LEARNING CONTROL FOR ROBOT MOTION UNDER GEOMETRIC END-POINT CONSTRAINT

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 s ystem. This paper proposes a class of learning control algorithms for improving operations of the robot arm under a geometrical end-point co nstraint at the next trial on the basis of the previous operation data . The command input torque is updated by a linear modification of pres ent joint velocity errors deviated from the desired velocity trajector y in addition to the previous input. It is shown that motion trajector ies approach an epsilon-neighborhood of the desired one in the sense o f squared integral norm provided the local feedback loop consists of b oth position and velocity feedbacks plus a feedback term of the error force vector between the reactive force and desired force on the end-p oint constrained surface. It is explored that various passivity proper ties of residual error dynamics of the manipulator play a crucial role in the proof of uniform boundedness and convergence of position and v elocity trajectories.