LuGre Friction Compensation Control of Servo Manipulator Based on Neural Network

To overcome the parameter identification difficulties of the LuGre friction model, and it is not easy to establish an accurate mathematical model, RBF neural network was used to approximate the LuGre friction model, and was combined with the computed torque controller. The stability of the system and the convergence of the tracking error of the closed-loop system were proved by the Lyapunov method. The simulation results show that the control algorithm can compensate the friction effectively and improve the tracking control performance.