In this paper, nine adaptive control
algorithms are compared. The best two of them are tested
experimentally. It is shown that the Adaptive FeedForward Controller AFFC)
is well suited for learning the parameters of the dynamic
equation, even in the presence of friction and noise. The
resulting control performance is better than with measured parameters for
any trajectory in the workspace. When the task consists of
repeating the same trajectory, an adaptive look-up-table MEMory, introduced and
analyzed in this paper, is simpler to implement and results
in even better control performance.