Nonlinear dynamic matrix control using local models

This paper proposes the concept of using a Local Model Network (LMN) to identify a highly nonlinear chemical process, and to implement a Dynamic Matrix Controller (DMC) that uses the local model network as its internal model. The LMN is constructed of local linear AutoRegressive with external input (ARX) models [1], and is trained using a hybrid learning approach developed by McLoone et al. [2]. It is shown how this LMN structure is linked to a long range predictive controller, specifically Dynamic Matrix Control. Originally, a linear step response model was used as the internal model of the controller, however to extend to the control of a highly nonlinear process, step responses for different operating points are extracted from the LMN. Simulation results for the method, when applied to a pH neutralization process, indicate an improvemsnt in control over a standard DMC controller.