Robust motion control based on model-based unknown system dynamics estimator for robot manipulators

摘要

Purpose Achieving accurate trajectory tracking control of robot manipulators is challenging due to dynamic model errors and uncertain payloads. This paper aims to enhance trajectory tracking performance for robots with n degrees of freedom (DOF). Design/methodology/approach This study proposes a robust motion control framework that combines uncertainty and disturbance estimator with model-based compensation. The proposed framework ensures precise trajectory tracking in robot manipulators. In addition, uncertainties in the high-DOF robot dynamics are estimated through a simple model-based compensation for system error dynamics. The stability of the closed-loop system of the proposed framework is analyzed and proved. Findings The results indicate that the proposed framework can significantly reduce tracking errors and increase disturbance resistance. The simulation results of a two-link robotic arm verify the effectiveness of the proposed method. The results of the experiments conducted on a seven-DOF torque-controlled Flexiv4S manipulator demonstrate the superior trajectory tracking performance and robustness of the proposed algorithm. Originality/value This study introduces a highly efficient, robust motion control framework for high-DOF robots, which can improve the trajectory tracking performance in the presence of model uncertainties.