基于GRNN网络和遗传算法的旋翼动平衡调整

针对传统旋翼调整方法没有考虑调整参数与振动信号之间的非线性关系,提出一种结合广义回归神经网络GRNN(General Regression Neural Network)和遗传算法的旋翼调整方法,采用GRNN建立旋翼动平衡调整模型,以桨叶调整参数作为GRNN输入,以旋翼转轴3个方向的加速度测量值和机身3个方向加速度测量值作为网络输出,建立调整参数与直升机振动信号之间的模型.以直升机振动作为目标函数,采用改进的遗传算法对桨叶调整参数进行寻优,获得直升机振动最小时的桨叶的调整量.飞行实验表明,通过1到2次飞行调整,可使3个方向机身振动(旋翼的一阶振动)为最小,完成旋翼的动平衡调整. 

Helicopter rotor smoothing based on GRNN neural network and genetic algorithm

Considering traditional adjustment method without calculating possible nonlinear between rotor adjustments and fuselage vibration signals,a new rotor adjustment method based on general regression neural network(GRNN) and genetic algorithm was presented.GRNN network was employed to model the relationship between the rotor adjustments and the fuselage vibrations,whose inputs are rotor adjustment parameters and whose outputs are acceleration measurements along the three axes of rotor shaft and the fuselage.With helicopter vibration as objective function,genetic algorithm was used to make a global optimization to find the suitable rotor adjustments corresponding to the minimum vibrations.Flight test results indicate that proposed rotor adjustment method can minimize fuselage vibration at fundamental rotor frequency along the three axes,only in one or two adjustment flights,and that the neural networks may be updated to include new data thus allowing the system to evolve and mature in the course of its use.