Model for Longitudinal Perch Maneuvers of a Fixed-Wing Unmanned Aerial Vehicle

Puopolo, Michael G.; Jacob, Jamey

doi:10.2514/1.c033136

Cited by 7 publications

(8 citation statements)

References 18 publications

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“…The longitudinal dynamical model is given as [13] 16 = F fdf + dC pLw cos(0 -yw) + dcpDw sin(0 yw') ^25) -dsLs cos (0 -ys) -dsDs sin(0 -%)…”

Section: A Longitudinal Attitude Dynamicsmentioning

confidence: 99%

Adaptive Optimal Flight Control for a Fixed-wing Unmanned Aerial Vehicle using Incremental Value Iteration

Kampen

2023

2023 IEEE International Conference on Mechatronics (ICM)

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This paper deals with the design o f an adaptive op timal controller for a fixed-wing Unmanned Aerial Vehicle(UAV) using an incremental value iteration algorithm. The incremental model is firstly introduced to linearize a nonlinear system. The re cursive least squares(RLS) identification algorithm is then used to identify the incremental model. Based on incremental control, the incremental value iteration algorithm is developed for a nonlinear optimal control problem. Moreover, this algorithm is applied to longitudinal attitude tracking of a fixed-wing unmanned aerial vehicle. Simulation results show that the designed adaptive flight controller is robust to variations in initial value o f the angle of attack.

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“…The longitudinal dynamical model is given as [13] 16 = F fdf + dC pLw cos(0 -yw) + dcpDw sin(0 yw') ^25) -dsLs cos (0 -ys) -dsDs sin(0 -%)…”

Section: A Longitudinal Attitude Dynamicsmentioning

confidence: 99%

Adaptive Optimal Flight Control for a Fixed-wing Unmanned Aerial Vehicle using Incremental Value Iteration

Kampen

2023

2023 IEEE International Conference on Mechatronics (ICM)

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“…In total, 10 UAVs have been modeled (Figure 3). These are: ALO and SIVA (INTA, 2020), Megastar (PROTECH data, 2020; Tristancho et al , 2010), Shadow (Tecktron Systems, 2009), Ant Plane (Funaki and Hirasawa, 2008), Perching (Puopolo and Jacob, 2015), Raven (AeroVironment, 2020), Taurus (Qazi et al , 2017), Brayraktar (Army Technology, 2020) and IAI (2013). ALO (span: 3.84 m and maximum take-off weight, maximum take-off weight (MTOW): 55 kg) and SIVA (span: 5.81 m, maximum weight: 300 kg) are UAVs developed by the Spanish National Institute for Aerospace Technology.…”

Section: Models Of Fixed-wing Uavsmentioning

confidence: 99%

“…but also in the following selected model parameters to design the controller: pitch, angle of attack, longitudinal and/or vertical velocity, altitude, pitch rate. Most of the published works consider both the lateral and longitudinal dynamics (Çoban, 2020; Oktay et al , 2016); being longitudinal stability models (Blakelock, 1991; Nelson, 1998) the minimum ones (alone or together with lateral) are used in new autopilot controller designs (Aliyu et al , 2015; Bertran and Sànchez Cerdà, 2016; Huang et al , 2009; Jafarov, 2006; Qazi et al , 2017; Puopolo and Jacob, 2015; Triputra et al , 2012). So, they are the first candidates to propose a step toward a generalized benchmark for results comparison.…”

Section: Introductionmentioning

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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“…The author also used a component-based approach to model the vehicle in the full AoA and side-slip angle angle. Puopolo and Jacob [ 17 ] modeled the longitudinal perch maneuverer of a fixed-wing UAV. Perch is a motion that birds usually use under deep stall.…”

Section: Related Workmentioning

confidence: 99%

Development of Model Predictive Controller for a Tail-Sitter VTOL UAV in Hover Flight

Zhou

Sun

et al. 2018

Sensors

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This paper presents a model predictive controller (MPC) for position control of a vertical take-off and landing (VTOL) tail-sitter unmanned aerial vehicle (UAV) in hover flight. A ‘cross’ configuration quad-rotor tail-sitter UAV is designed with the capabilities for both hover and high efficiency level flight. The six-degree-of-freedom (DOF) nonlinear dynamic model of the UAV is built based on aerodynamic data obtained from wind tunnel experiments. The model predictive position controller is then developed with the augmented linearized state-space model. Measured and unmeasured disturbance model are introduced into the modeling and optimization process to improve disturbance rejection ability. The MPC controller is first verified and tuned in the hardware-in-loop (HIL) simulation environment and then implemented in an on-board flight computer for real-time indoor experiments. The simulation and experimental results show that the proposed MPC position controller has good trajectory tracking performance and robust position holding capability under the conditions of prevailing and gusty winds.

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Model for Longitudinal Perch Maneuvers of a Fixed-Wing Unmanned Aerial Vehicle

Cited by 7 publications

References 18 publications

Adaptive Optimal Flight Control for a Fixed-wing Unmanned Aerial Vehicle using Incremental Value Iteration

Adaptive Optimal Flight Control for a Fixed-wing Unmanned Aerial Vehicle using Incremental Value Iteration

UAV generalized longitudinal model for autopilot controller designs

Development of Model Predictive Controller for a Tail-Sitter VTOL UAV in Hover Flight

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