Stability control of brushless dc motor driven dual rotor unmanned aerial vehicle with adaptive fuzzy 2-DOF PID controller

In this thesis, the balance control of an unmanned aerial vehicle driven by two brushless direct current motors was realized. First, the model of the balance system was created in the Matlab/Simulink environment. The created model was first run with PID and 2-DOF PID controller. Particle Swarm Optimization (PSO) algorithm, which has recently been widely used in optimization applications, was used to determine the controller parameters used in the study. Then, the adaptive fuzzy 2-DOF PID controller structure proposed in this study was implemented for both balance angle and motor speed control. The performance of PID, 2-DOF PID and Adaptive Fuzzy 2-DOF PID controllers were analyzed comparatively using the results of the Co-Simulation by creating a Co-Simulation structure for the UAV Balance System using STM32F4 microprocessor and Matlab/Simulink. When the results were analyzed, it was seen that the classical PID, 2-DOF PID and Adaptive Fuzzy 2-DOF PID controllers controlled the UAV Balance System stably. However, the Adaptive Fuzzy 2-DOF PID proposed in this study shows a better performance compared to the other controllers used in the study, especially by showing adaptive characteristics under variable operating conditions.