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 · Control and Cybernetics · 2007
Control and Intelligent Processing Center of ExcellenceElectrical and Computer Engineering Department, University of Tehran, Irane-mail: [email protected], [email protected], [email protected],[email protected]: In this paper, rotor speed and position of a SwitchedReluctance Motor (SRM) are controlled using an intelligent controlalgorithm. The controller is working based on a PID signal whileits gain is permanently tuned by means of an Emotional LearningAlgorithm to achieve a better control performance. Here, nonlinearcharacteristic of SRM is identified using an efficient training algo-rithm (LoLiMoT) for Locally Linear Neurofuzzy Model as an un-specified nonlinear plant model. Then, the Brain Emotional Learn-ing Based Intelligent Controller (BELBIC) is applied to the obtainedmodel. While the intelligent controller works based on a computa-tional model of a limbic system in the mammalian brain, its contri-bution is to improve the performance of a classic controller like PIDwithout much more control effort. The results demonstrate excellentimprovements of control action in different working situations.Keywords: intelligent control, emotion based learning, neuro-fuzzy models, switched reluctance motor.