Model-free Predictive Torque Control of an Induction Machine Based on Parameter Estimation

Hector Young, Mahdi Nematshahi, Sanaz Sabzevari, Rasool Heydari, Freddy Flores-Bahamonde, Catalina Gonzalez, Yongchang Zhang, Jose Rodriguez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The uncertainty or variation of the electric machine parameters in predictive torque control (PTC) has a noticeable impact on the controller's performance. This paper proposes a model-free PTC strategy based on the estimation of the prediction model parameters using input and output data of the controlled system, applied to an induction machine. This approach has the advantage of not requiring a detailed previous knowledge of the system, with a high robustness to mismatch in the inductance parameters of the machine. The stator resistance is identified as a critical parameter for PTC, therefore an adaptation mechanism based on support vector regression is proposed to increase the robustness of the system. Simulation tests are carried out to validate the effectiveness of the proposed strategy.

Original languageEnglish
Title of host publication6th IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages725-731
Number of pages7
ISBN (Electronic)9781665425575
DOIs
Publication statusPublished - 2021
Event6th IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2021 - Jinan, China
Duration: 20 Nov 202122 Nov 2021

Publication series

Name6th IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2021

Conference

Conference6th IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2021
Country/TerritoryChina
CityJinan
Period20/11/2122/11/21

Keywords

  • induction motors
  • Model-free predictive control
  • parameter estimation
  • support vector regression

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Energy Engineering and Power Technology
  • Control and Systems Engineering

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