A novel nonlinear model predictive control approach for hybrid systems

In the last decade there has been a growing interested concerning nonlinear model predictive control (NMPC). However, the number of practical implementation of modem NMPC techniques is still very small due to the difficulties that have to be overcome to develop a practical implementable NMPC controller. In this paper the real-time implementation of a NMPC technique to a laboratory azeotropic distillation column is considered. The particular control hardware of the pilot distillation system leads to a hybrid control architecture. In this paper a novel hybrid control approach is introduced, which exploits the advantageous properties of genetic algorithm (GA) in the solution of the mixed real-binary optimization problem from the controller. An artificial neural network (ANN) model based NMPC (ANNMPC) is implemented, which leads to good control performance in the real system.