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Process regulation via genealogical decision trees
Author(s) -
Ikonen E.,
GomezRamirez E.,
Najim K.
Publication year - 2008
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.848
Subject(s) - sequence (biology) , computer science , optimal control , multivariable calculus , mathematical optimization , population , scheme (mathematics) , markov decision process , set (abstract data type) , process (computing) , model predictive control , decision tree , trajectory , state (computer science) , control (management) , markov process , mathematics , artificial intelligence , algorithm , engineering , control engineering , statistics , mathematical analysis , sociology , genetics , biology , operating system , programming language , physics , demography , astronomy
This paper deals with regulation control on the basis of genealogical decision trees (GDTs). GDTs are a population‐based random search technique for solving sequential multimodal and multivariable trajectory tracking problems, when gradient information is not available or does not exist. A direct application of GDT results in an open‐loop control. In this paper, feedback regulation based on GDT is considered. In the proposed scheme, GDTs are used for solving off‐line a number of predictive control problems; a finite set of initial states is then constructed from these simulations, for each of which an optimal control sequence has been computed. Natural handling of missing state vector measurements is provided. Numerical examples dealing with the van der Vusse CSTR illustrate the feasibility and the efficiency of this feedback control algorithm. A discussion on alternative approaches and a numerical comparison with the Markov‐decision‐process‐based optimal policy are provided. Copyright © 2008 John Wiley & Sons, Ltd.