A Comparative Study on Using Brushless DC Motor Six-Switch and Four-Switch Inverter for UAV Propulsion System

Elkerdany, Mohamed Sameh; Safwat, Ibrahim M.; Yossef, Ahmed Medhat Mohamed; El-Khatib, Mohamed

doi:10.1109/iceeng45378.2020.9171757

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…The mechanical steering system is the most energy-consuming unit of tracking system because it needs energy to drive the motors and overcome the torque [ 16 ].The energy loss is seen in the form of heat energy which are dissipated in the metallic enclosures. There are several types of motors in use for the UAV tracking system, namely (i) stepper motors [ 17 , 18 ]; (ii) DC motors [ 19 , 20 ]; (iii) geared DC motors [ 21 , 22 ]; (iv) brushless DC (BLDC) motors [ 23 , 24 ]; and (v) brushless AC (BLAC) motors [ 25 ]. It has been reported that the steeping motor is the most effective in designing steering antenna [ 26 ].…”

Section: Uav Tracking System Overviewmentioning

confidence: 99%

The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review

Kelechi¹,

Alsharif

Oluwole³

et al. 2021

Sensors

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Unmanned aerial vehicle (UAV) antenna tracking system is an electromechanical component designed to track and steer the signal beams from the ground control station (GCS) to the airborne platform for optimum signal alignment. In a tracking system, an antenna continuously tracks a moving target and records their position. A UAV tracking antenna system is susceptible to signal loss if omnidirectional antenna is deployed as the preferred design. Therefore, to achieve longer UAV distance communication, there is a need for directional high gain antenna. From design principle, directional antennas are known to focus their signal energy in a particular direction viewed from their radiation pattern which is concentrated in a particular azimuth direction. Unfortunately, a directional antenna is limited by angle, thus, it must always be directed to the target. The other limitation of a UAV mechanical beam steering system is that the system is expensive to maintain and with low reliability. To solve this problem, we are proposing the use of MIMO technology as a readily available technology for UAV beyond line of sight technology. Although UAV antenna tracking is domiciled in the mechanical beam steering arrangement, this study shows that this native technology could be usurped by MIMO beam forming.

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Section: Uav Tracking System Overviewmentioning

confidence: 99%

The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review

Kelechi¹,

Alsharif

Oluwole³

et al. 2021

Sensors

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“…DC motors [5]- [7] have become indispensable in our daily lives due to their wide-ranging applications and numerous benefits. These motors find utility in diverse areas, including household appliances (blenders [8], [9] and fans [10], [11]), personal transportation (electric motorcycle [12], [13], electric cars [4], [14], [15], hoverboards [16]- [18]), public transportation (electric bus [19]- [21], electric train [22]), and even robotics (drones [23]- [26], UAVs [27]- [30]).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Speed Estimation in DC Motors using the Kalman Filter Method: A Comprehensive Analysis

Setiawan,

Ma'arif,

Rekik

et al. 2024

J. Ilm. Tek. Elektro Komput. Dan Inform

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The accurate estimation of speed is crucial for optimizing the performance and efficiency of DC motors, which find extensive applications in various domains. However, the presence of noise ripple, caused by interactions with magnetic or electromagnetic fields, poses challenges to speed estimation accuracy. In this article, we propose the implementation of the Kalman Filter method as a promising solution to address these challenges. The Kalman Filter is a recursive mathematical algorithm that combines measurements from multiple sources to estimate system states with improved accuracy. By employing the Kalman Filter, it becomes possible to estimate the true speed of DC motors while effectively reducing the adverse effects of noise ripple. This research focuses on determining the optimal values for the Kalman Filter parameters and conducting experiments on a DC motor to evaluate the performance of the proposed approach. The experimental results demonstrate that the Kalman Filter significantly improves the control of speed oscillations and enhances the stability of the DC motor system. Furthermore, a comprehensive analysis of the system's response and parameter tuning reveals the impact of different parameter combinations on settling time, overshoot, and rise time. By carefully selecting appropriate parameters, the proposed approach contributes to accurate speed estimation and effective control of DC motors, advancing the understanding and application of the Kalman Filter in various relevant fields. Overall, this research provides valuable insights into enhancing the performance and efficiency of DC motors through the integration of the Kalman Filter method.

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