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

Hossein Rouhani, Arash Sadeghzadeh, Caro Lucas, Babak Nadjar Araabi

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

11 Citaten (Scopus)

Samenvatting

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.

Originele taal-2Engels
Pagina's (van-tot)75-95
Aantal pagina's21
TijdschriftControl and Cybernetics
Volume36
Nummer van het tijdschrift1
StatusGepubliceerd - 31 aug 2007
Extern gepubliceerdJa

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