Non-linear adaptive control of a heat exchanger

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 (C) 2001 John Wiley Sons, Ltd.