This paper proposes a controller design approach that integrates RTO and MPC for the control of constrained uncertain nonlinear systems. Assuming that the economic function is a known function of constrained system's states, parameterized by unknown parameters and time-varying, the controller design objective is to simultaneously identify and regulate the system to the optimal operating point. The approach relies on a novel set-based parameter estimation routine and a robust model predictive controller that takes into the effect of parameter estimation errors. A simulation example is used to demonstrate the effectiveness of the design technique. © 2009 Elsevier Ltd. All rights reserved.
CITATION STYLE
Adetola, V., & Guay, M. (2010). Integration of real-time optimization and model predictive control. Journal of Process Control, 20(2), 125–133. https://doi.org/10.1016/j.jprocont.2009.09.001
Mendeley helps you to discover research relevant for your work.