Publication detail

Discrete-Time Modeling of PMSM for Parametric Estimation and Model Predictive Control Tasks

ZEZULA, L. BLAHA, P.

Original Title

Discrete-Time Modeling of PMSM for Parametric Estimation and Model Predictive Control Tasks

Type

conference paper

Language

English

Original Abstract

This paper presents novel implicit and explicit discrete-time permanent magnet synchronous motor models. Both derived models solve the problem of numerical instability and poor precision of motor currents' discrete-time prototypes formed using the forward Euler method and preserve the tolerable complexity of resulting descriptions. Discrete-time models of currents are derived based on the linear time-varying systems approach, considering the electrical angular velocity time-varying parameter. Angular velocity and angle are discretized by using the linear multistep methods. The implicit variant of the model is dedicated to parametric estimation tasks, and the explicit variant is to model predictive control. The derived descriptions are validated within the simulation by comparing the original continuous-time model and Euler approximation with the explicit model. Furthermore, the prediction capabilities of the explicit model and Euler approximation are compared as well.

Keywords

discrete-time systems, mathematical models, model checking, parameter estimation, permanent magnet motors, predictive control, predictive models, systems modeling

Authors

ZEZULA, L.; BLAHA, P.

Released

16. 11. 2023

Publisher

IEEE

ISBN

979-8-3503-3182-0

Book

IECON 2023: 49th Annual Conference of the IEEE Industrial Electronics Society

Pages count

6

URL

https://ieeexplore.ieee.org/document/10312226

Full text in the Digital Library

http://hdl.handle.net/11012/244305

BibTex

@inproceedings{BUT185380,
  author="Lukáš {Zezula} and Petr {Blaha}",
  title="Discrete-Time Modeling of PMSM for Parametric Estimation and Model Predictive Control Tasks",
  booktitle="IECON 2023: 49th Annual Conference of the IEEE Industrial Electronics Society",
  year="2023",
  pages="6",
  publisher="IEEE",
  doi="10.1109/IECON51785.2023.10312226",
  isbn="979-8-3503-3182-0",
  url="https://ieeexplore.ieee.org/document/10312226"
}

Documents

IECON_IPMSM_discr.pdf