Adaptive Sliding Mode Disturbance Observer Based Robust Control for Robot Manipulators Towards Assembly Assistance

Abstract

Parameter uncertainties and fluctuated disturbances have brought great difficulties to the smooth and precise control of robot manipulators in some industrial environments. To address these challenges, a robust adaptive sliding mode controller is proposed in this work for accurate control of the robot manipulator. In case of the modeling uncertainties caused by parameters variation during the operational process, the adaptive technique integrated with radial basis function neural networks is utilized. Under such scheme the parameters are automatically adjusted during the tracking process to enhance the system robustness. A new sliding mode disturbance observer is adopted in order to give compensation of the interference and a new variable structure scheme is proposed to effectively suppress the chattering phenomenon. Based on robot assembly and other application verification, the robustness and stability of the proposed approach are demonstrated with superior and smooth tracking performance.