An adaptive learning control of uncertain robotic systems

An iterative learning control scheme for precise tracking and parameter estimation of uncertain robotic systems is presented. The learning control scheme is globally convergent in the presence of disturbances and parameter variations. Under the persistent excitation condition in the domain of iteration sequence, it is proved that the estimated system parameters converge to the desired ones. The parameter estimator of the proposed learning control scheme does not use any system acceleration and inversion of the estimated inertia matrix, which makes the controller implementation more practical