Permanent Magnet Synchronous Motor PMSM Control by Combining Vector and PI Controller

Achour, Hafid Ben; Ziani, Said; Youssef, Chaou; Hassouani, Y. El; Daoudia, Abdelkarim

doi:10.37394/23203.2022.17.28

Cited by 27 publications

(10 citation statements)

References 8 publications

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“…This paper emphasizes the indisputable benefits of wavelets in biomedical engineering. This mathematical tool is also referenced in contexts related to energy [17] and motor control [18,19] for battery-powered cars. Finally, yet importantly, the suggested technique may be implemented and optimized with the help of AI, in particular the Convolutional Neural Network algorithm, for describing the time scales picture of FECG and MECG [20].…”

Section: Results Of Simulationsmentioning

confidence: 99%

Contribution to Single-Channel Fetal Electrocardiogram Identification

Ziani¹

2022

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This paper presents the recognition of fetal ECG signals from a single-channel recording. The Continuous Wavelet Transform generates a two-dimensional representation of a single-dimension input containing fetal electrocardiogram waves, which are accurately located and isolated. By segmenting this 2D representation using Otsu's method, the continuous wavelet transform of each wave may be estimated. The temporal expression of the wave is then rebuilt by calculating the Inverse Continuous Wavelet Transform. The continuity between each sample allows us to follow the waves from one image to the next. Consequently, the complete profile can be automatically processed.

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Section: Results Of Simulationsmentioning

confidence: 99%

Contribution to Single-Channel Fetal Electrocardiogram Identification

Ziani¹

2022

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“…The undeniable value of wavelets in biomedical engineering is highlighted in this work. Additionally, it is mentioned that this mathematical tool can be applied to energy [37][38][39] and motor control [40,41] for electric vehicles.…”

Section: Discussionmentioning

confidence: 99%

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

Ziani

Farhaoui

Moutaib

2023

Big Data Min. Anal.

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This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and a dimension-reduction technique like Nonnegative Matrix Factorization (NMF). Due to the highly disproportionate frequency of the fetus's heart rate compared to the mother's, the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy. Furthermore, we can disentangle the various components of fetal ECG, which serve as inputs to the CNN model to optimize the actual FECG signal, denoted by FECGr, which is recovered using the SVD-ICA process. The findings demonstrate the efficiency of this innovative approach, which may be deployed in real-time.

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“…where 𝜌 is the screw pitch of the screw mechanism. Meanwhile, the output force of the screw mechanism, called F BS , can be computed as in (14):…”

Section: Screw-nut Mechanismmentioning

confidence: 99%

“…Permanent magnet synchronous motors (PMSMs) are a common motor type utilized in EMLAs due to their numerous benefits, including high power and torque density, allowing them to generate substantial force and power relative to their size. 13,14 In addition, PMSMs are highly efficient at converting electrical power into mechanical power, making them suitable for energy-efficient applications, such as mobile manipulators, whose batteries can store only limited energy. 15,16 EMLAs are considered a multidisciplinary challenge, whose interactions spark intricate dynamics that are difficult to model for control design.…”

Section: Introductionmentioning

confidence: 99%

Robust decomposed system control for an electro‐mechanical linear actuator mechanism under input constraints

Heydari Shahna,

Bahari,

Mattila

2024

Intl J Robust & Nonlinear

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This paper aims to develop a robust decomposed system control (RDSC) strategy under input constraints for an electro‐mechanical linear actuator (EMLA) facing model uncertainty and external disturbances. At first, a state‐space model of a complex multi‐stage gearbox EMLA system, driven by a permanent magnet synchronous motor (PMSM), is developed, and the non‐ideal characteristics of the ball screw are presented through the model. The result is a four‐order nonlinear strict‐feedback form (NSFF) system decomposed into three subsystems. As the paper's main result, a novel RDSC strategy with uniform exponential stability for controlling subsystem states is presented. This developed controller avoids the "explosion of complexity" problem associated with backstepping by treating the time derivative of the virtual control input as an uncertain system term. The proposed method, despite assuming load disturbances and input constraints with arbitrary bounds, offers a straightforward control approach for a broader range of applications. Further, the controller's performance is evaluated by simulating two distinct duty cycles, each representing different levels of demand on the actuator facing load disturbances near the rated motor performance.

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Permanent Magnet Synchronous Motor PMSM Control by Combining Vector and PI Controller

Cited by 27 publications

References 8 publications

Contribution to Single-Channel Fetal Electrocardiogram Identification

Contribution to Single-Channel Fetal Electrocardiogram Identification

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

Robust decomposed system control for an electro‐mechanical linear actuator mechanism under input constraints

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