Unlike the model-based control, this chapter presents the trajectory tracking of parallel SCARA robot by means of iterative learning control, which is an open-loop control. After the introduction to the fundamentals of iterative learning control, two fuzzy based algorithms are designed by combining fuzzy control and iterative learning algorithm, namely, fuzzy open-loop iterative learning control and fuzzy adaptive iterative learning control, from the perspectives of practical application and algorithm complexity. The controller stability is proved based on Q-operator theory and Lyapunov theory, and fuzzy algorithm is used for gain tunings adaptively of the controller, to improve the anti-interference ability against the external disturbance. The simulation results show that the fuzzy adaptive iterative learning control algorithm can produce higher tracking accuracy and robustness, due to the introduction of the linearization of dynamic model.