Leveraging Data Engineering to Improve Unmanned Aerial Vehicle Control Design

Jardine, Peter T.; Givigi, Sidney N.; Yousefi, Shahram

doi:10.1109/jsyst.2020.3003203

Cited by 3 publications

(1 citation statement)

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“…Though this scheme is quite useful, it needs a predefined controller that already works and this may not be suitable for situations where a working controller is not available. Another approach is based on data engineering where the system uses flight data, machine learning and auto-tuning algorithms to pre-tune the control parameters in simulation before experimentation [14].…”

Section: A Related Workmentioning

confidence: 99%

Self-Organizing BFBEL Control System for a UAV Under Wind Disturbance

Muthusamy

Suthar

Muthusamy³

et al. 2024

IEEE Trans. Ind. Electron.

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A self-organising bidirectional fuzzy brain emotional learning (SO-BFBEL) controller is developed to control a quadcopter UAV in an uncertain environment. The proposed SO-BFBEL controller improves the performance of the existing BFBEL controller by generating more accurate fuzzy layers in real-time and removes the need to depend on expert knowledge to set the fuzzy layers. The proposed SO-BFBEL controller is applied to control the position of a quadcopter UAV for accurate 3-D eight shape trajectory tracking for three different speed settings under extreme wind disturbances up to 5m/s in real-time experimentation. Two industrial fans are used to create the wind disturbance for the experiments. The performance is compared to the DNN-MRFT based PID controller and to the BFBEL controller. The experimental results show that the proposed SO-BFBEL controller achieves robust trajectory tracking for both circle and 3D eight shaped trajectory under extreme wind disturbance and with lower computational cost. The proposed selforganising algorithm can be applied to optimise other controllers with fuzzy neural network structure. Video link: https://www.youtube.com/watch?v=nf˙aFB-yBmg.

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Section: A Related Workmentioning

confidence: 99%