A Novel Iterative Learning Control Approach Based on Steady-State Kalman Filtering

This paper presents a novel off-line iterative learning control algorithm for multiple-input-multiple-output time-varying discrete stochastic systems. Using the steady-state Kalman filtering method, we provide a novel framework for the selection of optimal/sub-optimal fixed learning gain matrices in real applications, which is convenient for engineers. Meanwhile, this framework considerably decreases the calculation about the operations of inverting matrix by introducing a matrix Riccati equation at every iteration. It is strictly proved that the input error covariance converges to its steady-state value asymptotically in the mean square sense, and accordingly, the tracking error covariance also converges. The numerical simulations verify the theoretical results.