Performance Evaluation of BLDC Motor Drive in Drone Using Artificial Intelligence

The drones are designed and developed with cameras and numerous sensors having a concept of autonomy. They will engage in agriculture and perimeter security activities. The aerial drones must have the desired power, acceleration, high torque, and high efficiency to full the requirement of agricultur...

Full description

Saved in:
Bibliographic Details
Published in2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) pp. 1 - 6
Main Authors Sheshadri, G, Rao, G Madhusudhana, Kumar, A Ananda, Prasad, S.Siva, Jareena, Sunkesula, Sriram, Cholleti
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.11.2023
Subjects
Adaptive systems
Agriculture
ANFIS Controllers
BLDC Motor
Brushless DC motors
Drones
Performance Analysis
Propellers
Simulation
Speed control
Speed-Torque characteristics
Stability analysis
Supervised learning
Torque
UAV
Online AccessGet full text

Cover

More Information
Summary:The drones are designed and developed with cameras and numerous sensors having a concept of autonomy. They will engage in agriculture and perimeter security activities. The aerial drones must have the desired power, acceleration, high torque, and high efficiency to full the requirement of agricultural applications, which are inbuilt with BLDC power. In practice, drones will face a problem in flying at desired speed for a long time. In this issue, it is better to improve the performance of the BLDC Motor with the advanced controllers. This paper proposes that the brushless DC motor's size and speed are optimized for aerial vehicles with an adaptive fuzzy inference system and a supervised learning technique. When they carry the load, the BLDCM will adjust the drone's speed in the dynamic stage. During this stage, the BLDCM must control its speed and torque with artificial intelligence controllers, like adaptive neuro-fuzzy inference systems (ANFIS), to enhance the UAV's functionality, resilience, and safety. However, analyses have been conducted to boost the performance of BLDCM, which is inbuilt into the UAV. This artificial controller will evaluate and improve the BLDC motor's performance and efficiency. Finally, this proposed method will be implemented in MATLAB/Simulink, and it has been expected that the performance of BLDCM will be improved with better results. The comparison may be done with the conventional controllers and ANFIS controllers.
DOI:10.1109/CSITSS60515.2023.10334068