Modeling and inverse controller design for an unmanned aerial vehicle based on the self-organizing map

被引:36
|
作者
Cho, J [1 ]
Principe, JC
Erdogmus, D
Motter, MA
机构
[1] Univ Florida, Computat NeuroEngn Lab, Gainesville, FL 32611 USA
[2] Oregon Hlth & Sci Univ, Dept Comp Sci & Elect Engn, Beaverton, OR 97006 USA
[3] NASA, Langley Res Ctr, Elect Syst Branch, Hampton, VA 23681 USA
来源
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2006年 / 17卷 / 02期
基金
美国国家航空航天局;
关键词
inverse controller; local linear model; multiple models; self-organizing map (SOM);
D O I
10.1109/TNN.2005.863422
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The next generation of aircraft will have dynamics that vary considerably over the operating regime. A single controller will have difficulty to meet the design specifications. In this paper, a self-organizing map (SOM)-based local linear modeling scheme of an unmanned aerial vehicle (UAV) is developed to design a set of inverse controllers. The SOM selects the operating regime depending only on the embedded output space information and avoids normalization of the input data. Each local linear model is associated with a linear controller, which is easy to design. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. The proposed multiple modeling and control strategy has been successfully tested in a simulator that models the LoFLYTE UAV.
引用
收藏
页码:445 / 460
页数:16
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