Predictive repetitive control based on frequency decomposition

This paper develops a predictive repetitive control algorithm based on frequency decomposition. In particular, the periodic reference signal is first represented using a frequency sampling filter model and then the coefficients of the model are analyzed to determine its dominant frequency components. Using the internal model control principle, the dominant frequency components are embedded in model used to obtain the predictive repetitive control algorithm such that the periodic reference is followed with zero steady-state error. The design framework here is based on predictive control using Laguerre functions and hence plant operational constraints are naturally incorporated in the design and its implementation.