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

Abstract

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 has been calculated based on the location of the detected object.Two separate fuzzy controllers are proposed to control the UAV 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, shows that the proposed technique outperforms the similar state-of-art control techniques for both mission and landing control.

A comprehensive study on the aerodynamic influence of stationary and moving obstacles on an isolated phantom DJI 3 UAV propeller

“…Drawing parallels with the work conducted by Mohammed Rabah et al. [39], who utilized CFD results to account for ground effects in their PID and Fuzzy Logic controllers, our study's findings contribute to the optimization of control methodologies when encountering obstacles such as fixed and moving walls. The outcomes obtained from our investigation, particularly regarding the impact of a moving wall on a propeller rotating at low rotational speed (3000 rpm), can now serve as instrumental data for fine‐tuning and augmenting the capabilities of various controllers.…”

“…In the context of thrust control, the CFD results derived from this study, specifically concerning the thrust increase when approaching a fixed wall or the decrease in thrust over a moving wall, offer valuable insights for refining and enhancing the control algorithm of the UAV. Drawing parallels with the work conducted by Mohammed Rabah et al [39], who utilized CFD results to account for ground effects in their PID and Fuzzy Logic controllers, our study's findings contribute to the optimization of control methodologies when encountering obstacles such as fixed and moving walls. The outcomes obtained from our investigation, particularly regarding the impact of a moving wall on a propeller rotating at low rotational speed (3000 rpm), can now serve as instrumental data for fine-tuning and augmenting the capabilities of various controllers.…”

“…This collaborative approach, leveraging CFD insights, not only aligns with established practices, as demonstrated by Mohammed Rabah et al. [39], but also extends the applicability of such methodologies to dynamic scenarios, enhancing the UAV's performance and adaptability during real‐world operations.…”

Generation of Limit Cycles in Nonlinear Systems: Machine Leaning Based Type-3 Fuzzy Control

“…Furthermore, the fuzzy logic systems have been suggested in the literature widely to analyze and identify complexities of the model for nonlinear systems. For instance, The concept of fuzzy control has been also developed by using fuzzy logic systems for systems without exact model and detailed information of uncertainties and disturbances [16], [17]. Note that there exists a significant number of papers in this field.…”

An image-based visual servoing control method for UAVs based on fuzzy logic

“…Fuzzy logic has a good effect on expressing qualitative knowledge and experience with unclear boundaries. 24,25 It uses the concept of membership function to distinguish fuzzy sets, deal with fuzzy relations, and simulate human brain to implement rule-based reasoning. Combining fuzzy logic with IBVS control, the system can adaptively select the appropriate servo gain in different states.…”