Predictive control of fractional Hammerstein models

Given that the Nonlinear Model Predictive Control (NMPC) of fractional Hammerstein models has not been well established, in literature, this work focuses on the NMPC based on these models. The algorithm is developed using two optimization methods, the Gradient Based Method (GBM) and the Nelder Mead Method (NM). The schemes are tested on models studied in the literature and they are compared in tracking performances and computing times.