An iterative learning control scheme for manipulators

This paper presents an iterative learning control scheme for high-geared industrial manipulators that perform repeated tasks. The input update law of the iterative learning control is given in the frequency domain, together with a sufficient condition for convergence of the iterative process. The proposed control scheme only utilizes locally measured signals and does not require the computation of complicated nonlinear manipulator dynamics. Moreover, on the assumption of repeating a task, unmodeled dynamics of a manipulator can be effectively canceled by the iterative learning control, without even a parametric model. Several important aspects of the iterative learning control scheme inherent in the frequency-domain design are discussed and the control performance is demonstrated through computer simulations