Robustness of learning control for robot manipulators

A class of simple learning control algorithms having a forgetting factor but not making use of the derivative of velocity signals for motion control of robot manipulators is proposed, and its convergence property is discussed. The robustness of such a learning control scheme with respect to initialization errors, disturbances, and measurement noise is studied. It is proved that motion trajectories converge to a neighborhood of the desired trajectory and eventually remain in it. Relationships of the size of attraction neighborhoods to the magnitudes of initialization errors and other disturbances are obtained, suggesting a rule for selection of the forgetting factor in the progress of learning