Abstract:
Unmanned aircraft vehicles have made significant advancements in recent decades. UAVs
are utilized worldwide by both civil and military organizations for a variety of tasks.
Takeoff and landing are typically the most critical and accident-prone phases of a UAV’s
mission. Due to the time and resources required to train a remote UAV pilot, it is ex-
tremely desirable for UAVs to be capable of autonomous flying. However, many low-cost
UAV flight control systems lack robust, reliable, and precise autonomous flight capa-
bilities for fixed-wing UAVs. In addition, low-cost sensors are susceptible to noise and
malfunction. Due to size and payload constraints, standby sensors are typically not
available in UAVs. This thesis presents the design and implementation of a guidance
and control algorithm for a multi-role UAV that can perform fully autonomous missions
and can be switched to target interception missions when required. A non-linear 6DoF
model of UAV has been implemented to represent UAV dynamics. The extended Kalman
filter is also applied to enhance reliability and cater to unmeasured and noisy states of
the UAV. This work also utilizes the visualization software ’FlightGear’ to visualize
the effects of different environmental conditions on the performance of the developed
guidance and control algorithms. The proposed framework demonstrated consistent per-
formance throughout the entire flight regime of the fixed-wing UAV. To further validate
the performance of the designed algorithm, a servo-in-loop simulation was also carried
out using an Arduino Mega 2560 embedded processor and high-fidelity feedback servo
motors. Results prove the algorithm’s effectiveness in meeting requirements and assure
its implementation in real-time applications.
