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Modification to adaptive model reduction for regulation of distributed parameter systems with fast transients
Author(s) -
Pourkargar Davood Babaei,
Armaou Antonios
Publication year - 2013
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.14207
Subject(s) - control theory (sociology) , controller (irrigation) , linear subspace , representation (politics) , reduction (mathematics) , computer science , nonlinear system , construct (python library) , focus (optics) , process (computing) , adaptive control , mathematics , control (management) , artificial intelligence , physics , geometry , optics , quantum mechanics , politics , law , political science , agronomy , biology , programming language , operating system
We focus on output feedback control of distributed processes whose infinite dimensional representation in appropriate Hilbert subspaces can be decomposed to finite dimensional slow and infinite dimensional fast subsystems. The controller synthesis issue is addressed using a refined adaptive proper orthogonal decomposition (APOD) approach to recursively construct accurate low dimensional reduced order models (ROMs) based on which we subsequently construct and couple almost globally valid dynamic observers with robust controllers. The novelty lies in modifying the data ensemble revision approach within APOD to enlarge the ROM region of attraction. The proposed control approach is successfully used to regulate the Kuramoto‐Sivashinsky equation at a desired steady state profile in the absence and presence of uncertainty when the unforced process exhibits nonlinear behavior with fast transients. The original and the modified APOD approaches are compared in different conditions and the advantages of the modified approach are presented. © 2013 American Institute of Chemical Engineers AIChE J , 59: 4595–4611, 2013