基于分层思路的动态非线性气动力建模方法

很多非线性气动力模型难以精确预测系统的小扰动线性特征。针对这一局限,提出了一种非线性分层模型,用于辨识跨声速非线性非定常气动力。分层建模需要同时提供微幅振荡和大幅振荡两套训练样本,首先通过线性模型(如带外输入的自回归(ARX)模型)对微幅振荡样本进行建模,而后采用非线性模型(如径向基函数神经网络(RBFNN))对大幅振荡的样本与线性模型的差量进行建模,进而把线性模型和非线性模型的输出相叠加,得到分层非线性动力学模型。算例表明建立的分层气动力模型与单一自回归径向基函数(AR-RBF)神经网络模型相比不仅具有更高的数值精度,可以精确预测大幅运动中的非线性特征,而且在小扰动状态下自动退化为线性模型,能够精确刻画结构微幅振荡下的线性动力学特性。

Dynamic nonlinear aerodynamics modeling method based on layered model

It is found that many nonlinear aerodynamic models cannot accurately predict linear characteristics under small disturbances. Based on the above limitation, a nonlinear layered model for identifying transonic nonlinear unsteady aerodynamic forces is presented. Layered modeling process needs training samples of both small and large amplitude oscillations. Firstly, the linear model (autoregressive with exogenous input, ARX) is constructed with small amplitude maneuver and the nonlinear model (radial basis function neural network, RBFNN) is constructed with a deviation of a large amplitude maneuver and linear model samples. Then the superposition is done with the outputs of both linear and nonlinear model. Finally the layered, nonlinear dynamic model is obtained. Results show that the layered aerodynamic model has higher numerical accuracy than the autoregressive RBF (AR-RBF) neural network model. The layered model has the ability of predicting large amplitude maneuvers. For small disturbance, layered model is transformed into linear model automatically and can precisely describe the linear dynamic characteristics of small amplitude oscillation.