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NONLINEAR INTERNAL MODEL CONTROL FOR MISO SYSTEMS BASED ON LOCAL LINEAR NEURO-FUZZY MODELS
Alexander Fink*, Oliver Nelles†, and Rolf Isermann*
* Darmstadt University of Technology, Institute of Automatic Control
Laboratory of Control Engineering and Process Automation
Landgraf-Georg-Str. 4, D-64283 Darmstadt, Germany
Phone: +49 6151 162114 Fax: +49 6151 293445
E-mail: {AFink, RIsermann}@iat.tu-darmstadt.de
† Siemens VDO Automotive, AT PT DTS FDC
Osterhofener Str. 14, D-93055 Regensburg, Germany
E-mail: Oliver.Nelles@gmx.de
The internal model control (IMC) scheme has been widely applied in the field of process control. So far, IMC has been mainly applied to linear processes. This paper discusses the extension of the IMC scheme to nonlinear processes based on local linear models where the properties of linear design procedures can be exploited. The IMC scheme results in controllers that are comparable to gain-scheduled PI or PID controllers which are the standard controllers in process industry. In practice, the tuning of conventional PI or PID controllers can be very time-consuming whereas the IMC design procedure is very simple and reliable. In this paper, the design effort of the IMC and conventional controller design methods will be discussed and control results will be compared by application to nonlinear control of an industrial-scale heat exchanger.
Keywords: Nonlinear Control, Internal Model Control (IMC), Local Linear Models, Neuro-Fuzzy Models, MISO Systems, Heat Exchanger
Session slot T-We-M12: Artificial Intelligence in Real-Time Control/Area code 9c : Artificial Intelligence in Real-Time Control
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