Comparison of global nonlinear models and 'model-on-demand' estimation applied to identification of a RTP wafer reactor

M. W. Braun, Daniel Rivera, A. Stenman, W. Foslien

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

'Model on Demand' (MoD) simulation of the temperature dynamics in a simulated Rapid Thermal Processing (RTP) reactor is compared against various types of global models (ARX, semiphysical, combined semiphysical with neural net). The identification data is generated from a m-level pseudo-random sequence input whose parameters are specified systematically using a priori information readily available to the engineer. The MoD estimator outperforms the ARX model and two semi-physical models, while matching the performance of a combined semi-physical with neural net model. This makes MoD estimation an appealing alternative to global methods because of its reduced engineering effort and simplified a priori knowledge regarding model structure.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Decision and Control
PublisherIEEE
Pages3950-3955
Number of pages6
Volume4
StatePublished - 1999
EventThe 38th IEEE Conference on Decision and Control (CDC) - Phoenix, AZ, USA
Duration: Dec 7 1999Dec 10 1999

Other

OtherThe 38th IEEE Conference on Decision and Control (CDC)
CityPhoenix, AZ, USA
Period12/7/9912/10/99

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality

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