Study of SMO Parameter Optimization Based on PMSM Finite Element Simulation

Wu, Faqi; Shao, Dingguo

doi:10.1109/imcec46724.2019.8983988

Cited by 9 publications

(1 citation statement)

References 4 publications

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“…In [7], proposed a novel signal processing method using a second order generalized integrator (SOGI) to process the back EMF signal, which can eliminate the filtering delay and reduce the harmonic interference. In [8], proposed a SMO parameter optimization method based on finite element simulation and genetic algorithm, which can well suppress the chattering phenomenon of SMO control and improve the accuracy of the observer, but the method will increase the computational complexity, and it cannot be well solved for harmonic interference.…”

Section: Introductionmentioning

confidence: 99%

Adaptive filter based position and speed estimation for high-speed PMSM control

Zhang

Ren

2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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An adaptive filtering strategy is proposed to address the impact of high-frequency chattering, filtering delay and harmonic interference on the estimation accuracy of the conventional sliding mode observer based rotor angular frequency estimation strategy for permanent magnet synchronous motors. At the same time, to solve the problem of narrow passband of the adaptive filter, a frequency-locked loop is introduced to reduce the frequency tracking error, and a quadrature phase-locked loop is introduced to superimpose the fundamental frequency speed on the angular frequency estimation to improve its tracking performance. Finally, the strategy is verified through experiments that the strategy has better dynamic and steady-state performance.

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Section: Introductionmentioning

confidence: 99%

Adaptive filter based position and speed estimation for high-speed PMSM control

Zhang

Ren

2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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An IoT Based diabetic patient Monitoring System Using Machine Learning and Node MCU

Rghioui

Naja

Mauri

et al. 2021

J. Phys.: Conf. Ser.

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Diabetic patient monitoring is a systematic method that provides us with detailed information about the diabetic patient. Diabetic patient monitoring systems play a significant role in monitoring the patient's health, especially with the use of Internet of Things (IoT) devices. Diabetic patient monitoring systems are able basically to monitor diabetic patients and save some data about blood glucose level, body temperature, and location. The role of this system is not limited to patients monitoring, it can also classify data using machine learning techniques. Predictive analytic for diabetic patients is very important due to its ability to help diabetic patients, their families, doctors, and medical researchers to make decisions on diabetic patient treatment based on big data. This paper describes a new system for monitoring diabetic patients and discusses predictive analytics using four different machine-learning algorithms. The performance and accuracy of the applied algorithms are discussed and compared to choose the best one in terms of several parameters.

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