Fault Detection and Fault-Tolerant Control of Permanent Magnet Linear Motors Using an Emotional Learning-Based Neural Network and a Linear Extended State Observer

Alireza Nezamzadeh; Mohammadreza Esmaeilidehkordi; Hamed Habibi; Amirmehdi Yazdani; Hai Wang; Afef Fekih

doi:10.3390/en19061413

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Fault Detection and Fault-Tolerant Control of Permanent Magnet Linear Motors Using an Emotional Learning-Based Neural Network and a Linear Extended State Observer

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Fault Detection and Fault-Tolerant Control of Permanent Magnet Linear Motors Using an Emotional Learning-Based Neural Network and a Linear Extended State Observer

Alireza Nezamzadeh, Mohammadreza Esmaeilidehkordi, Hamed Habibi, Amirmehdi Yazdani, Hai Wang and Afef Fekih

Energies (Basel), Vol.19(6), 1413

2026

DOI: https://doi.org/10.3390/en19061413

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Abstract

Energy & Fuels

Science & Technology

Technology

This paper presents a unified framework for reliable motion control of permanent magnet linear motors (PMLMs) by integrating fault detection (FD) and fault-tolerant control (FTC). The framework combines a brain emotional learning-based intelligent controller (BELBIC) with a linear extended state observer (LESO) to enable rapid detection and mitigation of abrupt and incipient faults, as well as disturbances and sensor noise that degrade tracking accuracy and system reliability. The LESO is employed to estimate unknown dynamics and lumped disturbances and to generate residuals for reliable fault detection, while BELBIC provides adaptive and robust control actions without requiring prior knowledge of system parameters or explicit fault models. Extensive simulation studies under actuator faults, system dynamics faults, external disturbances, and measurement noise are conducted. Comparative evaluations with benchmark approaches demonstrate improved fault detection speed, tracking accuracy, and robustness of the proposed framework, highlighting its potential for enhancing reliability and operational continuity in high-precision industrial applications.

Details

Title: Fault Detection and Fault-Tolerant Control of Permanent Magnet Linear Motors Using an Emotional Learning-Based Neural Network and a Linear Extended State Observer
Authors/Creators: Alireza Nezamzadeh - Isfahan University of Technology
Mohammadreza Esmaeilidehkordi - Isfahan University of Technology
Hamed Habibi - University of Luxembourg
Amirmehdi Yazdani - Murdoch University, School of Engineering and Energy
Hai Wang - Murdoch University, School of Engineering and Energy
Afef Fekih - University of Louisiana at Lafayette
Publication Details: Energies (Basel), Vol.19(6), 1413
Publisher: MDPI
Number of pages: 28
Identifiers: 991005876850307891
Murdoch Affiliation: School of Engineering and Energy; Harry Butler Institute
Language: English
Resource Type: Journal article

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