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Composite fast‐slow MPC design for nonlinear singularly perturbed systems
Author(s) -
Chen Xianzhong,
Heidarinejad Mohsen,
Liu Jinfeng,
Christofides Panagiotis D.
Publication year - 2012
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.13798
Subject(s) - singular perturbation , control theory (sociology) , model predictive control , nonlinear system , composite number , sampling (signal processing) , perturbation (astronomy) , stability (learning theory) , sampling time , computer science , mathematics , control (management) , physics , algorithm , mathematical analysis , filter (signal processing) , quantum mechanics , artificial intelligence , machine learning , computer vision , statistics
The design of a composite control system for nonlinear singularly perturbed systems using model predictive control (MPC) is described. Specifically, a composite control system comprised of a “fast” MPC acting to regulate the fast dynamics and a “slow” MPC acting to regulate the slow dynamics is designed. The composite MPC system uses multirate sampling of the plant state measurements, i.e., fast sampling of the fast state variables is used in the fast MPC and slow‐sampling of the slow state variables is used in the slow MPC. Using singular perturbation theory, the stability and optimality of the closed‐loop nonlinear singularly perturbed system are analyzed. A chemical process example which exhibits two‐time‐scale behavior is used to demonstrate the structure and implementation of the proposed fast–slow MPC architecture in a practical setting. © 2012 American Institute of Chemical Engineers AIChE J, 58: 1802–1811, 2012