Extension of dynamic matrix control to multiple models

Abstract

The purpose of the paper is to extend dynamic matrix control (DMC) to handle different operating regimes and to reject parameter disturbances. This is done by two new multiple model predictive control (MMPC) schemes: one based on actual step response tests and the other on a minimal knowledge based first order plus dead time models (FOPDT). Both approaches do not require fundamental modeling. As a benchmark comparison, the two controllers are compared with a nonlinear model predictive controller (NL-MPC) using an extended Kalman filter (EKF) with no initial model/plant mismatch. The application example is the isothermal Van de Vusse reaction, which exhibits challenging input multiplicity. Simulations include disturbances in the feed concentration, kinetic parameters, and additive input and output noise. The two controllers have comparable performance to NL-MPC and in the case of multiple disturbances can outperform NL-MPC.