IEEE Transactions on Automatic Control, Vol.61, No.1, 270-275, 2016
Discrete-Time Robust Iterative Learning Kalman Filtering for Repetitive Processes
A discrete-time, robust, iterative learning Kalman filter is proposed for state estimation on repetitive process systems with norm-bounded uncertainties in both the state and output matrices. The filter design combines iterative learning control and robust Kalman filtering by exploiting process repetitiveness.