Identification, Modeling and Control of Unmanned Aerial Vehicles

Kumar, Nilesh; Jain, Sheilza

doi:10.14257/ijast.2014.67.01

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…The finally selected (gathered) aircrafts from literature (Triputra et al , 2012; Sufendi et al , 2013; Huang et al , 2009; Ahmed et al , 2015; Kumar and Jain, 2014; Yit and Rajendran, 2015; Aliyu et al , 2015; Persson, 2016) have wingspans ranging from 1.2 to 6.2 m and show slight variations in their geometry. Regarding all the models, the variables are longitudinal velocity, u ; vertical velocity, w ; pitch rate, ρ (or p ); the pitch angle, θ ; the angle-of-attack, α ; and the altitude of the UAV, h .…”

Section: Models Of Fixed-wing Uavsmentioning

confidence: 99%

UAV generalized longitudinal model for autopilot controller designs

Bertrán

Tercero²,

Sànchez-Cerdà

2021

AEAT

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Purpose This paper aims to overcome the main obstacle to compare the merits of the different control strategies for fixed-wing unmanned aerial vehicles (UAVs) to assess autopilot performances. Up to now, the published studies of control strategies have been carried out over disperse models, thus being complicated, if not impossible, to compare the merits of each proposal. The authors present a worked benchmark for autopilots studies, consisting of generalized models obtained by merging UAVs’ parameters gathered from selected literature (journals) with other parameters directly obtained by the authors to include some relevant UAVs whose models are not provided in the literature. To obtain them it has been used a dedicated software (from U.S. Air Force). Design/methodology/approach The proposed models have been constructed by averaging both the main aircraft defining parameters (model derivatives) and pole-zero locations of longitudinal transfer functions. The suitability of the used methodologies has been checked from their capability to fit the short period and the phugoid modes. Previous analytical model arrangement has been required to match a uniform set of parameters, as the inner state variables are neither the same along the different published models nor between the additional models the authors have here contributed. Besides, moving models between the space state representation and transfer function is not just a simple averaging process, as neither the parameters nor the model orders are the same in the different published works. So, the junction of the models to a common set of parameters requires some residual’s computation and transient responses assessment (even Fourier analysis has been included to preserve the dominance of the phugoid) to keep the main properties of the models. The least mean squares technique has been used to have better fittings between SISO model parameters with state–space ones. Findings Both the SISO (Laplace) and state-space models for the longitudinal transfer function of an “averaged” fixed-wing UAV are proposed. Research limitations/implications More complicated situations, such as strong wind conditions, need another kind of models, usually based on finite element method simulation. These particular models apply fluid dynamics to study aerostructural aircraft aspects, such as flutter and other aerolastic aspects, the behavior under icing conditions or other distributed parameter problems. Even some models aim to control other aspects than the autopilot, such as the trajectory prediction. However, these models are not the most suitable for the basic UAV autopilot design (early design), so they are outside the objective of this paper. Obviously, the here-considered UAVs are not all the existing ones, but the number is large enough to consider the result as a reliable and realistic representation. The presented study may be seen as a stepping stone, allowing to include other UAVs in future works. Practical implications The proposed models can be used as benchmarks, or as a previous step to produce improved benchmarks, in order to have a common and realistic scenario the compare the benefits of the different control actions in UAV autopilots continuously presented in the published research. Originality/value A work with the scope of the presented one, merging model parameters from literature with other (often referred in papers and websites) whose parameters have been obtained by the authors has been never published.

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Section: Models Of Fixed-wing Uavsmentioning

confidence: 99%

UAV generalized longitudinal model for autopilot controller designs

Bertrán

Tercero²,

Sànchez-Cerdà

2021

AEAT

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“…A mathematical model of a dual drone system presented in [4] is adopted in this work. The interest in designing a controller for a drone system has gained considerable attention in the last few decades [5][6][7]. Choosing the control technique depends on the control target the vehicle must meet [8].…”

Section: Drones Systemmentioning

confidence: 99%

Failure Detection of Composites with Control System Corrective Response in Drone System Applications

2018

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The paper describes a novel method for the detection of damage in carbon composites as used in drone frames. When damage is detected a further novel corrective response is initiated in the quadcopter flight controller to switch from a four-arm control system to a three-arm control system. This is made possible as a symmetrical frame is utilized, which allows for a balanced weight distribution between both the undamaged quadcopter and the fallback tri-copter layout. The resulting work allows for continued flight where this was not previously possible. Further developing work includes improved flight stability with the aid of an underslung load model. This is beneficial to the quadcopter as a damaged arm attached to the main body by the motor wires behaves as an underslung load. The underslung load works are also transferable in a dual master and slave drone system where the master drone transports a smaller slave drone by a tether, which acts as an underslung load.

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