Global optimization method for Min-Max MPC based on Wiener and Hammerstein model

In the present work, a global optimization method known as the Generalized Geometric Programming (GGP) is used. The technique of convexification used in the present work is different from others presented in earlier works. The presented GGP allows to obtain the global optimum by few transformation applied to the original optimization problem. But for the other convexification technique many constraints will be taken into account to get the convex criterion. The GGP method allows to compute the optimal control sequence over a prediction horizon. The obtained sequence of input control is the solution of a min-max optimization problem. Hammerstein and Wiener models are presented where bounded uncertainties are considered with respect to parameters of the linear bloc. The efficiency of the GGP method is demonstrated through a simulation example.