Quad-Copter Design and Fabrication by using Neural Network idea based on Advanced Microprocessor


The current work aims to design and implement an independent quadcopter for locating a particular location and landing on a station of the required target. Where an outdoor quadcopter is designed and its flight is done by automatic flight. The quadcopter requires a wide control system for flight. Operation and tuning processes of the system become very difficult with existence of many parameters. Therefore, PID controller is optimized by the Invasive Weed Optimization (IWO) algorithm that is used to make quadcopter more stable, in addition to the sensors that help in achieving the stability and equilibrium for the quadcopter.In the present work, movements of the quadcopter that are named as roll, pitch and yaw are controlled by three PID controllers designed for this purpose. Here, the LattePanda controller board, USB camera that is connected with the board of the quadcopter and Neo-M8N GPS are used to locate the target and monitor the (X) mark during the automated landing of the quadcopter. Matlab 2014b program is installed inside the microprocessor LattePanda which can detect the object (Mark X) by using Deep Learning Algorithm. The control unit (PID) was easy to implement the simulation system by using the Matlab 2014b and required a short execution time during the simulation.