This paper is devoted to the rotor angular velocity estimation of the permanent-magnet synchronous motor (PMSM). It is an actual problem, for example, in sensorless control. We consider a classical, two-phase model in the stator frame of the unsaturated, non-salient PMSM in the state-space representation. All parameters of the model except the stator windings resistance and rotor inertia are assumed to be known. On the first step, we find the relation between measured signals and angular velocity and excluding the unknown parameters of the motor. This relation is simplified using properties of the measured signals and represented as the first-order regression model, where the unknown parameter is the angular velocity. On the next step, we propose the estimation scheme, which is based on the gradient descent method. The efficiency is illustrated through a set of numerical simulations.

Original languageEnglish
Title of host publicationConvergent Cognitive Information Technologies - 3rd International Conference, Convergent 2018, Revised Selected Papers
EditorsVladimir Sukhomlin, Elena Zubareva
PublisherSpringer Nature
Pages251-260
Number of pages10
ISBN (Print)9783030374358
DOIs
StatePublished - 2020
Event3rd International Scientific Conference on Convergent Cognitive Information Technologies, Convergent 2018 - Moscow, Russian Federation
Duration: 29 Nov 20182 Dec 2018

Publication series

NameCommunications in Computer and Information Science
Volume1140 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference3rd International Scientific Conference on Convergent Cognitive Information Technologies, Convergent 2018
Country/TerritoryRussian Federation
CityMoscow
Period29/11/182/12/18

    Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

    Research areas

  • Parameter identification, Permanent magnet synchronous motor, Real-time, Sensorless control

ID: 76547418