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• W2000724077 abstract "In this contribution, two methods for adaptation of non-linear adaptive controllers are presented and compared, namely the data-driven and the knowledge-based adaptation. A dynamic Takagi–Sugeno fuzzy model is utilized to model the non-linear process behaviour. Based on this model, a non-linear predictive controller is designed to control the process. In the presence of time-variant process behaviour and changing unmodelled disturbances, high control performance can be achieved by performing an on-line adaptation of the fuzzy model. First, a local weighted recursive least-squares algorithm is used for adaptation. It exploits the local linearity of the Takagi–Sugeno fuzzy model. In the second approach, process knowledge that is obtained from theoretical insights is utilized to design a knowledge-based adaptation strategy. Both approaches are compared and their effectiveness and real-world applicability are demonstrated by application to temperature control of a heat exchanger. Copyright © 2001 John Wiley & Sons, Ltd." @default.
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• W2000724077 date "2001-01-01" @default.
• W2000724077 modified "2023-10-03" @default.
• W2000724077 title "Non-linear adaptive control of a heat exchanger" @default.
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• W2000724077 doi "https://doi.org/10.1002/acs.660" @default.
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