Autolanding Strategies for a Fixed Wing UAV in Adverse Atmospheric Conditions

Yavrucuk, Ilkay; Kargin, Volkan

doi:10.2514/6.2008-6963

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…In Ref. 12, three lateral controllers as well as the crabbing and decrabbing maneuvers based on PID control are designed to deal with the crosswind, tail wind, and turbulence. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Anti-crosswind Autolanding of UAVs based on Active Disturbance Rejection Control

Xiong

Fan

et al. 2010

AIAA Guidance, Navigation, and Control Conference

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The safe landing of unmanned aerial vehicles (UAVs) under various wind conditions has been a challenging task for decades. As the first step of landing, the longitudinal landing control has been well researched. But the lateral landing control, which is the second step of landing, is still facing a lot of challenges especially in crosswind conditions. In this paper, a UAV with wide span is researched. Due to its wide span, its wing tips may possibly touch the ground if the wings are not level at touchdown. An autolanding control scheme which consists of a longitudinal autolanding control system and a lateral autolanding control system is designed based on the Active Disturbance Rejection Control (ADRC) to enable the UAV to land safely in crosswind. The longitudinal autolanding control system is composed of a throttle control subsystem and an altitude control subsystem. The lateral autolanding control system is composed of a crabbing control subsystem and a decrabbing control subsystem. In contrast to previous methods, our approach can directly and real-timely estimate the UAV's internal and external disturbances (e.g., system's longitudinal and lateral couplings, wind disturbances) and then compensate for them. Simulations are done from the glide phase to the parking at the end of taxiing under wide range wind disturbances (e.g., constant crosswind, changing crosswind). Results show that our autolanding control scheme can land the UAV safely under wide range crosswind with the help of ADRC.

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“…In Ref. 12, three lateral controllers as well as the crabbing and decrabbing maneuvers based on PID control are designed to deal with the crosswind, tail wind, and turbulence. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Anti-crosswind Autolanding of UAVs based on Active Disturbance Rejection Control

Xiong

Fan

et al. 2010

AIAA Guidance, Navigation, and Control Conference

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“…In recent years, some advanced control theories are gradually introduced into the design of attitude control system for UAVs with the development of computer technology [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In [1], the output feedback control method was used to design the attitude control system for UAV.…”

Section: Introductionmentioning

confidence: 99%

Design of Attitude Control System for UAV Based on Feedback Linearization and Adaptive Control

Zhou

Yin

Wang

et al. 2014

Mathematical Problems in Engineering

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Attitude dynamic model of unmanned aerial vehicles (UAVs) is multi-input multioutput (MIMO), strong coupling, and nonlinear. Model uncertainties and external gust disturbances should be considered during designing the attitude control system for UAVs. In this paper, feedback linearization and model reference adaptive control (MRAC) are integrated to design the attitude control system for a fixed wing UAV. First of all, the complicated attitude dynamic model is decoupled into three single-input single-output (SISO) channels by input-output feedback linearization. Secondly, the reference models are determined, respectively, according to the performance indexes of each channel. Subsequently, the adaptive control law is obtained using MRAC theory. In order to demonstrate the performance of attitude control system, the adaptive control law and the proportional-integral-derivative (PID) control law are, respectively, used in the coupling nonlinear simulation model. Simulation results indicate that the system performance indexes including maximum overshoot, settling time (2% error range), and rise time obtained by MRAC are better than those by PID. Moreover, MRAC system has stronger robustness with respect to the model uncertainties and gust disturbance.

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Autonomous Landing Design of UAVs using Feedback Linearization Controller with Anti Windup Scheme

2020

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Autolanding Strategies for a Fixed Wing UAV in Adverse Atmospheric Conditions

Cited by 5 publications

References 5 publications

Anti-crosswind Autolanding of UAVs based on Active Disturbance Rejection Control

Anti-crosswind Autolanding of UAVs based on Active Disturbance Rejection Control

Design of Attitude Control System for UAV Based on Feedback Linearization and Adaptive Control

Autonomous Landing Design of UAVs using Feedback Linearization Controller with Anti Windup Scheme

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