A Dynamics Modeling Method of Manipulator Based on Long Short Term Memory Neural Network

Abstract

Dynamics modeling plays an important role in studying the dynamics of manipulator. However, the traditional dynamics model is described by highly nonlinear coupled ordinary differential equations, which requires a large amount of computation and is difficult to be accurately modeled. Based on this, a dynamics modeling method of manipulator based on Long Short Term Memory (LSTM) neural network is proposed in this paper. The proposed network includes two LSTM layers and one full connection layer, which is built based on TensorFlow environment. The data sets were collected through dynamic simulations. The results show that the data curves generated by the network is basically consistent with that of the test set, and the fitting degree is high within the normal working range, which indicates that the method of building the dynamics model of the manipulator by using LSTM neural network is effective.