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Intelligent Controller Design for Quad-Rotor Stabilization in Presence of Parameter Variations
Doukhi, Oualid,Fayjie, Abdur Razzaq,Lee, Deok Jin Hindawi Limited 2017 Journal of advanced transportation Vol.2017 No.-
<P>The paper presents the mathematical model of a quadrotor unmanned aerial vehicle (UAV) and the design of robust Self-Tuning PID controller based on fuzzy logic, which offers several advantages over certain types of conventional control methods, specifically in dealing with highly nonlinear systems and parameter uncertainty. The proposed controller is applied to the inner and outer loop for heading and position trajectory tracking control to handle the external disturbances caused by the variation in the payload weight during the flight period. The results of the numerical simulation using gazebo physics engine simulator and real-time experiment using AR drone 2.0 test bed demonstrate the effectiveness of this intelligent control strategy which can improve the robustness of the whole system and achieve accurate trajectory tracking control, comparing it with the conventional proportional integral derivative (PID).</P>
Real-Time Deep Learning for Moving Target Detection and Tracking Using Unmanned Aerial Vehicle
Oualid Doukhi,Sabir Hossain,Deok-Jin Lee(이덕진) 제어로봇시스템학회 2020 제어·로봇·시스템학회 논문지 Vol.26 No.5
Real-time object detection and tracking are crucial for many applications such as observation and surveillance, and search-and-rescue. There have been many advancements in deep learning techniques for object detection and tracking due to the successful development of computing devices. Based on these ideas, the YOLO deep learning visual object detection algorithm was utilized to visually guide the UAV to track the detected target. The detected target bounding box and the image frame center were the main parameters that were used to control the forward motion, heading, and altitude of the vehicle. The proposed control system approach consisted of two PID controllers that managed the heading and altitude rates. For a real-time computing device a Nvidia Jetson TX2 based edge-computing module is used, which takes the input data from onboard sensors such as camera. A navigation system operated entirely onboard the UAV in the absence of external localization sensors or a GPS signal is introduced, and it used a fisheye camera to perform a visual SLAM for localization. The robustness and effectiveness of the proposed deep-learning based target detection and tracking algorithms were verified through various simulation and real-time flight experiments.
Oualid Doukhi,이덕진 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.9
In this paper, a robust adaptive neural network certainty equivalent controller for a quadrotor unmanned aerial vehicle is proposed, which is applied in the outer loop for position control to directly generate the desired roll and pitch angles commands and then to the inner loop for attitude control. The newly proposed controller takes into account the vehicle’s kinematic and modelling error uncertainties which are associated with external disturbances, inertia, mass, and nonlinear aerodynamic forces and moments. The control method integrates an adaptive radial basis function neural networks to approximate the unknown nonlinear dynamics with certainty equivalent control technique, in this way leading to the fact that precise dynamic model and prior information of disturbances are not needed. The adaptation law was derived by using a Lyapunov theory to verify the stability and superiority of the new algorithms. The performance and effectiveness are also verified by carrying out several simulations. It was shown from the analysis that the altitude, position, and attitude tracking errors are converged to zero and the closed loop stability is guaranteed under extreme conditions.