This paper addresses the initial shift problem in iterative learning control with system relative degree. The tracking error caused by nonzero initial shift is detected when applying a conventional learning algorithm. Finite initial rectifying action is introduced in the learning algorithm and is shown e ective in the improvement of tracking performance, in particular robustness with respect to variable initial shifts. The uniform convergence of the output trajectory to a desired one jointed smoothly with a speci