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
Second International Conference on Informatics Research for Development of Knowledge Society Infrastructure (ICKS'07)