基于神经网络模型的襟翼主动控制旋翼减振分析

摘要

Based on the radial basis function (RBF) neural network,a mathematical model of vibration load for active controlled flap (ACF) is established.In the model,the trailing-edge-flap angle is as input and the rotor hub oscillating loads are as output.An orthogonal experimental method is used for the selection of the training set,then the training samples are calculated in CAMRAD Ⅱ software and the RBF network is trained off-line by the orthogonal samples.With the aid of RBF network,a multicyclic controller is given for the active control of rotor vibration loads.In order to verify the effectiveness of the network and the controller,the vibration loads of a two-blade ACF rotor with diameter 4 m are analyzed.Numerical results indicate that the vertical 2Ω load is reduced to almost 50 ％,and the 2Ω load in the other directions shows different degrees of reduction.%基于径向基函数(Radial basis function,RBF)神经网络构建了一种带后缘襟翼主动控制(Active controlled flap,ACF)的旋翼振动载荷计算模型.采用正交试验方法确立RBF网络训练样本的输入,在CAMRAD Ⅱ中计算前飞状态下与训练样本对应的旋翼桨毂六力素,并将主通过频率下的分量作为样本输出,对RBF网络进行离线训练.在此基础上采用多周控制器对被控模型进行振动载荷主动控制.随后以2桨叶4 m直径ACF旋翼为例,构建了其桨毂减振分析方法,并对桨毂动载荷各分量的减振效果进行了分析.研究表明,采用正交样本训练的RBF网络能够精确映射襟翼偏角与桨毂振动载荷的非线性关系,施加多周控制后,桨毂垂向振动载荷降低接近50％,其他方向的振动载荷也有不同程度的降低.