Convex optimisation in nonlinear predictive control based on fractional Hammerstein model

Nonlinear model predictive control is still an open subject especially when dealing with fractional models. Regarding a variety of processes represented by nonlinear fractional models, this work is dealing with the predictive control of nonlinear systems based on the fractional Hammerstein models. The control problem can be non-convex where local optimization methods usually don’t lead to the best solution. In this paper, a new stochastic method which is the Honey Badger Algorithm is compared to the Nelder Mead method. Based on the curve of the nonlinear static characteristic of the system, the non-convex optimization problem is converted into a convex one by reducing the search domain using tightened constraints around the best solution. The realized schemes for non-convex and convex problems are illustrated through different simulation examples.