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Fuzzy multi‐model based adaptive predictive control and its application to thermoplastic injection molding
Author(s) -
Li Mingzhong,
Yang Yi,
Gao Furong,
Wang Fuli
Publication year - 2001
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450790209
Subject(s) - fuzzy logic , model predictive control , control theory (sociology) , representation (politics) , computer science , fuzzy control system , fuzzy rule , interval (graph theory) , nonlinear system , range (aeronautics) , mathematical optimization , artificial intelligence , algorithm , mathematics , control (management) , engineering , physics , combinatorics , quantum mechanics , aerospace engineering , politics , political science , law
Many chemical processes are inherently nonlinear. A single linear model is ineffective for these processes. Several local linear models may be developed for different operating conditions. A combination of these local models, through a fuzzy logic representation, results in an overall model for a wider operation range. In this paper, on‐line improvements and a fuzzy multi‐model have been proposed for predictive control implementation. Firstly, assuming that the premises of the fuzzy rules keep their original structures, the linear parameters in the rule consequents are on‐line updated by a weighted recursive least squares algorithm at each sample interval. Secondly, a batch learning algorithm is proposed to tune the fuzzy rule premises using a competitive learning algorithm. The effectiveness of the proposed improvements is demonstrated with experimental applications to the filling velocity control of thermoplastic injection molding