Browsing by Author "Nigatu Wanore"

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    Trajectory Tracking Control of ISR Quadrotor UAV using GA Optimized Fuzzy-PID Based Neural Network Controller
    (Addis Ababa University, 2023-10) Nigatu Wanore; Lebsework Negash(PhD)
    The ISR Quad-Rotor is an unmanned aerial vehicle (UAV) utilized for intelligence gathering, surveillance, and reconnaissance missions. This thesis focuses on modeling and controlling the quad-rotor to identify and track a Person of interest using an intelligent control technique. The initial step involves deriving a nonlinear mathematical model for the 6DOF (six degrees of freedom) Quadrotor UAV using the Newton-Euler formalism. To ensure trajectory tracking of the quad-rotor, a Fuzzy Proportional-Integral-Derivative (Fuzzy-PID) controller, tuned with a Genetic Algorithm (GA), was implemented to generate data for a neural network (NNFPID-GA). The fuzzy logic approach facilitates parameter adjustment based on predefined fuzzy rules, while the GA algorithm determines the scaling factors of the Fuzzy-PID controller. The proposed control system was designed based on input-output data from the GA Optimized Fuzzy-PID controller. A network was trained using the Levenberg-Marquardt backpropagation algorithm with the assistance of the MATLAB®NN Toolbox. MATLAB®simulations were conducted to validate the effectiveness of the proposed control algorithm. Additionally, a flight test were performed using the UAV Toolbox to assess the stable flight performance of a developed GA Optimized Fuzzy-PID based neural network(NNFPID-GA) controller for the Quadrotor UAV. To evaluate the performance of both controllers, a comparison study based on performance metrics was conducted. Even though both controllers offer faster response in terms of settling time and improved performance, with less overshoot and better robustness in handling parameter variation and disturbance rejection capability, the GA Optimized Fuzzy-PID based neural network controller(NNFPID-GA) outperforms the GA Optimized Fuzzy-PID controller(FPID-GA). Finally, in order to generate trajectories for the controller, a face recognition system using Python were implemented. The training results demonstrate an accuracy of 98.65%, indicating that the system can effectively distinguish between a wanted person (Person of interest) and other individuals.