Adaptive path following for Unmanned Aerial Vehicles in time-varying unknown wind environments

Zhou, Bingyu; Satyavada, Harish; Baldi, Simone

doi:10.23919/acc.2017.7963104

Cited by 19 publications

(9 citation statements)

References 13 publications

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“…On the other hand, an approximate ḣ can be obtained as follows: in absence of wind, the angle between V g and the horizontal plane, is the flight path angle γ fp . So ḣ = V g sin γ fp (14) where V g indicates the ground speed. One can define the commanded airspeed and altitude (V a,c and h c ) in terms of their energy, i.e.,…”

Section: Total Energy Control Systemmentioning

confidence: 99%

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ArduPilot-Based Adaptive Autopilot: Architecture and Software-in-the-Loop Experiments

Baldi

Sun

Xia

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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Section: Total Energy Control Systemmentioning

confidence: 99%

“…But in real life, it is difficult to obtain an accurate model that takes into account environmental influences on the UAV dynamics. Therefore, model-based control methods should be augmented or replaced by adaptive [12]- [14] or intelligent control [15] methods, to handle some uncertainty.…”

Section: Introductionmentioning

confidence: 99%

ArduPilot-Based Adaptive Autopilot: Architecture and Software-in-the-Loop Experiments

Baldi

Sun

Xia

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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“…The possibility of using nonrecursive and recursive parameter estimation on-board of UAVs was examined in [19]. Adaptation of UAVs to different wind conditions has also been the subject of several studies, usually relying on compensating for the offset position error induced by the wind disturbance [20]- [22]. Adaptation in formations of UAVs was studied in [23], [24].…”

Section: Related Workmentioning

confidence: 99%

Embedding Adaptive Features in the ArduPilot Control Architecture for Unmanned Aerial Vehicles

Liu

Xia

Baldi

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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The operation of Unmanned Aerial Vehicles (UAVs) is often subject to state-dependent alterations and unstructured uncertainty factors, such as unmodelled dynamics, environmental weather disturbances, aerodynamics gradients, or changes in inertia and mass due to payloads. While a large number of autopilot solutions have been proposed to operate UAVs, none of these solutions is able to counteract the effects of state-dependent and unstructured uncertainties online by parameter estimation and adaptive control techniques. This work presents a systematic integration of adaptive control into ArduPilot, a popular open-source autopilot suite maintained by a large community of UAV developers. Adaptation features are embedded in the ArduPilot control structure without altering the original architecture, to allow users to use the autopilot suite as usual. Tests show that the proposed adaptive ArduPilot provides consistent improved performance in several uncertain flight conditions. The source code of the proposed adaptive ArduPilot is released at https://github.com/ Friend-Peng/Adaptive-ArduPilot-Autopilot.

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“…In [87] an adaptive control scheme for UAVs path following under wind disturbances is proposed. This control strategy integrates the Vector Field PF law with an adaptive term to deal with the effect of unknown wind disturbances.…”

Section: Vector Fieldmentioning

confidence: 99%

A Survey of Path Following Control Strategies for UAVs Focused on Quadrotors

Rubí

Pérez

Morcego

2019

J Intell Robot Syst

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The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.

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Adaptive path following for Unmanned Aerial Vehicles in time-varying unknown wind environments

Cited by 19 publications

References 13 publications

ArduPilot-Based Adaptive Autopilot: Architecture and Software-in-the-Loop Experiments

ArduPilot-Based Adaptive Autopilot: Architecture and Software-in-the-Loop Experiments

Embedding Adaptive Features in the ArduPilot Control Architecture for Unmanned Aerial Vehicles

A Survey of Path Following Control Strategies for UAVs Focused on Quadrotors

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