System identification of biped robot based on dynamic fuzzy neural network and improved RBF neural network

Abstract

Aiming at the identification problem in continuous and discrete hybrid dynamical system of biped robots, we present a joint identification method based on dynamic fuzzy neural network (DFNN) and improved radial basis function neural network (RBFNN) with expectation-maximization clustering algorithm. First we apply the improved RBFNN to identify the swing stage in biped robot walking, and then use the DFNN to identify collision stage in biped motion, further more, achieve the whole identification of the biped robot's motion trajectory. The simulation results show that the method has fast learning speed, high identification precision, can effectively identify the biped robot system, thus it's applicable to hybrid dynamic systems with continuous and discrete properties.