Emotional learning based intelligent speed and position control applied to neurofuzzy model of switched reluctance motor

• Autorzy: Rouhani H. , Sadeghzadeh A. , Lucas C. , Araabi B. N.
• Języki publikacji: EN

Abstrakty

EN

In this paper, rotor speed and position of a Switched Reluctance Motor (SRM) are controlled using an intelligent control algorithm. The controller is working based on a PID signal while its gain is permanently tuned by means of an Emotional Learning Algorithm to achieve a better control performance. Here, nonlinear characteristic of SRM is identified using an efficient training algorithm (LoLiMoT) for Locally Linear Neurofuzzy Model as an unspecified nonlinear plant model. Then, the Brain Emotional Learning Based Intelligent Controller (BELBIC) is applied to the obtained model. While the intelligent controller works based on a computational model of a limbic system in the mammalian brain, its contribution is to improve the performance of a classic controller like PID without much more control effort. The results demonstrate excellent improvements of control action in different working situations.

Słowa kluczowe

EN

intelligent control; emotion based learning; neuro-fuzzy models; switched reluctance motor

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2007
• Tom: Vol. 36, no 1
• Strony: 75--95
• Opis fizyczny: Bibliogr. 19 poz., rys., wykr.

Twórcy

autor

Rouhani H.

autor

Sadeghzadeh A.

autor

Lucas C.

autor

Araabi B. N.

• Mechanical Engineering Department, University of Tehran, Tehran, Iran,

Bibliografia

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