Neuro-predictive control of an infrared dryer with a feedforward-feedback approach

Morteza Mohammadzaheri, Lei Chen, Ali Mirsepahi, Mehdi Ghanbari, Reza Tafreshi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this research, a hybrid control system is proposed to address the temperature control of an infrared dryer. The control system includes a feedback-predictive controller and a neural network steady state control law. The feedback-predictive controller outputs the amplified value of the predicted error as the transient control command. The predictive model was employed to suppress the undesirable effect of the dead-time of the system. A multilayer perceptron was designed and tested based on a control equilibrium point and steady state control to be used as a feedforward controller. The stability of the control system in a continuous domain was proved with no limit on the amplification gain of the predictive-feedback controller. In other words, there is no concern about losing stability with accelerating convergence towards the reference. The entire control system was constructed in Simulink and compiled to a C code and applied on the experimental setup. Experimental results are outstanding in comparison with the results of an interactively tuned IMC-based PID controller.

Original languageEnglish
Pages (from-to)1972-1977
Number of pages6
JournalAsian Journal of Control
Volume17
Issue number5
DOIs
Publication statusPublished - Sep 1 2015

Keywords

  • GTZ systems
  • Infrared dryer
  • Predictive
  • Processes with dead-time

ASJC Scopus subject areas

  • Control and Systems Engineering

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