UAV Brushless DC motor Speed Control via Adaptive Neuro Fuzzy Inference Systems (ANFIS) and Self-Adaptive PID

Sarhan, Amr; Hafez, Ahmed T.

doi:10.2514/6.2019-1563

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…The article's main point is that sprayer drones make the air relevant for agricultural activities and processes in unique, fundamentally valuable, and commercially viable ways. The authors presented in [1], [3], and [17] that the impressive performance of unmanned aerial vehicles (UAV) in various commercial and military applications subjected to rotor control of the motor. The UAV's rotors are necessary to ensure stability and safe flight while carrying out its desired load.…”

Section: Literature Surveymentioning

confidence: 99%

“…The recent technology has extended aerial vehicle time as much as feasible through drone lightning and effective BLDC motor control. Therefore, there is a need to develop associated technologies if a drone cracks, leading to motor destruction that results in significant financial losses [2], [3]. Additionally, research is being conducted to enhance the drone's size to carry significant mission weights such as courier and pesticide spraying.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance evaluation of BLDC motor drive mounted in aerial vehicle (drone) using adaptive neuro-fuzzy

Rao,

Prasanna,

Rayudu

et al. 2024

IJPEDS

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The development of autonomous drones equipped with cameras and various sensors has paved the way for their application in agriculture and perimeter security. These aerial drones require specific power, acceleration, high torque, and efficiency to meet the demands of agricultural tasks, utilizing built-in brushless DC (BLDC) motors. However, a common challenge drone’s face is maintaining the desired speed for extended periods. Enhancing the performance of BLDC motors through advanced controllers is crucial to address this issue. This research paper proposes optimizing the size and speed of brushless DC motors for aerial vehicles using an adaptive fuzzy inference system and supervised learning techniques. When these drones carry loads, the BLDC motors must dynamically adjust the drone's speed. During this phase, the motors must control their speed and torque using artificial intelligence controllers like adaptive neuro-fuzzy inference systems (ANFIS) to enhance the drone's functionality, resilience, and safety. This research has conducted analyses focused on improving the performance of BLDC motors explicitly personalized for unmanned aerial vehicle (UAVs). The proposed method will be implemented using MATLAB/Simulink, expecting to significantly enhance the BLDC motor's performance compared to conventional controllers. Comparative analyses will be conducted between traditional and ANFIS controllers to validate the effectiveness of the proposed approach.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance evaluation of BLDC motor drive mounted in aerial vehicle (drone) using adaptive neuro-fuzzy

Rao,

Prasanna,

Rayudu

et al. 2024

IJPEDS

View full text Add to dashboard Cite

show abstract

“…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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MRAS disturbance observer-based sensorless field-oriented backstepping control of BLDC motor drive

Joshi,

Deb,

Giri

2024

Electr Eng

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UAV Brushless DC motor Speed Control via Adaptive Neuro Fuzzy Inference Systems (ANFIS) and Self-Adaptive PID

Cited by 8 publications

References 22 publications

Performance evaluation of BLDC motor drive mounted in aerial vehicle (drone) using adaptive neuro-fuzzy

Performance evaluation of BLDC motor drive mounted in aerial vehicle (drone) using adaptive neuro-fuzzy

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

MRAS disturbance observer-based sensorless field-oriented backstepping control of BLDC motor drive

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