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.