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.
['Zhixing Cao', 'Ridong Zhang', 'Yi Yang', 'Jingyi Lu', 'Furong Gao']
Discrete-Time Robust Iterative Learning Kalman Filtering for Repetitive Processes