Research on Adaptive Back-stepping Control of Harmonic Drive Based on the RBF Neural Network

Due to its own structural characteristics, a harmonic drive system has a wide range of nonlinear factors, such as flexible deformation, friction and external uncertain interference. Most of the traditional controllers simplify the system to a certain extent, or do not consider the nonlinear external disturbance, resulting in that the performance of the designed controller cannot achieve the desired results. In order to improve the accuracy of the system, the dynamic model of the harmonic drive system is established considering the nonlinear stiffness and nonlinear friction of the system. Based on the test data, the parameters of the model are identified by the least square method. Radial basis function （RBF） neural network is used to approximate the nonlinear friction and external uncertain disturbance torque of the system on-line, and an adaptive inversion controller based on RBF neural network is proposed. Using Lyapunov stability theory, the convergence of the closed-loop system is proved. The simulation results show that, compared with the ordinary Back-stepping control, the proposed RBF neural network adaptive inversion control can effectively approach the system nonlinear friction and external unknown disturbance after being subjected to external unknown disturbance, and its peak value of tracking error can be quickly stabilized to 0.000 82 rad. The Back-stepping control is sensitive to external unknown interference, and the peak value of its tracking error increases to about 0.012 3 rad. The proposed RBF neural network adaptive inversion control can suppress the influence of parameter dynamic changes and external disturbances on the transmission accuracy of the system, and improve the transmission accuracy of the system.