Dynamics and Simulation of the Effects of Wind on UAVs and Airborne Wind Measurement

Choi, Hyoung Sik; Lee, Sangjong; Ryu, Hyeok; Shim, Hyunchul; Ha, Cheolkeun

doi:10.2322/tjsass.58.187

Cited by 21 publications

(16 citation statements)

References 9 publications

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“…Another commonly studied testbed uses a proportional-embedded PID controller to jointly control position and attitude [1,2,3,12,13]. The AscTec Hummingbird is a typical model of this type of quadrotor.…”

Section: Control Systems For Unmanned Aerial Vehicles Uavsmentioning

confidence: 99%

A Mathematical Model for Controlling a Quadrotor UAV

Kowalik¹,

Novák

2021

Transactions on Aerospace Research

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Given the recent surge in interest in UAVs and their potential applications, a great deal of work has lately been done in the field of UAV control. However, UAVs belong to a class of nonlinear systems that are inherently difficult to control. In this study we devised a mathematical model for a PID (proportional integral derivative) control system, designed to control a quadrotor UAV so that it follows a predefined trajectory. After first describing quadrotor flight dynamics, we present the control model adopted in our system (developed in MATLAB Simulink). We then present simulated results for the use of the control system to move a quadrotor UAV to desired locations and along desired trajectories. Positive results of these simulation support the conclusion that a quadrotor UAV spatial orientation control system based on this model will successfully fulfil its task also in real conditions.

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Section: Control Systems For Unmanned Aerial Vehicles Uavsmentioning

confidence: 99%

A Mathematical Model for Controlling a Quadrotor UAV

Kowalik¹,

Novák

2021

Transactions on Aerospace Research

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“…In practice, due to, e.g., imperfect channel estimation and finite feedback, the UAVs can never have perfect CSI. Moreover, the wind would incur non-negligible body jittering of the UAVs, which will also harm the acquisition of CSI [19]. Therefore, it is of great importance to investigate the robust system design of UAV assisted wireless communication system under imperfect CSI assumptions.…”

Section: Index Termsmentioning

confidence: 99%

“…where K = max{N m , ∀m ∈ M} and θ nmi = 0 for N n < i ≤ K, ∀n, m ∈ M. (20a) collects all the inter-cell interferences, while (20b) contains [Θ n1 , ..., Θ n K ] and {θ nm K } m, K (where m = n) that are relevant to UAV n . Remind that Θ n K = Nm m=1,m =n θ mn K , it can be verified that there exists a matrix E n ∈ {0, 1} M K×M M K , such that θ n = E n θ, ∀n ∈ M. Then, based on (19) and (20), problem (18) can be compactly rewritten as…”

Section: Nn K=1mentioning

confidence: 99%

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Intelligent User Clustering and Robust Beamforming Design for UAV-NOMA Downlink

Xu,

Fang,

Cai

et al. 2020

Preprint

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In this work, we consider a downlink non-orthogonal multiple access (NOMA) network with multiple single-antenna users and multi-antenna unmanned aerial vehicles (UAVs). In particular, the users are spatially located in several clusters by following the Poisson Cluster Process and each user cluster is served by a hovering UAV with NOMA. For practical considerations, we assume that only imperfect channel state information (CSI) of each user is available at the UAVs. Based on this model, the problem of joint user clustering and robust beamforming design is formulated to minimize the sum transmission power, and meanwhile, guarantee the quality of service requirements of users. Due to the integer variables of user clustering, coupling effects of beamformers, and infinitely many constraints caused by the imperfect CSI, the formulated problem is challenging to solve. For computational complexity reduction, the original problem is divided into user clustering subproblem and robust beamforming design subproblem. By utilizing the users' position information, we propose a k-means++ based unsupervised clustering algorithm to first deal with the user clustering problem. Then, we focus on the robust beamforming design problem. To attain insights on solving the robust beamforming design problem, we firstly investigate the problem with perfect CSI, and the associated problem is shown can be solved optimally. Secondly, for the problem in the general case with imperfect CSI, a semidefinite relaxation (SDR) based method is proposed to produce a suboptimal solution efficiently. Moreover, we provide a sufficient condition under which the SDR based approach can guarantee to obtain an optimal rank-one solution, which is theoretically analyzed. Finally, an alternating direction method of multipliers based algorithm is proposed to allow the UAVs to perform robust beamforming design in a decentralized fashion efficiently. Simulation results demonstrate the efficacy of the proposed algorithms and transmission scheme.

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“…In practical UAV communication systems, UAV-mounted transceivers flying in the air may encounter strong wind gusts, which leads to random body jittering with respect to angular movements [7]. The estimation accuracy of the D. Xu, Y.…”

Section: Introductionmentioning

confidence: 99%

Robust Resource Allocation for UAV Systems with UAV Jittering and User Location Uncertainty

Sun

et al. 2018

2018 IEEE Globecom Workshops (GC Wkshps)

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In this paper, we investigate resource allocation algorithm design for multiuser unmanned aerial vehicle (UAV) communication systems in the presence of UAV jittering and user location uncertainty. In particular, we jointly optimize the twodimensional position and the downlink beamformer of a fixedaltitude UAV for minimization of the total UAV transmit power. The problem formulation takes into account the quality-of-service requirements of the users, the imperfect knowledge of the antenna array response (AAR) caused by UAV jittering, and the user location uncertainty. Despite the non-convexity of the resulting problem, we solve the problem optimally employing a series of transformations and semidefinite programming relaxation. Our simulation results reveal the dramatic power savings enabled by the proposed robust scheme compared to two baseline schemes. Besides, the robustness of the proposed scheme with respect to imperfect AAR knowledge and user location uncertainty at the UAV is also confirmed.

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Dynamics and Simulation of the Effects of Wind on UAVs and Airborne Wind Measurement

Cited by 21 publications

References 9 publications

A Mathematical Model for Controlling a Quadrotor UAV

A Mathematical Model for Controlling a Quadrotor UAV

Intelligent User Clustering and Robust Beamforming Design for UAV-NOMA Downlink

Robust Resource Allocation for UAV Systems with UAV Jittering and User Location Uncertainty

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