Increasing pushes toward next-generation/smart manufacturing motivate the development of economic model predictive control (EMPC) designs which can be practically deployed. For EMPC, the constraints, objective function, and accuracy of the state predictions would benefit from process models that describe the process physics. However, obtaining first- principles models of chemical process systems can be time-consuming or challenging such that it is preferable to develop physics-based process models automatically from process operating data. In this work, we take initial steps in this direction by suggesting that because experiments that are used to characterize first-principles models often target specific types of data, an EMPC may be utilized to gather non-routine operating data that ideally provides insights on the process physics and thereby allows physics-based process models to be developed on-line. These models can then be used to update the model, objective function, and constraints of the controller. Closed-loop stability and recursive feasibility considerations are discussed for the proposed EMPC design, and the controller's application is illustrated through a chemical process example.
Controls and Control Theory | Industrial Engineering | Operational Research | Process Control and Systems
Giuliani, L. and H. Durand, “Economic Model Predictive Control Design via Nonlinear Model Identification,” Proceedings of the 6th IFAC Conference on Nonlinear Model Predictive Control, 6 pages, Madison, Wisconsin, 2018.