Neuro-control approach of switched reluctance motor drives

V. Trifa, Elena Gaura, L. Moldovan, Maione B. De Sario M. (Editor), Savino M. Pugliese P. (Editor)

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

3 Citations (Scopus)


The purpose of the paper is to present several studies on neural networks used for modelling a switched reluctance motor (SRM) with variable structure control. A positioning system with four-phase SRM is presented, in which the position error is processed by a sliding-mode controller. The control unit represents the subject of a neural network based model. The proposed network system has a feed-forward type architecture, structured on three layers of processing units. The networks are trained using the BKP algorithm. Once the network system is trained, it is integrated as a part of the positioning system. The training and testing sets of examples are obtained by numerical simulation of the positioning system using the Matlab environment.
Original languageEnglish
Title of host publication8th Mediterranean Electrotechnical Conference, 1996. MELECON '96.
Number of pages5
ISBN (Print)0-7803-3109-5
Publication statusPublished - 1996


  • Adaptive control systems
  • Algorithms
  • Calculations
  • Computer control
  • Computer simulation
  • Electric control equipment
  • Electric currents
  • Electric resistance
  • Mathematical models
  • Neural networks
  • Nonlinear equations
  • Vectors, Electromagnetic torque
  • Feedforward type architecture
  • Neuro-control approach
  • Nonlinear voltage equations
  • Positioning system
  • Real time control
  • Sliding mode controller
  • Switched reluctance motor drives
  • Torque equation
  • Vector control drives
  • Reluctance motors


Dive into the research topics of 'Neuro-control approach of switched reluctance motor drives'. Together they form a unique fingerprint.

Cite this