Fully-actuated multi-rotor aerial platforms are receiving increasing research interests for the capability of six degree-of-freedom (DOF) motions such as hovering at non-horizontal attitude angles. Existing fully-actuated aerial vehicles have demonstrated such capability for a limited range of angles and limited thrust efficiencies. This thesis presents an over-actuated aerial platform that achieves maneuvering at arbitrary attitudes with uniformly high thrust efficiency over its achievable configuration space. A novel vectoring thrust force actuator by mounting a regular quad copter on a passive mechanical gimbal mechanism is proposed. The UAV platform achieves full six DOF motion with redundancies from four of these vectoring thrust actuators. We present the hierarchical controller that generates the high level virtual wrench command allocated to each gimbal actuator and the low-level actuator control to track the commanded wrench. And we demonstrate the UAV platform's 6 DOF maneuvers by both simulations and real-world experiments on a prototype we built. Aerodynamic effects largely affect the performance of aerial vehicles, especially on over-actuated vehicles that could be subject to different airflow configurations. For those aerodynamic effects that are difficult to model but could appear repeatedly, iterative learning control (ILC) has great potential to improve the system performance. This thesis presents the applications of both model-based and data-driven ILC algorithms on the over-actuated aerial platform and shows great improvements against the aerodynamics effects. A formulation is demonstrated to convert the closed-loop dynamics of the over-actuated aerial platform to linear model with six independent control channels. Model-based and data-driven ILC are applied on one or more control channels, and by simulations and real-world experiments, the ILC algorithms are shown to have great improvement and fast convergence rate against a variety of aerodynamic effects.