Self-organising BFBEL Control System for a UAV under Wind Disturbance

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 self-organising algorithm can be applied to optimise other controllers with fuzzy neural network structure. Video link: https://www.youtube.com/watch?v=nf_aFB-yBmg .