Aerodynamic Effects in Multirotors Flying Close to Obstacles: Modelling and Mapping

Sanchez-Cuevas, Pedro J.; Martín, Víctor Manuel Montes; Heredia, Guillermo; Ollero, A.

doi:10.1007/978-3-030-35990-4_6

Cited by 4 publications

(5 citation statements)

References 15 publications

(13 reference statements)

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“…Consequently, the controller must be designed to facilitate stability in the presence of such nonlinear disturbances. [4] demonstrates the safe distance for operation is beyond 2× Radius of Propeller from the obstruction or wall.…”

Section: Introductionmentioning

confidence: 92%

“…2) Ceiling Effect: When a UAV flies underneath a horizontal surface, nonlinear disturbances in the form of ceiling effect (CE) acts on the UAV. Contrary to GE, CE pulls the UAV towards the surface [4]. The mathematical approximation is found as a curve in (10) Ceiling Effect:…”

Section: Aerodynamic Ceiling Ground and Wall Effectmentioning

confidence: 99%

“…The literature is sufficiently populated with efforts to model ceiling and ground effects [3], [4], [5], [6], but there is a clear gap in formulating control algorithms to tackle these effects in a combined fashion. Nonlinear Model Predictive Control (MPC) [7] has been used for navigation and obstacle avoidance of UAVs for real-time utilities.…”

Section: Related Workmentioning

confidence: 99%

“…1) Ground Effect: When a UAV flies over a horizontal surface, ground effects (GE) occur. GE is an aerodynamic effect that has been studied extensively and seen to push UAVs away from the ground [4], [5]. The theoretical model of GE presented by Cheeseman and Bennet [16] is a widely accepted thurst ratio approximation of GE as given in (9).…”

Section: Aerodynamic Ceiling Ground and Wall Effectmentioning

confidence: 99%

See 3 more Smart Citations

Control Barrier Function-based Predictive Control for Close Proximity operation of UAVs inside a Tunnel

Mundheda¹,

K²,

Kandath³

2023

Preprint

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This paper introduces a method for effectively controlling the movement of an Unmanned Aerial Vehicle (UAV) within a tunnel. The primary challenge of this problem lies in the UAV's exposure to nonlinear distance-dependent torques and forces generated by the tunnel walls, along with the need to operate safely within a defined region while in close proximity to these walls. To address this problem, the paper proposes the implementation of a Model Predictive Control (MPC) framework with constraints based on Control Barrier Function (CBF). The paper approaches the issue in two distinct ways; first, by maintaining a safe distance from the tunnel walls to avoid the effects of both the walls and ceiling, and second, by minimizing the distance from the walls to effectively manage the nonlinear forces associated with close proximity tasks. Finally, the paper demonstrates the effectiveness of its approach through testing on simulation for various close proximity trajectories with the realistic model of aerodynamic disturbances due to the proximity of the ceiling and boundary walls.

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Section: Introductionmentioning

confidence: 92%

Section: Aerodynamic Ceiling Ground and Wall Effectmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Aerodynamic Ceiling Ground and Wall Effectmentioning

confidence: 99%

See 2 more Smart Citations

Control Barrier Function-based Predictive Control for Close Proximity operation of UAVs inside a Tunnel

Mundheda¹,

K²,

Kandath³

2023

Preprint

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show abstract

“…Conversion from quaternion to Euler angle can be performed by using a canonical set of Euler angles as [45]…”

Section: B Quadrotor Flight Dynamics Modelingmentioning

confidence: 99%

Robust Tracking Control for Quadrotor UAV With External Disturbances and Uncertainties Using Neural Network Based MRAC

Madebo,

Abdissa,

Lemma

et al. 2024

IEEE Access

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In this paper, an intelligent Model Reference Adaptive Control (MRAC) based on a neural network is proposed for robust tracking control of quadrotor UAV under external disturbances and parameter variations. First, the singularity-free dynamic model of the quadrotor is developed using Newton-Quaternion formalism. Then, conventional MRAC is designed to generate training data. With the generated data, the Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) are trained offline to get an initial set of network parameters for position controller parameters estimation and attitude control of the quadrotor, respectively, and an online learning algorithm is developed to update those network parameters in real-time. Finally, the performance of the designed Neural network-based MRAC has been evaluated using a numerical simulation in a nominal scenario and by introducing parametric variation and external disturbances as matched and unmatched uncertainties into the system. The simulation results show that the proposed controller has a better tracking performance and disturbance rejection capability compared with the Linear Quadratic Regulator (LQR) and conventional MRAC. Furthermore, the utilized control efforts are minimal and smooth proving functional safety and economical use of the controller. Therefore, the suggested controller is feasible for real-time implementation of the quadrotor UAV.

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Chat-PM: A Class of Composite Hybrid Aerial/Terrestrial Precise Manipulator

Ding,

Ji,

Zhang

et al. 2024

IEEE Robot. Autom. Lett.

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Aerodynamic Effects in Multirotors Flying Close to Obstacles: Modelling and Mapping

Cited by 4 publications

References 15 publications

Control Barrier Function-based Predictive Control for Close Proximity operation of UAVs inside a Tunnel

Control Barrier Function-based Predictive Control for Close Proximity operation of UAVs inside a Tunnel

Robust Tracking Control for Quadrotor UAV With External Disturbances and Uncertainties Using Neural Network Based MRAC

Chat-PM: A Class of Composite Hybrid Aerial/Terrestrial Precise Manipulator

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