Identification model of flexible mechanism dynamic response based on RBF neural network

The aim of the research is to setup kinematical parameters identification model of flexible mechanism. Taking advantage of radial basis function (RBF) artificial neural network, the model is realized to identify complicated nonlinear dynamic response of flexible mechanism. Driving moments, damps and nonlinear dynamical parameters are considered as the inputs and outputs (samples) of RBF that constructed to be trained. A flexible expand mechanism of space station is employed to test this identification method. The simulation results indicate that it is a better approach with very well convergence properties and higher fidelity. This method provides an available way of model identification for complex large system.