An adaptive learning control method for constrained motion of uncertain robotic systems

Abstract

The authors propose a learning controller with parameter adaptation for constrained motion of robot manipulators. The additional computed torque error term is used in this controller to reduce the load from feedback and keeps its gain reasonably low. Position and force trackings are achieved by applying the position learning control algorithms to position controlled axes and the force learning control algorithms to force control axes. Joint acceleration terms are never used in the controller and the PE condition is not required for force tracking. The uniform convergence of position, velocity, acceleration and force trajectories is obtained under simple conditions.