Learning hybrid force and position control of robot manipulators

Abstract

A hybrid force and position controller with a feedforward compensator is designed in the constraint coordinates. When the operation is periodic, the learning control is applied to enhance the performance of the hybrid force and position control as the feedforward compensator is updated in each cycle by the force and position error in the preceding trials. This scheme is proved to be stable. In the experiments, a two-degree-of-freedom SCARA-type direct-drive robot manipulator was used. The deburring tool mounted on the upper link of the robot could follow a flat, tilted flat, and curved 1/4 inch aluminum plate with a desired contact force of 10 N, and with desired tangential velocity. Considering the loss of contact observed at the initial trial, the performance of the system improved significantly.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>