Normalized-Model Reference System for Parameter Estimation of Induction Motors

Adolfo Véliz-Tejo, Juan Carlos Travieso-Torres, Andrés A. Peters, Andrés Mora, Felipe Leiva-Silva

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


This manuscript proposes a short tuning march algorithm to estimate induction motors (IM) electrical and mechanical parameters. It has two main novel proposals. First, it starts by presenting a normalized-model reference adaptive system (N-MRAS) that extends a recently proposed normalized model reference adaptive controller for parameter estimation of higher-order nonlinear systems, adding filtering. Second, it proposes persistent exciting (PE) rules for the input amplitude. This N-MRAS normalizes the information vector and identification adaptive law gains for a more straightforward tuning method, avoiding trial and error. Later, two N-MRAS designs consider estimating IM electrical and mechanical parameters. Finally, the proposed algorithm considers starting with a V/f speed control strategy, applying a persistently exciting voltage and frequency, and applying the two designed N-MRAS. Test bench experiments validate the efficacy of the proposed algorithm for a 10 HP IM.

Original languageEnglish
Article number4542
Issue number13
StatePublished - 1 Jul 2022
Externally publishedYes


  • adaptive systems
  • induction motors
  • nonlinear dynamical systems
  • parameter estimation
  • persistent excitation


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