Alireza Sharifi; Mahdi Foroughi; H. Nobahari
Volume 10, Issue 4 , March 2018, , Pages 9-17
Abstract
In this paper, an adaptive-neuro-fuzzy controller is implemented online for a temperature control system using model-based design. First, the time domain identification approaches are utilized for the dynamic model identification. Then, the identified model is used in the adaptive-neuro-fuzzy controller. ...
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In this paper, an adaptive-neuro-fuzzy controller is implemented online for a temperature control system using model-based design. First, the time domain identification approaches are utilized for the dynamic model identification. Then, the identified model is used in the adaptive-neuro-fuzzy controller. The simulated model of the proposed controller, created in the Simulink environment, is translated into C code using Simulink Coder. The generated C code is compiled into a hardware device and is successfully embedded on a microcontroller. In the next step, the experimental setup of a temperature controller is done to verify the adaptive-neuro-fuzzy controller. Finally, a comparison was made between the proposed controller and a classical proportional-integral-derivative controller to investigate the performance of the proposed approach. The results demonstrate that the proposed approach provides an excellent performance for a temperature control system.
