In this paper, a high-accuracy motion control of a torque-controlled motor servo system with nonlinear friction compensation is presented. Friction always exists in the servo system and reduces its tracking accuracy. Thus, it is necessary to compensate for the friction effect. In this paper, a novel controller that combines robust adaptive control with friction compensation based on neural network observer is proposed. An improved LuGre friction model is applied into the friction compensation as it is known as a good model to express the nonlinear friction. A single hidden-layer network is utilized to observe the immeasurable friction state. Then, the robust adaptive controller is used to handle the parametric uncertainty, the parametric estimation error, friction compensation error, and other uncertainties. Lyapunov theory is utilized to analyze the stability of the closed-loop system. The experimental results demonstrate the effectiveness of the proposed algorithm.

The permanent-magnet synchronous motor (PMSM) system, which is a nonlinear dynamic system, will exhibit a variety of chaotic or limit-cycle phenomenon under some choices in system parameters and external disturbances and its chaotic characteristics will become obvious. Based on the mathematical model of the PMSM system, the property of equilibrium points is analyzed and the relationship between Hopf bifurcation and the system parameters associated with control parameters is illustrated. In addition, bifurcation diagram, Lyapunov exponent map, and phase plane diagram are also presented in this paper. An adaptive nonlinear feedback controller, which could estimate the system parameters online, is then designed to eliminate the chaos and drive the speed of PMSM to a desired value in presence of system parametric uncertainty. Numerical simulation proves that the proposed control method has a better controlling effect than the general nonlinear feedback controller.