Minimum‐Energy Iterative Learning Control for Multi‐Agent Systems With Optimal Intermediate‐Point Allocation

Iterative learning control applies to applications in which the same finite‐duration task is repeated, with each instance termed a trial. The objective is to track a specified reference trajectory over a finite duration, termed the trial length. In some applications, such as multi‐agent systems, tracking at each instant or point over the trial length is not required; only at selected points is it required, known as point‐to‐point iterative learning control. This article develops a new point‐to‐point design in which the points requiring tracking vary from trial to trial, and the solution minimizes energy, which is relevant to systems with a limited power budget. Also, an algorithm is developed to improve computational efficiency by sharing the burden among the agents forming the system. A numerical case study highlights the benefits of the new design.