Experiments in nonlinear adaptive control

In this paper, 2 control algorithms for learning and compensating for the dynamics of manipulators during the motion are presented and tested experimentally. It is shown that the adaptive feedforward controller (AFFC) is well suitable for learning the parameters of the dynamic equation, even, in presence of friction and noise. The resulting control performances are better than with measured parameters for any trajectory in the workspace. When the task consists of driving a repeated trajectory, an adaptive look-up-table MEMory, introduced and analyzed in this paper, is however simpler to implement and results in even better control performances.