This paper addresses the problem of dynamic iterative learning control for discrete linear repetitive processes with different relative degrees, whose aim is to develop monotonically convergent control law design over a finite frequency domain. For the control objects with zero relative degree and high relative degree, the dynamic iterative learning controllers in the finite frequency domain are designed by combining the two-dimensional (2D) system theory. Using the generalized Kalman-Yakubovich-Popov (KYP) lemma, the sufficient conditions for the existence of the controller and the gain matrix of the controller are given in the form of linear matrix inequalities (LMI). Finally, the superiority and feasibility of such a control law are tested on a spring damping system and a gantry robot, including a comparative performance against a static law applied to the same robot.