Abstract: To address disturbance effects in the trajectory tracking control of the SCARA robot, a dynamic model of the SCARA robot is established. On the basis of analyzing the influence of friction on robot joints, a high-speed friction compensation term is introduced to establish a Viscous and Coulomb friction improvement model for robot joints. A sliding-mode adaptive control strategy for SCARA robot trajectory tracking is proposed based on an RBF neural network, and the stability and convergence of the control system are analyzed by using the Lyapunov method. The tracking accuracy and stability of the control system are simulated and experimented with the Delta DRS40L3 SCARA robot as the research object. The results show that, compared with the PID algorithm, the maximum error, the mean square value of the tracking error, and the root mean square of the input moment of the SCARA robot trajectory tracking by the RBF neural network sliding mode adaptive control have been substantially reduced. The proposed method enables high-precision trajectory tracking for SCARA robots.

Key words: SCARA robot, trajectory tracking, RBF neural network, sliding mode, adaptive control