Visionbased Fractional Order Sliding Mode Control for Autonomous Vehicle Tracking by a Quadrotor UAV This paper proposes a visionbased sliding mode control technique for autonomous tracking of a moving vehicle by a quadrotor. The proposed vision algorithm estimates the quadrotorxe2x80x99s position relative to moving vehicle using an onboard monocular camera. The relative position is provided as an input to a Fractional Order Sliding mode Controller (FOSMC) which ensures the convergence of the relative position between the moving vehicle and the quadrotor thereby enabling it to track the vehicle effectively. In addition, the proposed controller guarantees robustness towards bounded external disturbances and modelling uncertainties. The proposed visionbased control scheme is implemented using numerical simulations and validated in realtime on the DJI Matrice 100. Theses validations help in gaining into the maximum allowable speed of the moving target for the quadrotor to successfully track the object. This plays a vital role in surveillance operations and intruder chase.

Modeling and Control of an Unmanned Aerial Vehicle with Tilt Rotors Equipped with a Camera In this work, we present a control law for an unmanned aerial vehicle (UAV) carrying a camera through a two degrees of freedom (2DOF) gimbal mechanism. The vehicle is an H shaped quadrotor with an additional mechanism that allows the independent projection, along the longitudinal direction, of the thrust generated by each rotor. This particular characteristic shows useful during the execution of some specific tasks, especially the ones that require high velocity. A control law is designed to deal with the underactuation and nonlinearities behavior of the vehicle, that are expressed mathematically by the EulerLagrange formulation. The control structure is arranged in four subsystems, designed to control the dynamics of the UAV position, attitude, rotors inclination, and camera pointing. For the position control, the inverse dynamic is used to linearize the translation subsystem, and then a robust Hxe2x88x9e controller is applied. For the remaining subsystems, the SuperTwisting Sliding Mode Control is applied. We present, through simulation, the performance of the designed control structure, proving that the controlled UAV is stabilized and able to track given trajectories even in the presence of unmodeled dynamics, parametric uncertainties and external disturbances (such as wind gusts).