Premium
Neuro‐Predictive Control of an Infrared Dryer with a Feedforward‐Feedback Approach
Author(s) -
Mohammadzaheri Morteza,
Chen Lei,
Mirsepahi Ali,
Ghanbari Mehdi,
Tafreshi Reza
Publication year - 2015
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1038
Subject(s) - feed forward , control theory (sociology) , model predictive control , pid controller , controller (irrigation) , control system , temperature control , computer science , control engineering , engineering , control (management) , artificial intelligence , agronomy , electrical engineering , biology
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.