Nonlinear 3D path following control of a fixed-wing aircraft based on acceleration control

Galffy, Andras; Böck, Martin; Kugi, Andreas

doi:10.1016/j.conengprac.2019.03.006

Cited by 17 publications

(9 citation statements)

References 17 publications

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“…Due to the extra timing law , path-following control is more flexible than trajectory tracking control. In this sense, path-following control is quite suitable for some UAVs with physical speed restrictions, such as fixed-wing UAVs [99], [100], rotary-wing UAVs [101] and airships [102], [103], and for some specific missions without temporal requirements.…”

Section: B Path-following Problemmentioning

confidence: 99%

Unmanned Aerial Vehicles: Control Methods and Future Challenges

Zuo

Liu

Han

et al. 2022

IEEE/CAA J. Autom. Sinica

128

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Section: B Path-following Problemmentioning

confidence: 99%

Unmanned Aerial Vehicles: Control Methods and Future Challenges

Zuo

Liu

Han

et al. 2022

IEEE/CAA J. Autom. Sinica

128

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show abstract

“…where ω M max{1, ||P ||}. Defining Ω (ω m /ω M ) and using (38), the time derivative (37) further becomes…”

Section: A Proof Of Theoremmentioning

confidence: 99%

“…However, this is done at the price of requiring accurate parametric and structural knowledge of the UAV dynamics. To relax the requirement of an accurate model of the UAV, sliding-mode control [37] or ad-hoc robust cascaded design [38], [39] have been studied to robustify the feedback linearization design. Robustness in sliding-mode control of similar designs is ensured by fixed gains designed based on a worst-case uncertainty bound: therefore, these approaches require a priori bounded uncertainty with known bounds, implying that some parametric a structural knowledge is still crucial in the autopilot design.…”

mentioning

confidence: 99%

An Underactuated Control System Design for Adaptive Autopilot of Fixed-Wing Drones

Baldi

Roy

Yang

et al. 2022

IEEE/ASME Trans. Mechatron.

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Effective design of autopilots for fixed-wing unmanned aerial vehicles (UAVs) is still a great challenge, due to unmodeled effects and uncertainties that these vehicles exhibit during flight. Unmodeled effects and uncertainties comprise longitudinal/lateral cross-couplings, as well as poor knowledge of equilibrium points (trimming points) of the UAV dynamics. The main contribution of this article is a new adaptive autopilot design, based on uncertain Euler-Lagrange dynamics of the UAV and where the control can explicitly take into account under-actuation in the dynamics, reduced structural knowledge of cross-couplings and trimming points. This system uncertainty is handled via appropriately designed adaptive laws: stability of the controlled UAV is analyzed. Hardware-in-the-loop tests, comparisons with an Ardupilot autopilot and with a robustified autopilot validate the effectiveness of the control design, even in the presence of strong saturation of the UAV actuators.

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“…Other approaches include the use of pressure sensors [12] to counteract turbulence effects in wind tunnel tests. Various sensor principles, both anticipating, such as differential pressure sensors [13] and strain gauges [14], as well as reactive measurements, e.g., inertial measurements used for acceleration control [15] are considered. The disadvantage of reactive measurements is that rejection efforts can only be started upon measuring the first negative effects of the disturbance.…”

Section: Introductionmentioning

confidence: 99%

Turbulence load prediction for manned and unmanned aircraft by means of anticipating differential pressure measurements

Galffy

Gaggl²,

Mühlbacher³

et al. 2021

CEAS Aeronaut J

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This paper focuses on the prediction of disturbance effects of the vertical acceleration of an aircraft flying in atmospheric turbulence. To this end, 5-hole probes with high-dynamic differential pressure sensors are installed in front of a fixed-wing unmanned aircraft system (UAS) and a manned experimental aircraft to measure the local airspeed and angle of attack of the airflow. Test flights are performed in light, moderate and severe turbulence to assess the anticipating character and the accuracy of the predicted acceleration. Thereby, depending on the flown airspeed, anticipation times up to 0.1 s are observed. For the UAS the prediction accuracy is assessed to be 71.19% for moderate turbulence and 71.05% for severe turbulence, where vertical acceleration disturbances higher than 30 m/s2 are measured. The first manned test flight revealed a prediction accuracy of 61.97%.

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Nonlinear 3D path following control of a fixed-wing aircraft based on acceleration control

Cited by 17 publications

References 17 publications

Unmanned Aerial Vehicles: Control Methods and Future Challenges

Unmanned Aerial Vehicles: Control Methods and Future Challenges

An Underactuated Control System Design for Adaptive Autopilot of Fixed-Wing Drones

Turbulence load prediction for manned and unmanned aircraft by means of anticipating differential pressure measurements

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