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https://hdl.handle.net/2440/94338
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Type: | Journal article |
Title: | Neuro-predictive control of an infrared dryer with a feedforward-feedback approach |
Author: | Mohammadzaheri, M. Chen, L. Mirsepahi, A. Ghanbari, M. Tafreshi, R. |
Citation: | Asian Journal of Control, 2015; 17(6):1972-1977 |
Publisher: | Wiley |
Issue Date: | 2015 |
ISSN: | 1561-8625 1934-6093 |
Statement of Responsibility: | Morteza Mohammadzaheri, Lei Chen, Ali Mirsepahi, Mehdi Ghanbari, and Reza Tafreshi |
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. |
Keywords: | Infrared dryer; processes with dead-time; predictive; GTZ systems |
Rights: | © 2014 Chinese Automatic Control Society and Wiley Publishing Asia Pty Ltd |
DOI: | 10.1002/asjc.1038 |
Published version: | http://dx.doi.org/10.1002/asjc.1038 |
Appears in Collections: | Aurora harvest 2 Mechanical Engineering publications |
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