Browse by author
Lookup NU author(s): Dr Jie ZhangORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
A nonlinear gain scheduling control strategy based on neuro-fuzzy network models is proposed. In neuro-fuzzy-network-based modeling, the process operation is partitioned into several fuzzy operating regions, and within each region, a local linear model is used to model the process. The global model output is obtained through center-of-gravity defuzzification. Process knowledge is used to initially set up the network structure, and process input-output data are used to train the network. Based on a neuro-fuzzy network model, a nonlinear controller can be developed by combining several local linear controllers that are tuned on the basis of the local model parameters. This strategy represents a nonlinear gain scheduled controller. The techniques have been Successfully applied to the modeling and control of pH dynamics in a simulated continuous stirred tank reactor.
Author(s): Zhang J
Publication type: Article
Publication status: Published
Journal: Industrial and Engineering Chemistry Research
Year: 2001
Volume: 40
Issue: 14
Pages: 3164-3170
ISSN (print): 0888-5885
ISSN (electronic): 1520-5045
Publisher: American Chemical Society
URL: http://dx.doi.org/10.1021/ie990866h
DOI: 10.1021/ie990866h
Altmetrics provided by Altmetric
