve the learning ability of the presented neural network control system, fuzzy rules based on expert learning experience are introduced. Numerical simulation results are given to illustrate that the presented neural network control method possess strong adaptability to system nonlinear variations.

引用

页码：585 / 587