Helicopter rotor tuning based on neural network and particle swarm optimization

Considering the drawbacks of traditional rotor adjustment method without calculating possible nonlinear between rotor adjustments and fuselage vibration signals of the helicopter, a new rotor adjustment method based on the general regression neural network (GRNN) and the particle swarm optimization (PSO) was presented. GRNN network was employed to model the relationship of the rotor adjustment parameters and the fuselage vibrations, whose input parameters are rotor adjustment parameters and whose outputs are acceleration measurements along the three axes of rotor shaft and the fuselage. With the helicopter vibration as an objective function, the PSO was used to make a global optimization to find the suitable rotor adjustments corresponding to the minimum vibrations. Flight test results indicate that the neural networks are easily updated if new data becomes available thus allowing the system to evolve and mature in the course of its use.