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Constrained predictive control using orthogonal expansions
Author(s) -
Finn Cory K.,
Wahlberg Bo,
Ydstie B. Erik
Publication year - 1993
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.690391109
Subject(s) - control theory (sociology) , mathematics , model predictive control , laguerre polynomials , bounded function , a priori and a posteriori , mathematical optimization , constraint satisfaction , convergence (economics) , stability (learning theory) , computer science , control (management) , mathematical analysis , philosophy , statistics , epistemology , artificial intelligence , machine learning , probabilistic logic , economics , economic growth
In this article, we approximate bounded operators by orthogonal expansion. The rate of convergence depends on the choice of basis functions. Markov‐Laguerre functions give rapid convergence for open‐loop stable systems with long delay. The Markov‐Kautz model can be used for lightly damped systems, and a more general orthogonal expansion is developed for modeling multivariable systems with widely scattered poles. The finite impulse response model is a special case of these models. A‐priori knowledge about dominant time constants, time delay and oscillatory modes is used to reduce the model complexity and to improve conditioning of the parameter estimation algorithm. Algorithms for predictive control are developed, as well as conditions for constraint compatibility, closed‐loop stability and constraint satisfaction for the ideal case. An H∞‐like design technique proposed guarantees robust stability in the presence of input constraints; output constraints may give “chatter.” A chatter‐free algorithm is proposed.