Iterative learning control (ILC) applies to systems that operate in repetitive mode. The goal of control is to ensure that the system’s output follows a reference trajectory with a specified accuracy. In ILC literature, each repetition is called a trial, and its finite duration is known as the trial length (also, the terms “pass” and “iteration” are commonly used). With each repetition, ILC should reduce the difference between the reference trajectory and the output signal called trial-to-trial error , ideally bringing it to zero. In this paper, a new ILC design method is proposed for multi-agent discrete systems based on the application of gradient optimization within 2D models in combination with the method of vector Lyapunov functions for repetitive processes. The new method yields a simple ILC structure that accelerates the convergence of the trial-to-trial error. An example confirming this property is provided.