Development of flight control system with WiFi and GPS integration for aircraft and implementation on fixed-wing UAV

Unmanned Aerial Vehicles (UAVs) have gained significant importance today and are expected to remain one of the most effective technological platforms in the future. Current UAVs, through autopilot controllers, are capable of autonomous flight, safe flight control, fail-safe management, and various intelligent flight modes, all of which are provided by ready-to-use software developed by manufacturers. These ready-made autopilot systems can be utilized with limited calibration and software improvements, or through the integration of additional microcontrollers via I2C communication for more complex tasks. However, in this thesis, instead of employing an off-the-shelf autopilot system, the aim was to design an original flight control system by integrating components through a custom-developed embedded system and software. The proposed system was developed by enabling communication among existing hardware components to form a unified flight control structure, with emphasis placed on the avionics circuit design and electronic integration of the flight control unit. The designed system was tested on a real V-tail UAV prototype. The thesis consists of literature review and conceptual background, algorithm and controller principles, hardware, software, and testing sections, and concludes with results, recommendations, and references. Within the scope of the study, an avionics circuit was built, and software was developed in Python. The software integrated the Ublox Neo-6M GPS module (via TX-RX ports), the ADXL345 accelerometer, HMC5883L compass, and BMP180 barometer sensors (via I2C communication), as well as servo motor actuators connected through the GPIO pins of the Raspberry Pi 4. Test results conducted on the V-tail prototype demonstrated that GPS data could be acquired in real time, the UAV's balance could be maintained using IMU data, and both heading and altitude information could be successfully measured. Based on the sensor data, servo motors driving the control surfaces were effectively managed. Consequently, this study achieved the design and simulation of an original autopilot control system capable of meeting the fundamental functions of commercially available autopilot boards.