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

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.

Automated summary

This paper proposes a robust adaptive sliding mode controller to address the challenges of parameter uncertainties and fluctuated disturbances in the smooth and precise control of robot manipulators in industrial environments. The adaptive technique integrated with radial basis function neural networks is used to handle modeling uncertainties caused by parameter variation. A new sliding mode disturbance observer and variable structure scheme are adopted to compensate for interference and suppress the chattering phenomenon. Experimental results demonstrate the robustness, stability, and superior tracking performance of the proposed approach in robot assembly and other applications.