论文信息 - FPGA based neural network position and speed estimator for switched reluctance motor drive

FPGA based neural network position and speed estimator for switched reluctance motor drive

This paper presents the design and implementation of the sensorless control system for the Switched Reluctance Motor (SRM) drive without a position sensor. The SRM has been receiving attention for industry applications due to its low cost in mass production, reduced maintenance requirements, rugged behavior and large torque output over very high speed range. On the other hand, torque ripple, acoustic noise and rotor position sensor requirements are the main disadvantages. This work has been supported by the Czech Ministry of Education - project: Modern Control of the Switched Reluctance Motor Drives - 913/2008/G1.

Jakub Talla | Josef Stehlik

[1] Seref Sagiroglu,et al. Modelling of a nonlinear switched reluctance drive based on artificial neural networks , 1994 .

[2] Timothy J. E. Miller,et al. Switched Reluctance Motors and Their Control , 1993 .

[3] R. Krishnan,et al. Switched Reluctance Motor Drives , 2019 .

[4] Peter Vas,et al. Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-neural, and Genetic-Algorithm-based Techniques , 1999 .

[5] D. Schroder,et al. Learning unknown nonlinearities using a discrete observer in combination with neural networks , 1995, IAS '95. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting.