Experimental Validation of Robust Iterative Learning Control on an Overhead Crane Test Setup

This paper presents an experimental validation of a recently proposed robust normoptimal iterative learning control (ILC). The robust ILC input is computed by minimizing the worst-case value of a performance index under model uncertainty, yielding a convex optimization problem. The proposed robust ILC design is experimentally validated on a lab scale overhead crane system, showing the advantages of the approach over classical ILC designs in monotonic convergence and tracking performance.