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Robust steady‐state target calculation for model predictive control
Author(s) -
Kassmann Dean E.,
Badgwell Thomas A.,
Hawkins Robert B.
Publication year - 2000
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.690460513
Subject(s) - model predictive control , steady state (chemistry) , control theory (sociology) , hierarchy , interior point method , computer science , point (geometry) , dual (grammatical number) , state (computer science) , control (management) , mathematical optimization , mathematics , algorithm , artificial intelligence , art , chemistry , geometry , literature , economics , market economy
Abstract In practice, model predictive control (MPC) algorithms are typically embedded within a multilevel hierarchy of control functions. The MPC algorithm itself is usually implemented in two pieces: a steady‐state target calculation followed by a dynamic optimization. A new formulation of the steady‐state target calculation is presented that explicitly accounts for model uncertainty. When model uncertainty is incorporated, the linear program associated with the steady‐state target calculation can be recast as a second‐order cone program. This article shows how primal‐dual interior‐point methods can take advantage of the resulting structure. Simulation examples illustrate the effect of uncertainty on the steady‐state target calculation and demonstrate the advantages of interior‐point methods.