Adaptive Sliding Mode Fault-tolerant Control for Hypersonic Aircraft Using RBF Neural Networks

被引：0
|
作者：
Zhai, Rongyu [1 ]
Qi, Ruiyun [1 ]
Jiang, Bin [1 ]
机构：
[1] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 211106, Jiangsu, Peoples R China
来源：
PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC) | 2016年
关键词：
Adaptive; Sliding mode; Fault-tolerant; Radial basis function neural network (RBFNN); ACTUATOR FAULTS; SPACE-VEHICLE;
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：
In this paper, an adaptive sliding mode fault-tolerant control (FTC) scheme is proposed to solve the robust and fast attitude tracking problems for a hypersonic aircraft. Firstly, the dynamic model of the aircraft with external disturbances and two kinds of faults is given. Then, a radial basis function neural network (RBFNN) is designed to estimate the unknown additive fault, and the unknown partial loss of effectiveness fault is handled by the adaptive control law. Combined with the sliding mode control theory, the fault-tolerant controllers are designed for the outer and inner loops of the faulty system, respectively. Finally, simulation results verify the effectiveness of the proposed control scheme.
引用
收藏
页码：1879 / 1884
页数：6
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