SYMar 1, 2018
Terminal Iterative Learning Control for Autonomous Aerial Refueling under Aerodynamic DisturbancesXunhua Dai, Quan Quan, Jinrui Ren et al.
This paper studies the model of the probe-drogue aerial refueling system under aerodynamic disturbances, and proposes a docking control method based on terminal iterative learning control to compensate for the docking errors caused by aerodynamic disturbances. The designed controller works as an additional unit for the trajectory generation function of the original autopilot system. Simulations based on our previously published simulation environment show that the proposed control method has a fast learning speed to achieve a successful docking control under aerodynamic disturbances including the bow wave effect.
ROMay 24, 2017
A Control Performance Index for Multicopters Under Off-nominal ConditionsGuang-Xun Du, Quan Quan, Zhiyu Xi et al.
In order to prevent loss of control (LOC) accidents,the real-time control performance monitoring problem is studied for multicopters. Different from the existing work, this paper does not try to monitor the performance of the controllers directly. In turn, the disturbances of multicopters under off-nominal conditions are estimated to affect a proposed index to tell the user whether the multicopter will be LOC or not. Firstly, a new degree of controllability (DoC) will be proposed for multicopters subject to control constrains and off-nominal conditions. Then a control performance index (CPI) is defined based on the new DoC to reflect the control performance for multicopters. Besides, the proposed CPI is applied to a new switching control framework to guide the control decision of multicopter under off-nominal conditions. Finally, simulation and experimental results show the effectiveness of the CPI and the proposed switching control framework.