An Adaptive Sliding-Mode Iterative Constant-force Control Method for Robotic Belt Grinding Based on a One-Dimensional...

Area of Science:

• Robotics
• Manufacturing Engineering
• Control Systems

Background:

• Robotic belt grinding requires precise force control for quality and efficiency.
• Existing methods struggle with grinding force uncertainties and dynamic variations.
• A one-dimension force sensor limits traditional force control approaches.

Purpose of the Study:

• To develop an adaptive sliding-mode iterative constant-force control for a 6-DOF robotic belt grinding platform.
• To address uncertainties in grinding forces and improve control system stability.
• To enhance the processing quality and efficiency of robotic belt grinding operations.

Main Methods:

• Revealed the relationship between normal and tangential grinding forces, presenting a simplified mapping for 1D sensors.
• Established a deformation-based dynamic model for robotic belt grinding.
• Proposed an adaptive iterative learning method combined with sliding mode control.

Main Results:

• Reduced grinding force fluctuation to less than 2N after ten iterations.
• Significantly decreased the mean, standard deviation, and variance of absolute grinding force error.
• Demonstrated significant improvement in the surface quality of machined parts.

Conclusions:

• The proposed force control method is effective for robotic belt grinding.
• The adaptive algorithm exhibits fast convergence and strong adaptability.
• This approach enhances both processing precision and efficiency in automated manufacturing.