| This research work is focused on the different steps of designing, building, simulating, and testing an intelligent flight control module for an increasingly popular unmanned aerial robot vehicle (UAV), known as a quadrotor. Also an in-depth view of the modeling of the kinematics, dynamics, and control of such an interesting UAV is presented. Eventually, a quadrotor UAV test-bed is built from scratch using custom off-the-shelf components.;A helium balloon of calculated size is added to the frame of the quadrotor so that the lifting gas cancels some of the dead weight. A feasible ratio of buoyancy force and weight is found to maintain the stability of the quadrotor. A feasible size and pitch of propellers is also explored to minimize the power usage as well as providing sufficient thrust for desired maneuverability.;A bank of supercapacitors is also added to the existing power supply design. The idea is to exploit the advantages of super capacitors such as fast-charging and discharging and high power density. The proposed design of power supply provides longer operational time when compared to the conventional battery based circuits. Also the design is cost effective and improves the battery life by preventing deep discharges from the lithium-polymer battery power source.;The experimental results demonstrated a successful control performance under both inference engines. When compared to other conventional techniques applied for a similar purpose, the proposed methodology showed a higher robustness despite the induced disturbances. Also a slight improvement was observed in the overall flight-time of the quadrotor after applying the hybrid UAV design as well as the hybrid power supply unit (PSU) module to the quadrotor.;In order to achieve autonomous flight, fuzzy logic is adopted for building the flight controller of the quadrotor. It has been witnessed that fuzzy logic control may offer several advantages over certain types of conventional control methods, specifically in dealing with highly nonlinear systems and modeling uncertainties. Some of these advantages are explained and compared with other conventional control techniques. Two types of fuzzy inference engines are employed in the design of the flight controller each method of which is explained and evaluated. |
