Model predictive control of plant transitions using a new identification technique for interpolating nonlinear models

This paper presents a model based controller design approach for plants that operate in several distinct operating regimes and make transitions between them. Often it is difficult to identify a single global model that describes plant behavior in all the regimes. In the present work we propose an identification method that builds linear models for the individual regimes, and then interpolates nonlinear models in between these local models to match plant dynamics during transitions. The identification technique is shown to work well with transition data which lack excitation. A model predictive controller based on the local and the transition models is then presented and applied to a reactor.