Projection Based Iterative Learning Control with Its Application to Continuous-Time System Identification

The paper proposes a new iterative learning control 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 H² optimization. Then, based on the proposed control method, a novel approach to identification of continuous-time systems directly from the sampled I/O data is presented. Its effectiveness is demonstrated through numerical examples