Kalman filter-augmented iterative learning control on the iteration domain

In this paper a novel stochastic iterative learning control (ILC) scheme is suggested to reduce the base-line error of the ILC system along the iteration axis. Assuming knowledge of the measurement noise and process noise statistics, our ILC scheme uses a Kalman filter to estimate the error of the output measurement and a fixed gain learning controller to ensure that the estimated error (also actual error) is less than a specified upper bound. An algebraic Riccati equation is solved analytically to find the steady-state covariance matrix and to prove that the system eventually converges to the base-line error. The effectiveness of the suggested method is illustrated through a numerical example