Adaptive learning control of robot manipulators in task space

Adaptive sliding mode control is considered, together with an iterative learning control scheme for tracking control of robot manipulators in Cartesian space coordinates. It is shown that the algorithm is globally convergent in the presence of external disturbances and modelling uncertainties. Owing to the robustness of the algorithm, a large gain for learning control can be used to achieve fast convergence of tracking errors. Moreover, the control scheme is rather simple and the inverse of the Jacobian matrix is not required.