Economic multi-stage output nonlinear model predictive control

Nonlinear Model Predictive control is one of the most promising control strategies in the field of advanced control. It can be used to optimize economic cost functions online satisfying all constraints which makes it very appealing in the context of industrial applications. In the last years, several robust NMPC methods have been presented. Among them, multi-stage stochastic NMPC has been proven to provide very promising results and to be computationally feasible by the use of advanced optimization tools. In this paper, we present an extension of the multi-stage approach that takes into account explicitly not only plant-model mismatch but also state estimation error through innovation sampling. We accommodate these errors into the resulting optimization problem by including them in the scenario tree formulation. We use a multiple-model estimation algorithm that fits to the multi-stage approach. The results are illustrated by simulation results of a chemical reactor.