Aerodynamic Modeling of Fully-Actuated Multirotor UAVs with Nonparallel Actuators

Abbaraju, Praveen; Ma, Xiaoyi; Jiang, Guangying; Rastgaar, Mo; Voyles, Richard M.

doi:10.1109/iros51168.2021.9636572

Cited by 6 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Import initial value of state and control inputs Solve the Equilibrium state of multirotors (26) Calculate optimization problem function (24) Import initial value of rotor tilt angles Minimum? In the outer loop, relying on the solution of the inner loop, problem (24) is solved based on sequential quadratic programming (SQP) [34].…”

Section: Update the Dynamic Of Multirotorsmentioning

confidence: 99%

See 1 more Smart Citation

Large-Sized Multirotor Design: Accurate Modeling with Aerodynamics and Optimization for Rotor Tilt Angle

Xie,

Yan,

Liang

et al. 2023

Drones

View full text Add to dashboard Cite

Advancements in aerial mobility (AAM) are driven by needs in transportation, logistics, rescue, and disaster relief. Consequently, large-sized multirotor unmanned aerial vehicles (UAVs) with strong power and ample space show great potential. In order to optimize the design process for large-sized multirotors and reduce physical trial and error, a detailed dynamic model is firstly established, with an accurate aerodynamic model. In addition, the center of gravity (CoG) offset and actuator dynamics are also well considered, which are usually ignored in small-sized multirotors. To improve the endurance and maneuverability of large-sized multirotors, which is the key concern in real applications, a two-loop optimization method for rotor tilt angle design is proposed based on the mathematical model established previously. Its inner loop solves the dynamic equilibrium points to relax the complex dynamic constraints caused by aerodynamics in the overall optimization problem, which improves the solution efficiency. The ideal design results can be obtained through the offline process, which greatly reduces the difficulties of physical trial and error. Finally, various experiments are carried out to demonstrate the accuracy of the established model and the effectiveness of the optimization method.

show abstract

Section: Update the Dynamic Of Multirotorsmentioning

confidence: 99%

“…Furthermore, a general explicit mathematical model of aerodynamics is required for the design of multirotors. The simplified mathematical model of aerodynamics is adopted in [26]. In [27,28], an accurate model of the aerodynamics of a rotor is derived in detail.…”

Section: Introductionmentioning

confidence: 99%

Large-Sized Multirotor Design: Accurate Modeling with Aerodynamics and Optimization for Rotor Tilt Angle

Xie,

Yan,

Liang

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…This also affects the aerodynamics forces acting on the vehicle, which are not easily characterized. Furthermore, recently has been shown in [7] that the aerodynamic effects caused due to tilt angled propeller configurations has impact on the vehicle performance, and in particular characterize experimentally some effects that were not particularly considered before as the blade flapping effect for cant angled propellers. The authors proposed an aerodynamic model for these effects, and could be compensated but wind tunnel experiments must be carried out, which is not an easy task.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Fault-Tolerant Control for an Hexarotor with Model Uncertainty

Colombo¹,

Fernandez²,

Giribet³

2022

Preprint

View full text Add to dashboard Cite

In this paper we present a learning-based tracking controller based on Gaussian processes (GP) for a fault-tolerant hexarotor in a recovery maneuver. In particular, to estimate certain uncertainties that appear in a hexacopter vehicle with the ability to reconfigure its rotors to compensate for failures. The rotors reconfiguration introduces disturbances that make the dynamic model of the vehicle differ from the nominal model. The control algorithm is designed to learn and compensate the amount of modeling uncertainties after a failure in the control allocation reconfiguration by using GP as a learning-based model for the predictions. In particular the presented approach guarantees a probabilistic bounded tracking error with high probability. The performance of the learning-based fault-tolerant controller is evaluated through experimental tests with an hexarotor UAV.

show abstract