Adaptive Fuzzy Backstepping Control for Robot Joint Based on Modified LuGre Friction Model

In order to achieve precise and stable control of the robot joint under the influence of nonlinear friction and unknown external disturbance moment, the LuGre friction model is modified to describe the nonlinear friction characteristics of the system, and an adaptive algorithm is used to compensate the friction to approximate the change of friction. The fuzzy neural network is used to approximate the influence of unknown external disturbance moment on the system. In this paper, the tangent barrier Lyapunov function is introduced to constrain the output signal, so that the error is limited within a given range. A hyperbolic sine function tracking differentiator is used to solve the differential explosioncaused by virtual input differentiation and the poor accuracy of the first-order filter. A fuzzy adaptive backstepping control method with friction compensation is proposed by combining the adaptive control method with the backstepping control theory. Lyapunov criterion is used to prove that all the errors of the closed-loop system are uniformly bounded. Simulation results show that，compared with the traditional PID control and RBFDSC, the position tracking error of the proposed control method is improved by nearly 7.5% and 3%, respectively. Moreover, when the parameters of the LuGre model are changed, the adaptive algorithm can accurately track and compensate for the friction force, thus verifying the effectiveness and robustness of the proposed control strategy. © 2022 Hunan University. All rights reserved.