Neural network internal model process control

Lifford McLauchlan; Mehrübe Mehrübeoğlu

doi:10.1117/12.784091

15 April 2008 Neural network internal model process control

Proceedings Volume 6961, Intelligent Computing: Theory and Applications VI; 69610M (2008) https://doi.org/10.1117/12.784091
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

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.

Citation Download Citation

Lifford McLauchlan and Mehrübe Mehrübeoğlu "Neural network internal model process control", Proc. SPIE 6961, Intelligent Computing: Theory and Applications VI, 69610M (15 April 2008); https://doi.org/10.1117/12.784091

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