Noise tolerant iterative learning control for a class of continuous-time systems

The paper proposes a noise tolerant iterative learning control (ILC) for a class of linear continuous-time systems, which achieves high-precision tracking for uncertain plants by iteration of trials in the presence of heavy measurement noise. The robustness against measurement noise is achieved through (i) projection of continuous-time I/O   signals onto a finite-dimensional parameter space, (ii) using error data of all past iterations via an integral operation in the learning law and (iii) noise reduction by H2H2 optimization subject to a specified convergence speed of the ILC.