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Proceedings Paper

Neural network internal model process control
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Paper Abstract

In industrial process control, many processes or plants are already stable. Thus, the desired process transient behavior and steady state error are the design constraints in these cases. Two common control techniques used in process control are internal model control (IMC) or Proportional Integral Derivative (PID) control. IMC can only be used on already stable or stabilized plants or processes due to its structure. Many plants or processes though cannot be completely identified or are modeled using reduced order linear models. This can lead to modeling errors. On the other hand, neural networks can be used to identify nonlinear processes or functions. In this research, neural networks are used for intelligent/adaptive system identification of the plant to be utilized in the internal model control. This adaptive neural network IMC structure is simulated to control a simplified process model. The efficacy of the neural network IMC method is compared to classic PID control.

Paper Details

Date Published: 15 April 2008
PDF: 10 pages
Proc. SPIE 6961, Intelligent Computing: Theory and Applications VI, 69610M (15 April 2008); doi: 10.1117/12.784091
Show Author Affiliations
Lifford McLauchlan, Texas A&M Univ., Kingsville (United States)
Mehrübe Mehrübeoğlu, Texas A&M Univ., Corpus Christi (United States)

Published in SPIE Proceedings Vol. 6961:
Intelligent Computing: Theory and Applications VI
Kevin L. Priddy; Emre Ertin, Editor(s)

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