Abstract
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 language | English |
|---|---|
| Title of host publication | 8th Mediterranean Electrotechnical Conference, 1996. MELECON '96. |
| Publisher | IEEE |
| Pages | 1461-1464 |
| Number of pages | 5 |
| ISBN (Print) | 0-7803-3109-5 |
| DOIs | |
| Publication status | Published - 1996 |
Keywords
- 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