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LF‐LPV input/output data‐based predictive controller design for nonlinear systems
Author(s) -
Yoo KeeYoun,
Rhee HyunKu
Publication year - 2002
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.690480913
Subject(s) - model predictive control , control theory (sociology) , controller (irrigation) , nonlinear system , system identification , subspace topology , identification (biology) , computer science , engineering , control engineering , control (management) , data modeling , artificial intelligence , physics , botany , quantum mechanics , database , agronomy , biology
Most control engineers concentrate on finding a controller given the plant model or identifying a model from the data. There is no doubt that model‐based control and system identification are closely related, simply because one depends strongly on the other. In this work a subspace identification algorithm for LF‐LPV (linear‐fractional linear parameter‐varying) models is reformulated from a control point of view. This algorithm is referred to as an input/output data‐based predictive control, in which an explicit model of the system to be controlled is not calculated at any point in the algorithm. It allows for the construction of a nonlinear model predictive controller for an unknown nonlinear system directly from a set of its open‐loop measurements. As an example of the input/output data‐based predictive control, the styrene solution polymerization in a continuous reactor system is considered to prove the superior performance of LF‐LPV input/output data‐based predictive controller for polymer quality control. This approach gives a new angle for attacking the problem of identifying and controlling nonlinear systems.