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Design of robust constrained model‐predictive controllers with volterra series
Author(s) -
Genecili Hasmet,
Nikolaou Michael
Publication year - 1995
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.690410909
Subject(s) - control theory (sociology) , model predictive control , parametric statistics , volterra series , nonlinear system , robust control , stability (learning theory) , series (stratigraphy) , mathematics , work (physics) , time domain , computer science , control (management) , engineering , statistics , physics , computer vision , mechanical engineering , machine learning , biology , paleontology , quantum mechanics , artificial intelligence
Sufficient conditions are developed in this work for robust stability and pefformance of nonlinear model‐predictive control systems that use an end‐condition and second‐order Volterra model with parametric uncertainty in the time domain. The robust stabilfly conditions involve the lengths of the prediction and control horizons, as well as the coefficients of the control move suppression terms in the on‐line objective function. These conditions may be used for both analysis and synthesis purposes. A case study of a chemical reactor is presented to elucidate these issues.