A comparison of fundamental model-based and multiple model predictive control

Abstract

A multiple model strategy is implemented in a model predictive control framework. The model bank design requires minimal plant knowledge based on the ranges of gains, dominant time constants and time delays. The application example is the isothermal Van de Vusse reaction in a continuous stirred tank reactor, which exhibits challenging input multiplicity behavior. Disturbances include additive input and output noises and changes in system parameters. Results are compared with an extended Kalman filter (EKF)-based model predictive controller that uses a fundamental model with a disturbance parameter estimated online. The multiple model predictive controller performance is comparable to that demonstrated by the EKF-based model predictive controller.