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An intelligent optimization algorithm with novel fitness function for high-performance PMSM FOC.

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Title: An intelligent optimization algorithm with novel fitness function for high-performance PMSM FOC.
Authors: You, Zhou1 (AUTHOR), Bian, Yongming1 (AUTHOR), Zhang, Yifan1 (AUTHOR), Chen, Li1,2 (AUTHOR) lilychen@tongji.edu.cn
Source: Alexandria Engineering Journal. Mar2025, Vol. 115, p286-296. 11p.
Subject Terms: Optimization algorithms, Permanent magnet motors, Magnetic flux, Steady-state responses, Mathematical optimization
Abstract: This paper proposes an advanced intelligent optimization algorithm (IO-FOC) with a novel fitness function to improve the dynamic response and steady-state performance of field-oriented control (FOC) systems for permanent magnet synchronous motor (PMSM). The extended state observer (ESO) dynamically compensates for nonlinearities and time-varying parameters, while the IO algorithm optimizes ESO gain and current-loop PI parameters in real time. This dual-layer optimization enhances system robustness, anti-interference capability, and control precision. The novel fitness function, designed using dq-axis current and parameter identification, predicts and compensates for disturbances in inductance, resistance, and magnetic flux linkage, ensuring system stability under complex operating conditions. Experimental validation demonstrates that IO-FOC achieves superior performance in start-up, steady-state, and dynamic conditions compared to conventional methods, highlighting its potential for high-precision industrial applications. [ABSTRACT FROM AUTHOR]
Database: Supplemental Index
Description
Abstract:This paper proposes an advanced intelligent optimization algorithm (IO-FOC) with a novel fitness function to improve the dynamic response and steady-state performance of field-oriented control (FOC) systems for permanent magnet synchronous motor (PMSM). The extended state observer (ESO) dynamically compensates for nonlinearities and time-varying parameters, while the IO algorithm optimizes ESO gain and current-loop PI parameters in real time. This dual-layer optimization enhances system robustness, anti-interference capability, and control precision. The novel fitness function, designed using dq-axis current and parameter identification, predicts and compensates for disturbances in inductance, resistance, and magnetic flux linkage, ensuring system stability under complex operating conditions. Experimental validation demonstrates that IO-FOC achieves superior performance in start-up, steady-state, and dynamic conditions compared to conventional methods, highlighting its potential for high-precision industrial applications. [ABSTRACT FROM AUTHOR]
ISSN:11100168
DOI:10.1016/j.aej.2024.12.029