Iterative learning based repetitive disturbance rejection with application to color registration error reduction in flatbed document scanners

For systems that perform repetitive tasks with identical initial condition for each trial, we propose an iterative learning control framework with repetitive disturbance rejection based on quadratic optimal zero phase repetitive control. Asymptotic stability condition is presented. The proposed method can be implemented as a calibration algorithm that requires less memory and computation resources. Experimental validation by reducing the repetitive color registration error on a flatbed document scanner illustrates the effectiveness of the proposed approach.