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

A self-organizing 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 5 m/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 3-D eight shaped trajectory under extreme wind disturbance and with lower computational cost. The proposed self-organizing algorithm can be applied to optimize other controllers with fuzzy neural network structure. Note to Practitioners - The learning rate alpha and beta are set manually at the beginning (if no simulations are done to test) to check for the appropriate magnitude of the control signal. If the learning rates are too low, then the controller output will be too low to control the system (too slow to adapt) and if it is too high, then the system will become too sensitive or unstable (if the control signal is too high). Once the magnitude of the learning rate is achieved, then it will be trivial to adjust it further. For example, if alpha and beta values set to 0.0001 is too low and 1 is too high, then around 0.01 will give the most appropriate response. It can then be increased or decreased based on the system response. No other parameters require any tuning and they can all be set to the default values as mentioned in the article.