Research of Hybrid Hysteresis Modeling of Harmonic Reducer based on SDH Model and Neural Network in Series - Xuanju Dang; Kaili Wang; Hui Jiang; Xiru Wu; Xiangwen Zhang; Shijie Tang (Jixie chuandong, 2019)
A hybrid hysteresis model consisting of SDH model and neural network in series is constructed for the special nonlinear hysteresis characteristics of the harmonic reducer in the flexible link. The SDH model is used as the pre-model of which hysteresis characteristics between the input and output signals is similar to the hysteresis characteristics of the harmonic reducer. The dynamic RBF neural network which can describe the nonlinearity is used as the post-model to make the hybrid hysteresis model to describe the hysteresis nonlinearity characteristic of the harmonic reducer. The data obtained under different frequency input signals and different load are modeled. Compared with the classical RBF neural network model and SDH model, the experimental results show that the constructed hybrid hysteresis model has high precision and strong adaptability.