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Selection of control configurations for economic model predictive control systems
Author(s) -
Ellis Matthew,
Christofides Panagiotis D.
Publication year - 2014
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.14514
Subject(s) - selection (genetic algorithm) , process (computing) , function (biology) , control (management) , control function , computer science , control theory (sociology) , nonlinear system , sensitivity (control systems) , economic model , process control , engineering , control engineering , mathematical optimization , mathematics , economics , machine learning , artificial intelligence , physics , quantum mechanics , evolutionary biology , electronic engineering , biology , operating system , macroeconomics
Economic model predictive control (EMPC) is a feedback control method that dictates a potentially dynamic (time‐varying) operating policy to optimize the process economics. The objective function used in the EMPC system may be a general nonlinear function that describes the process/system economics. As this function is not derived on the sole basis of classical control considerations (stabilization, tracking, and optimal control action calculation) but rather on the basis of economics, selecting the appropriate control configuration, and quantifying the influence of a given input on an economic cost is an important task for the proper design and computational efficiency of an EMPC scheme. Owing to these considerations, an input selection methodology for EMPC is proposed which utilizes the relative degree and the sensitivity of the economic cost with respect to an input to identify and select stabilizing manipulated inputs with the most dynamic and steady‐state influence on the economic cost function to be assigned to EMPC. Other considerations for input selection for EMPC are also discussed and integrated into a proposed input selection methodology for EMPC. The control configuration selection method for EMPC is demonstrated using a chemical process example. © 2014 American Institute of Chemical Engineers AIChE J , 60: 3230–3242, 2014