Integration of real-time optimization and model predictive control

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...

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Bibliographic Details
Published in:	Journal of process control Vol. 20; no. 2; pp. 125 - 133
Main Authors:	Adetola, V., Guay, M.
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.02.2010
Subjects:	Adaptive control Model predictive control Real-time optimization Real-time optimization Model predictive control Adaptive control
ISSN:	0959-1524, 1873-2771
Online Access:	Get full text
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Summary:	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.
ISSN:	0959-1524 1873-2771
DOI:	10.1016/j.jprocont.2009.09.001