Handling structural plant-model mismatch via multi-stage nonlinear model predictive control

Abstract

In this paper we present an approach to deal with structural uncertainties (structure and dimension of the model are not perfectly known) in the framework of nonlinear model predictive control (NMPC). The presented method is based on robust multi-stage NMPC which represents the uncertainty as a scenario tree with the possibility of recourse, reducing the conservativeness of the approach. In the case of structural mismatch, the unmodeled dynamics can cause a significant difference between the model used for the predictions and the reality, which can cause violations of constraints. We generate a scenario tree to account for the mismatch between the plant and the model by assuming a bound on the effect of the structural mismatch on the model. Thus the proposed scheme will be robust to the structural mismatch present in the model. We demonstrate the advantages of the proposed approach for a nonlinear continuous stirred tank reactor example.