Paper Info
Title
Predictive Control Based On Local Linear Fuzzy Models
Abstract
This paper deals with predictive control based on fuzzy models. A novel algorithm (LOLIMOT) is proposed for the construction of Takagi-Sugeno fuzzy models. The rule consequents are optimized by a local orthogonal least-squares method that selects the significant regressors. The rule premises are optimized by a tree construction algorithm which partitions the input space in hyper-rectangles. A generalized predictive controller (GPC) and a dynamic matrix controller (DMC) are designed. Both controllers require the extraction of a linear model from the Takagi-Sugeno fuzzy model. For the GPC a new technique called local dynamic linearization is proposed that exploits the special structure of the local linear models. The DMC is based on the evaluation of a step response. The effectiveness of both the identification algorithm and the predictive controllers is shown by application to temperature control of an industrial-scale cross-flow heat exchanged.
Year
DOI
Venue
1998
10.1080/00207729808929563
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Keywords
Field
DocType
temperature control,heat exchanger,predictive control,linear model
Step response,Mathematical optimization,Control theory,Control theory,Linear model,Fuzzy logic,Model predictive control,Temperature control,Fuzzy control system,Linearization,Mathematics
Journal
Volume
Issue
ISSN
29
7
0020-7721
Citations 
PageRank 
References 
13
2.35
8
Authors
3
Name
Order
Citations
PageRank
Martin Fischer1132.35
Oliver Nelles29917.27
Rolf Isermann3722344.37