NONLINEAR PREDICTIVE CONTROL USING LOCAL MODELS - APPLIED TO A BATCH FERMENTATION PROCESS

The problem of controlling processes that operate within a wide range of operating conditions is addressed. The operation of the process is decomposed into a set of operating regimes, and simple local state-spa ce model structures are developed for each regime. These are combined into a global model structure using an interpolation method. Unknown l ocal model parameters are identified using empirical data. The control problem is solved using a model predictive controller based on this m odel representation. As an example, a simulated batch fermentation rea ctor is studied. The model-based controller's performance is compared to the performance with an exact process model, and a linear model. It is experienced that a non-linear model with good prediction capabilit ies can be constructed using elementary and qualitative process knowle dge combined with a sufficiently large amount of process data.