Cho Jeongho, Principe Jose C, Erdogmus Deniz, Motter Mark A
Computational NeuroEngineering Laboratory, University of Florida, Gainesville, FL 32611, USA.
IEEE Trans Neural Netw. 2006 Mar;17(2):445-60. doi: 10.1109/TNN.2005.863422.
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.
下一代飞机在运行过程中的动力学特性会有很大差异。单一控制器将难以满足设计规格。本文提出了一种基于自组织映射(SOM)的无人机(UAV)局部线性建模方案,以设计一组逆控制器。SOM仅根据嵌入的输出空间信息选择运行状态,避免了输入数据的归一化。每个局部线性模型都与一个易于设计的线性控制器相关联。控制器的切换与跟踪不同运行条件的活跃局部线性模型同步进行。所提出的多重建模和控制策略已在模拟LoFLYTE无人机的模拟器中成功测试。