Abstract
A modeling and control method for a class of nonlinear systems whose dynamics depend on a process variable is presented. Two ExpARMAX (exponential ARMAX) models, a type of global NARMAX (nonlinear ARMAX) model are used to develop a multi-step predictive control algorithm which does not use on-line parameter estimation for the nonlinear system. One ExpARMAX model is used as the internal model of the predictive controller, and the other is used to predict future values of the measurable disturbances used in the predictive controller. The global ExpARMAX models only require off-line identification, and their local linearization forms are similar to linear ARMAX models. Case studies taken from a selective catalytic reduction (SCR) process for the reduction of Nitrogen Oxide (NOx) emissions of thermal power plants verify the effectiveness of the method.
