Sensorless Control of Brushless DC Motor Based on Wavelet Theory

Yılmaz, Murat; Tuncay, R. Nejat; Üstün, Özgür; Krein, T. Philip

doi:10.1080/15325000902953377

Cited by 11 publications

(7 citation statements)

References 20 publications

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“…The BLDC operates with a rectangular current and a trapezoidal air-gap flux so that it requires a low-cost Hall sensor for commutation. Numerous sensorless techniques have been developed for both BLDC and BLAC machines [61]. Constant-power operation can be offered by using advanced conduction angle control for the BLDC and using flux-weakening control for the BLAC.…”

Section: Pm Machinesmentioning

confidence: 99%

Limitations/capabilities of electric machine technologies and modeling approaches for electric motor design and analysis in plug-in electric vehicle applications

Yılmaz

2015

Renewable and Sustainable Energy Reviews

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Section: Pm Machinesmentioning

confidence: 99%

Limitations/capabilities of electric machine technologies and modeling approaches for electric motor design and analysis in plug-in electric vehicle applications

Yılmaz

2015

Renewable and Sustainable Energy Reviews

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“…In the field of motor control, sensorless control has been applied for a long time [18][19][20]. Recently, intelligent motor control using an RNN has been studied [21,22]. Further research is underway to replace the difficult and expensive sensors installed in the field of wind and solar energy, which are new and renewable energy sources, by applying a prediction method using DNN.…”

Section: Related Workmentioning

confidence: 99%

Sensorless Air Flow Control in an HVAC System through Deep Learning

Son

Kim

2019

Applied Sciences

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Sensor-based intelligence is essential in future smart buildings, but the benefits of increasing the number of sensors come at a cost. First, purchasing the sensors themselves can incur non-negligible costs. Second, since the sensors need to be physically connected and integrated into the heating, ventilation, and air conditioning (HVAC) system, the complexity and the operating cost of the system are increased. Third, sensors require maintenance at additional costs. Therefore, we need to pursue the appropriate technology (AT) in terms of the number of sensors used. In the ideal scenario, we can remove excessive sensors and yet achieve the intelligence that is required to operate the HVAC system. In this paper, we propose a method to replace the static pressure sensor that is essential for the operation of the HVAC system through the deep neural network (DNN).

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“…Studies show that 3-phase conduction mode has a better torque-speed capability than the first method in the constant power region [1]. Various advance speed controllers and advance converters [2][3][4][5], either with a sensor or sensorless [6][7][8][9], have been introduced by researchers to date. The first strategy is called the hysteresis method and works based on the relationship between machine torque and current.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies have covered this problem by proposing different solutions. The number of sensors can be reduced only by measuring the DC link current [11] or using the wavelet theory [7]. Current control strategies also employ hysteresis modulation.…”

Section: Introductionmentioning

confidence: 99%

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Simplified model predictive current control of non-sinusoidal low power brushless DC machines

Eshkevari¹,

Torkaman²

2020

Turk J Elec Eng & Comp Sci

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Several strategies have been proposed to control nonsinusoidal brushless DC machines (BLDCMs). However, high electromagnetic torque ripple and current overshoots occur in commutation times, which are significant problems of those strategies such as for hysteresis current controllers. This paper proposes a model predictive strategy to solve the above issues. It is simple and straightforward. Moreover, it reduces the motor torque ripple significantly and improves the response rate of the control system to the load torque variation in comparison with the conventional technique. The torque varies smoothly, and the performance of the system at commutation time is improved by eliminating the adverse effects of commutation times on the machine's current and torque. This method also operates better than the conventional controller in medium switching frequencies. The novelty of this strategy is that it employs a model predictive strategy to realize the above claims. The real implementation possibility and performance of the controller are investigated by simulations for a 60-V/180-W/300-RPM BLDCM. This paper also compares the proposed current controller with the conventional controller. The results show that the torque ripple reduces 50%, and the brake and response times are improved.

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Sensorless Control of Brushless DC Motor Based on Wavelet Theory

Cited by 11 publications

References 20 publications

Limitations/capabilities of electric machine technologies and modeling approaches for electric motor design and analysis in plug-in electric vehicle applications

Limitations/capabilities of electric machine technologies and modeling approaches for electric motor design and analysis in plug-in electric vehicle applications

Sensorless Air Flow Control in an HVAC System through Deep Learning

Simplified model predictive current control of non-sinusoidal low power brushless DC machines

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