Helicopter Rotor Balance Adjustment Using GRNN Neural Network and Genetic Algorithm

Considering the drawbacks of 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 is presented. GRNN network is employed to model the relationship of the rotor adjustments and 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 helicopter vibration as objective function, genetic algorithm (GA) 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. Moreover 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.