H2-suboptimal Iterative Learning Control for Continuous-Time System Identification

This paper shows a new design method of the noise tolerant iterative learning control (ILC) for continuous-time system identification. First, we review the noise tolerant ILC method proposed by the authors, which updates the input in the finite parameter space to achieve perfect tracking for given reference signal. Second, we propose the H²-suboptimal ILC design subject to the specified pole location constraint in terms of LMI (linear matrix inequalities). This method turns out to be effective to achieve smaller variance of the estimated system parameters, and specifies the convergence speed of the ILC based identification. Finally, the effectiveness is demonstrated through simulation in the presence of heavy noises