Title: Stabilizing model predictive control for nonlinear systems

Abstract: Model Predictive Control (MPC), also referred to as Receding
Horizon (RH) control, is an optimization based tool for designing control laws
for a wide range of industrial plants because of its capability of handling
physical constraints and multivariable systems. MPC for linear
constrained systems is widely accepted and used in many practical process control
applications. The expectations for Nonlinear Model Predictive Control (NMPC, i.e.
MPC based on a nonlinear plant description) are high, because of its additional
ability to take into account process nonlinearities. The possible success in
industrial applications is strongly related to the fulfillment of the following
properties: low computational load, large domain of attraction, good performance
and robustness. In the lecture these aspects will be discussed with reference to
the algorithms proposed in the literature of the last years.