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A novel adaptive neuro-fuzzy and adaptive proportional resonant control scheme for PMSM based electric vehicle applications
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  • Published: 10 February 2026

A novel adaptive neuro-fuzzy and adaptive proportional resonant control scheme for PMSM based electric vehicle applications

  • Elango Sangeetha1 &
  • Vijaya Priya Ramachandran2 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Mathematics and computing

Abstract

The permanent magnet synchronous motor (PMSM) offers a very competitive technology for field oriented control (FOC) technique in electric vehicles (EVs) due to its increased efficiency and high power density. However, achieving precise speed and current control under varying driving conditions remains a challenging part. To overcome the speed and current control issues in PMSM this paper proposed a two-stage control technique: one is an adaptive neuro- fuzzy inference system (ANFIS) based speed controller in the outer loop, while adaptive proportional resonant (PR) controller in the current loop. The ANFIS controllers effectively suppress the speed ripples during transient state, while the adaptive PR controller improves the sinusoidal current tracing performance and reduce the steady state error of the controller. The key contribution of the proposed controller is its ability to dampen torsional mode oscillation caused by steady state and dynamic condition in both speed and current loop. The effectiveness of the proposed controller is evaluated using MATLAB/Simulator and it is compared with hardware in the loop (HIL) real time simulator using RT5700. The proposed controller results show good transient efficiency, gives the best speed control in start-up, acceleration, deceleration and load changing situations also reduce the torque ripple and current harmonics in EV application compared to conventional controllers.

Data availability

All datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Funding

Open access funding provided by Vellore Institute of Technology.

Author information

Authors and Affiliations

  1. School of Electrical Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India

    Elango Sangeetha

  2. School of Electrical Engineering, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India

    Vijaya Priya Ramachandran

Authors
  1. Elango Sangeetha
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  2. Vijaya Priya Ramachandran
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Contributions

Conceptualization, E.S. and V.R.; methodology, E.S.; software, E.S.; validation, V.R.; formal analysis, V.R.; investigation, V.R.; resources, E.S.; data curation, E.S.; writing—originaldraft preparation, E.S.; writing—review and editing, V.R.; visualization, E.S.; supervision, V.R.; projectadministration, V.R.

Corresponding author

Correspondence to Vijaya Priya Ramachandran.

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The authors declare no competing interests.

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Cite this article

Sangeetha, E., Ramachandran, V.P. A novel adaptive neuro-fuzzy and adaptive proportional resonant control scheme for PMSM based electric vehicle applications. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35363-2

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  • Received: 02 September 2025

  • Accepted: 05 January 2026

  • Published: 10 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-35363-2

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Keywords

  • Adaptive neuro- fuzzy inference system
  • Electric vehicle (EV)
  • Permanent magnet synchronous motor and proportional resonant controller
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