An AI-in-Loop Fuzzy-Control Technique for UAV's Stabilization and Landing

In this paper, an adaptable fuzzy control mechanism for an Unmanned Aerial Vehicle (UAV) to manipulate its mechanical actuators is provided. The mission (landing) for the UAV is defined to track (land on) an object that is detected by a deep learning object detection algorithm. The inputs of the controller are the location and speed of the UAV that have been calculated based on the location of the detected object. Two separate fuzzy controllers are proposed to control the UAV's motor throttle and its roll and pitch over the mission and landing time. Fuzzy logic controller (FLC) is an intelligent controller that can be used to compensate for the non-linearity behaviour of the UAV by designing a specific fuzzy rule base. These rules will be utilized to adjust the control parameters during the mission and landing period in runtime. To add the effect of the ground for tuning the FLC membership function over the landing operation, a computational flow dynamic (CFD) modeling has been investigated. The proposed techniques is evaluated on MATLAB/Simulink simulation platform and real environment. Statistical analysis of the UAV location reported during stabilization and landing process, on both simulation and real platform, show that the proposed technique outperforms the similar state-of-art control techniques for both mission and landing control.