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A nonlinear gain scheduling control strategy based on neuro-fuzzy networks

Lookup NU author(s): Dr Jie ZhangORCiD


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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.

Publication metadata

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


DOI: 10.1021/ie990866h


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