Convergence analysis of nonlinear model predictive control based on fractional Hammerstein model

Abstract

Nonlinear Model Predictive Control (NMPC) is still an open problem especially when dealing with more complexity in models. Regarding a variety of processes represented by nonlinear fractional models, this work is dealing with a predictive control algorithm based on the Fractional Hammerstein Models (FHM). The predictive NMPC algorithm is developed for SISO and MIMO models. Two deterministic optimisation methods are employed, the Gradient Based Method (GBM) and the Nelder Mead Method (NM). The algorithms are analysed and compared in tracking performances and computing times. The realized schemes are illustrated through simulation examples.