A variably tuned multiple model predictive controller based on minimal process knowledge

A multiple model predictive controller is designed using minimal plant knowledge based on the ranges on gains, dominant time constants and time delays. The algorithm uses a weighted multiple model bank of first order plus deadtime models as the prediction model for a constrained model predictive controller. A variable tuning strategy is implemented to improve controller performance. The simulated process I´S the isothermal Van de Vusse reaction in a continuously stirred tank reactor (CSTR); this system exhibits input multiplicities, making it a challenging control problem