Neural network model identification control of dual-inertia system with a flexible load considering payload mass variation and nonlinear deformation

Abstract

The dual-inertia system with a flexible load (DSFL) is a complex nonlinear model, which originates from the flexible load deformation and payload mass variation. In dynamic modeling, it is impossible to consider all the nonlinear factors, including high-order modes and inaccurate friction torque, which leads to the existence of uncertain components in the DSFL. Based on this, it is impossible to build a dynamic model of the DSFL that is completely consistent with the real physical model. This modeling errors increases the tracking errors of the DSFL. In this study, the neural networks are employed to recognize the lumped uncertain components of the DSFL dynamic model, so as to reduce the flexible load's tracking error. Firstly, based on the deformation accuracy comparison, a relatively high precision model simplification method is proposed, which neglects the second order mode. Next, the control law of the neural network identification control strategy is designed according to the stability theorem. Finally, through physical control experiments and simulation analysis, it is shown that this control strategy enhances the motion accuracy of the DSFL. The experimental results show that this control strategy predicted by the radial basis function (RBF) neural network can reduce the rotation angle error by 15.86%.