This research deals with the autonomous landing maneuver of a quadrotor unmanned aerial vehicle (UAV) on an unmanned ground vehicle (UGV). It is assumed that the UGV moves independently, and there is no communication and collaboration between the two vehicles. This paper aims at the design of a closed-loop vision-based control system for quadrotor UAV to perform autonomous landing maneuvers in the possible minimum time despite the wind-induced disturbance force. In this way, a fractional-order fuzzy proportional-integral-derivative controller is introduced for the nonlinear under-actuated system of a quadrotor. Also, a feedback linearization term is included in the control law to compensate model nonlinearities. A supervisory control algorithm is proposed as an autonomous landing path generator to perform fast, smooth, and accurate landings. On the other hand, a compound AprilTag fiducial marker is employed as the target of a vision positioning system, enabling high precision relative positioning in the range between 10 and 350 cm height. A software-in-the-loop simulation testbed is realized on the windows platform. Numerical simulations with the proposed control system are carried out, while the quadrotor system is exposed to different disturbance conditions and actuator dynamics with saturated thrust output are considered.
rehabilitation robots should be able to adapt with physical characteristics of patient body in order to provide a safe and comfortable interaction. In this paper, an elbow rehabilitation robot with novel cable-based series elastic actuator is proposed. The cable actuation mechanism provides two parameters to adjust actuation torque and joint stiffness. An independent position-stiffness control algorithm is designed to independently control position and stiffness through the cablebased series elastic actuator. Finally, a simulation-based examination is used to illustrate ability of the elbow rehabilitation robot in providing appropriate elbow stiffness during the flexion and the extension movements.
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