Optimal adjustment method of rotor multi-dimensional vibration

Considering characteristics of non-linear and difficult to model for the rotor system, helicopter rotor dynamic balancing adjustment models was established by radial basis function(RBF) neural network. According to the constraints, the fitness function was established by using the helicopter vibration as objective function and the optimization variables were used by the adjustment parameters of rotor. The radial basis function neural network learning and optimization were used by the helicopter vibration and the adjustment parameters of rotor. Particle swarm optimization(PSO) algorithm was used to make a global optimization to find the suitable rotor adjustments corresponding to the minimum vibrations. The experimental results indicate that the particle swarm optimization algorithm is higher than the genetic algorithm in the aspect of efficiency optimization and the radial basis function combined with the particle swarm optimization algorithm can effectively achieve the helicopter rotor dynamic balance adjustment.