• Journal of Institute of Control, Robotics and Systems
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• v.11 no.3
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• pp.262-269
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• 2005

Automatic landing of UAVs receives increasing interest these days, with increasing number of the developed UAV systems. In this paper, a glidepath tracking algorithm of the subscale UAV was proposed and the performance was analyzed. Flight data analysis shows that the existing autolanding flight control algorithm has a classical type glidepath control. This paper presents an alternative glidepath tracking strategy based on embedded flight control law. The performance of the proposed strategy was investigated through the TDP(Touch Down Point) error analysis with regard to various flight environment: steady headwind, atmospheric disturbance, communication transfer delay. It was verified that the proposed glidepath tracking strategy can be successfully applied to the practical autolanding of UAV systems.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.5
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• pp.376-382
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• 2014

This paper presents a method to enable a UAV in autonomous flight along a desired path to follow it backwards when a strong headwind prevents the vehicle from proceeding forward. The main purpose of the reverse path following in this study is to return to a mission quickly when the wind becomes weaker. When the nonlinear path following guidance law is used, there are two reference points available in the path following. One of the two points is selected considering a flight direction for calculating a straight-line distance(L) from the vehicle to the point for the path following. An initial heading angle with respect to the wind direction determines whether the reverse path following is feasible or not at the time of the wind is generated. The result of the proposed method based on kinematic model in this study is verified through simulations implemented in Matlab.

• Advances in aircraft and spacecraft science
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• v.4 no.6
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• pp.651-677
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• 2017

For the past ten years' efforts have been made to introduce environmentally-friendly "green" electric-taxi and maneuvering airplane systems. The stated purpose of e-taxi systems is to reduce the taxiing fuel expenses, expedite pushback procedures, reduce gate congestion, reduce ground crew involvement, and reduce noise and air pollution levels at large airports. Airplane-based autonomous traction electric motors receive power from airplane's APU(s) possibly supplemented by onboard batteries. Using additional battery energy storages ads significant inert weight. Systems utilizing nose-gear traction alone are often traction-limited posing serious dispatch problems that could disrupt airport operations. Existing APU capacities are insufficient to deliver power for tractive taxiing while also providing for power off-takes. In order to perform comparative and objective analysis of taxi tractive requirements a "standard" taxiing cycle has been proposed. An analysis of reasonably expected tractive resistances has to account for steepest taxiway and runway slopes, taxiing into strong headwind, minimum required coasting speeds, and minimum acceptable acceleration requirements due to runway incursions issues. A mathematical model of tractive resistances was developed and was tested using six different production airplanes all at the maximum taxi/ramp weights. The model estimates the tractive force, energy, average and peak power requirements. It has been estimated that required maximum net tractive force should be 10% to 15% of the taxi weight for safe and expeditious airport movements. Hence, airplanes can be dispatched to move independently if the operational tractive taxi coefficient is 0.1 or higher.

• Journal of Aerospace System Engineering
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• v.13 no.2
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• pp.1-9
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• 2019

This paper elaborates on the directional axis guidance and control algorithm used in mission flight for high altitude long endurance UAV. First, the directional axis control algorithm is designed to modify the control variable such that a strong headwind prevents the UAV from moving forward. Similarly, the guidance algorithm is designed to operate the respective algorithms for Fly-over, Fly-by, and Hold for way-point flight. The design outcomes of each guidance and control algorithm were confirmed through nonlinear simulation of high altitude long endurance UAV. Finally, the penultimate purpose of this study was to perform an actual mission flight based on the design results. Consequently, flight tests were used to establish the flight controllability of the designed guidance and control algorithm.