Rejection of Periodic Disturbances Based on Adaptive Repetitive Model Predictive Control

The paper presents an adaptive strategy to reject periodic disturbances with unknown period based on a combination of model predictive control and repetitive control. A novel period estimator is presented. For the integer period case, the estimator is designed based on integer programming. For the non-integer period case, it is designed based on a two-step optimization, namely integer programming followed by a constrained least square method. With the estimated period, feedforward compensation is made to improve the tracking performance asymptotically. Simulation results are given to show the effectiveness of the algorithm.