Reset-free Iterative Learning Control and Its Application to Continuous-time System Identification

Abstract

This paper proposes a new approach for the projection type iterative learning control (ILC) with an application to identification of continuous-time systems. First, this paper gives a framework to perform ILC without resetting the initial condition at each iteration, which can be achieved by introducing the dynamics into the system representation in the finite-dimensional signal subspace. Second, it is shown how to identify the system parameters based on the proposed ILC. The method does not require us to wait for the equilibrium state patiently or reset the system forcibly in the identification process. Furthermore, a class of gain decreasing filters are introduced. Combination of these results gives us the estimates which converge to the true system parameters against measurement noise. A numerical example is given to demonstrate these properties of the proposed method.