Predictive functional control based on an adaptive fuzzy model of a hybrid semi-batch reactor

In this paper a new approach to the control of a nonlinear, time-varying process is proposed. It is based on a recursive version of the fuzzy identification method and predictive functional control. First, the recursive fuzzy identification method is derived, after which it is used in connection wit...

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Bibliographic Details
Published in:Control engineering practice 2010-08, Vol.18 (8), p.979-989
Main Authors: DOVZAN, Dejan, SKRJANC, Igor
Format: Article
Language:English
Subjects:
Adaptative systems
Adaptive control systems
Applied sciences
Clustering
Computer science
control theory
systems
Control theory. Systems
Exact sciences and technology
Fuzzy
Fuzzy logic
Fuzzy predictive functional control
Fuzzy set theory
Identification methods
Mathematical models
Modelling and identification
Nonlinearity
Online recursive identification
Reactors
Recursive fuzzy clustering
Recursive fuzzy identification
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Summary:In this paper a new approach to the control of a nonlinear, time-varying process is proposed. It is based on a recursive version of the fuzzy identification method and predictive functional control. First, the recursive fuzzy identification method is derived, after which it is used in connection with fuzzy predictive functional control to construct an adaptive fuzzy predictive functional controller. The adaptive FPFC is then tested on a nonlinear, time-varying, semi-batch reactor process and compared with the standard FPFC, which uses non-adaptive fuzzy model. The simulation results are promising; they indicate that the control of time-varying, nonlinear processes with the FPFC can be improved with the use of an adaptive fuzzy model. An improvement in reference tracking and disturbance rejection can be observed, but the main advantage is the reduced number of switchings between hot and cold water. This is an important improvement in the case of real applications.
ISSN:0967-0661
1873-6939
DOI:10.1016/j.conengprac.2010.04.004