Learning control for robot motion under geometric end-point constraint

Abstract

SUMMARY Learning control is a new approach to the probelm of skill refinement for robotic manipulators. It is considered to be a mathematical model of motor program learning for skilled motions in the central nervous system. This paper proposes a class of learning control algorithms for improving operations of the robot arm under a geometrical end-point constraint at the next trial on the basis of the previous operation data. The command input torque is updated by a linear modification of present joint velocity errors deviated from the desired velocity trajectory in addition to the previous input. It is shown that motion trajectories approach an e-neighborhood of the desired one in the sense of squared integral norm provided the local feedback loop consists of both position and velocity feedbacks plus a feedback term of the error force vector between the reactive force and desired force on the end-point constrained surface. It is explored that various passivity properties of residual error dynamics of the manipulator play a crucial role in the proof of uniform boundedness and convergence of position and velocity trajectories.