Robustification of iterative learning control and repetitive control by averaging

This paper describes a recently developed averaging technique to robustify iterative learning and repetitive controllers. The robustified controllers are found by minimizing cost functions that are averaged over either multiple analytical time-domain models or experimental frequency-domain data. The technique is simple and general, and it can be applied to any iterative learning control (ILC) or repetitive control (RC) design that minimizes a cost function. Very substantial improvement in convergence to zero tracking error in the presence of model uncertainties can be achieved for both ILC and RC by this averaging technique.