Position estimation of an outer rotor permanent magnet synchronous machine using linear hall-effect sensors and neural networks
Wang, Yuyao
Loading…
Permalink
https://hdl.handle.net/2142/105643
Description
Title
Position estimation of an outer rotor permanent magnet synchronous machine using linear hall-effect sensors and neural networks
Author(s)
Wang, Yuyao
Issue Date
2019-07-03
Director of Research (if dissertation) or Advisor (if thesis)
Haran, Kiruba S
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Permanent magnet synchronous machine, position estimation, Hall effect, neural networks
Abstract
This thesis presents and evaluates a new method for estimating the angular position for an outer rotor permanent magnet synchronous machine (PMSM). PMSMs are increasingly used as prime movers in electric vehicles such as cars and bicycles, and the precise control of these machines requires reliable feedback of the rotational position of the rotor. Conventional methods of achieving this feedback signal rely on either physically connected sensors or the implementation of sensorless methods, each of which has certain drawbacks.
The proposed method uses an array of linear Hall-effect sensors located in the leakage magnetic field of the rotor. These sensors detect the rotation-dependent changing field, which is fed into a machine-learning based neural network algorithm to interpret the signals. Due to the use of machine-learning, the algorithm will first need to be trained to properly correlate the sensor signals to the rotor angle. Data sets of training signals are acquired with commercial sensors and an outer rotor PMSM, and offline training steps and results are discussed. The main objective is to design a cost-effective position estimation system that is comparable to encoders and resolvers in functionality and performance, without the limitations of sensorless position estimation methods.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
