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Enhanced Adaptive Super-Twisting Current-Constrained Control of PMSM Based on Generalized Proportional-Integral Observer

Boqiang Wei*, Yiwei Feng

College of Automation and Electrical Engineering, Lanzhou University of Technology, Lanzhou, China

* Corresponding Author: Boqiang Wei. Email: email

Energy Engineering 2026, 123(10), 4 https://doi.org/10.32604/ee.2026.080617

Abstract

This paper presents an advanced control strategy, termed enhanced adaptive super-twisting current-constrained algorithm (CCEASTA), to address overcurrent protection challenges and reject disturbances in non-cascade permanent magnet synchronous motors (PMSM) drives. The proposed strategy consists of two fundamental components. Firstly, a gain-adaptive super-twisting sliding mode controller with current constraints is designed in a non-cascade framework. The purpose of this structure is to suppress chattering, accelerate dynamic response, and ensure overcurrent protection. Secondly, a generalized proportional-integral observer (GPIO) is incorporated to estimate unknown state variables and external disturbances in real time. The estimates thus obtained facilitate feed-forward compensation, thereby significantly reducing the impact of disturbances on the system.

Keywords

Permanent magnet synchronous motor; current-constrained control; enhanced adaptive super-twisting algorithm; generalized proportional-integral observer

Cite This Article

APA Style
Wei, B., Feng, Y. (2026). Enhanced Adaptive Super-Twisting Current-Constrained Control of PMSM Based on Generalized Proportional-Integral Observer. Energy Engineering, 123(10), 4. https://doi.org/10.32604/ee.2026.080617
Vancouver Style
Wei B, Feng Y. Enhanced Adaptive Super-Twisting Current-Constrained Control of PMSM Based on Generalized Proportional-Integral Observer. Energ Eng. 2026;123(10):4. https://doi.org/10.32604/ee.2026.080617
IEEE Style
B. Wei and Y. Feng, “Enhanced Adaptive Super-Twisting Current-Constrained Control of PMSM Based on Generalized Proportional-Integral Observer,” Energ. Eng., vol. 123, no. 10, pp. 4, 2026. https://doi.org/10.32604/ee.2026.080617



cc Copyright © 2026 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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