System identification from multiple-trial data corrupted by non-repeating periodic disturbances - Phan, Minh Q.; Longman, Richard W.; Lee, Soo Cheol; Lee, Jae-Won (Zielona Góra: Uniwersytet Zielonogórski)
Iterative learning and repetitive control aim to eliminate the effect of unwanted disturbances over repeated trials or cycles. The disturbance-free system model, if known, can be used in a model-based iterative learning or repetitive control system to eliminate the unwanted disturbances. In the case of periodic disturbances, although the unknown disturbance frequencies may be the same from trial to trial, the disturbance amplitudes, phases, and biases do not necessarily repeat.
Keywords: system identification; disturbance identification; iterative learning control; repetitive control; interaction matrix