Iterative identification method for linear continuous-time systems

This paper presents a novel approach to identification of continuous-time systems directly from the sampled I/O data based on trial iterations. The method achieves identification through ILC (iterative learning control) concepts 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 and (ii) Kalman filter type noise reduction. In addition, an alternative simpler method is given with some robustness analysis. Its effectiveness is demonstrated through numerical examples for a non-minimum phase plant