D. Singh, N. Verma, A. Ghosh, Appasaheb Malagaudanvar
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引用次数: 2
摘要
本文介绍了模糊逻辑理论在固定翼飞机气动建模中的应用。为了演示,从记录的非线性飞行廓线飞行数据中提取ATAS飞机的滚转力矩控制和稳定性导数。采用基于模糊逻辑的Takagi Sugeno Kang (TSK)模型来表示飞机的非线性动力学,将其近似为若干局部线性模型。在现代飞机由于交叉耦合和非线性复杂性而难以确定平衡点(纵倾点)的情况下,该方法显示出其优点。输入空间采用基于网格的Rusipini型模糊划分,每个训练数据集的输入生成两个三角型隶属函数。基于加权最小二乘算法提取模糊规则,每个规则对应一个局部线性模型,用于模拟气动(控制和稳定)导数。采用五重交叉验证法对生成的模糊模型进行充分性检验。训练和测试数据集的参数跟踪趋势和均方误差表明,TSK模糊模型在提取气动导数方面具有良好的建模能力。
Fuzzy systems practices for aerodynamic parameter modeling of the aircraft
This paper presents an application of fuzzy logic theory for aerodynamic modeling of the fixed wing aircraft. For the demonstration purpose, rolling moment control and stability derivatives of ATAS aircraft are extracted from the recorded flight data of nonlinear flight profile. Fuzzy logic based Takagi Sugeno Kang (TSK) model is used to represent the nonlinear dynamics of the aircraft, which can approximate it into several locally linear models. The method shows its benefit in case when it becomes very difficult to identify the equilibrium points (trim points) due to cross coupling and nonlinear complexities associated with modern aircraft. The grid based Rusipini type fuzzy partitions are used for input spaces and two triangular type membership functions are generated per input from the training data set. The fuzzy rules are extracted based on weighted least square algorithms and each rule corresponds to local linear model for mimicking of aerodynamic (control and stability) derivatives. Fivefold cross validation method is used to access the adequacy of the generated fuzzy model. The parameter tracking trends and mean square error for training and testing data sets show commendable modeling capability of TSK fuzzy model for extraction of aerodynamic derivatives.