Global optimization method for model predictive control based on Wiener model

This paper deals with model predictive control based on Wiener model. The nonlinear block of the considered model is represented by a polynomial relation and the model´s parameters are determined using the neural networks. A global optimization method, i.e. the generalized geometric programming method, is used to solve the nonconvex optimization control problem. The efficiency of the proposed controller is illustrated through a simulation example.