Theory and implementation of a hybrid learning force control scheme

Theory and implementation of a repetitive learning control algorithm for hybrid position and force control of a robotic manipulator are presented. The complete control system will involve learning position control for translational motion tangent to an unknown surface, learning force control normal to the surface, and learning orientation control using torque feedback to maintain tangential motion relative to the surface. An IBM 7545 robot equipped with a wrist force/torque sensor was used to evaluate the performance of the proposed controller. It was found that the repetitive learning algorithm outperformed a compatible proportional-integral-derivative (PID) controller by a significant margin in terms of the tracking accuracy. This implementation also demonstrates the great flexibility and wide range of application of this control scheme, as well as its robustness to nonperiodic disturbances