Modeling and Control of a Powered Parafoil in Wind and Rain Environments

Jin, Tao; Liang, Wei; Luo, Shijun; Chen, Zengqiang; Tan, Panlong; He, Yiqian

doi:10.1109/taes.2017.2667838

Cited by 20 publications

(8 citation statements)

References 29 publications

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“…Hence, the influence of apparent mass on the system must be considered. The calculation method of the apparent mass refers to the reference from the works of Lissaman and Brown and Tao et al The forces on the parafoil include the aerodynamic force

F_{s}^{aero}

, gravity

F_{s}^{G}

, and tension of suspension ropes

F_{s}^{t}

\frac{\partial P_{s}}{\partial t} + W_{s} \times P_{s} = F_{s}^{aero} + F_{s}^{G} + F_{s}^{t}

\frac{\partial H_{s}}{\partial t} + W_{s} \times H_{s} + V_{s} \times P_{s} = M_{s}^{aero} + M_{s}^{G} + M_{s}^{t} + M_{s}^{f} .

…”

Section: Modeling Of Flexible Wing Uav Systemmentioning

confidence: 99%

Altitude control for flexible wing unmanned aerial vehicle based on active disturbance rejection control and feedforward compensation

Sun

Chen

et al. 2019

Intl J Robust & Nonlinear

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Summary For achieving the accurate trajectory tracking of the flexible wing unmanned aerial vehicle in the complicated missions, especially the vertical component, a feedforward compensation unit–based active disturbance rejection control (ADRC) is proposed. In ADRC, the internal dynamics and complicated influence of the total disturbance will be estimated and dynamically compensated by extended state observer (ESO). It puts a very high request on the observation ability of ESO with the unpredictable external disturbance, complex internal coupling influence, and the strong nonlinear characteristic of the proposed system. For this reason, by deeply analyzing the model of this system, the varying attitude influence on the altitude control will be deduced. Then, this influence will be compensated previously by a feedforward compensation unit. Through the previous compensation of the calculable part of the internal dynamics and total disturbance, the burden of ESO can be reduced largely. In this way, it improves the control effect of the ADRC with better observation precision of ESO. After that, based on the hardware‐in‐the‐loop simulation, the effectiveness of the proposed method is verified completely with the complicated flight missions. The robustness of the control effect and observation ability of ESO are also verified by the Monte Carlo simulation. At last, the results of actual flight experiment prove the advancement and practicability of the proposed ADRC method.

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F_{s}^{aero}

, gravity

F_{s}^{G}

, and tension of suspension ropes

F_{s}^{t}

\frac{\partial P_{s}}{\partial t} + W_{s} \times P_{s} = F_{s}^{aero} + F_{s}^{G} + F_{s}^{t}

\frac{\partial H_{s}}{\partial t} + W_{s} \times H_{s} + V_{s} \times P_{s} = M_{s}^{aero} + M_{s}^{G} + M_{s}^{t} + M_{s}^{f} .

…”

Section: Modeling Of Flexible Wing Uav Systemmentioning

confidence: 99%

Altitude control for flexible wing unmanned aerial vehicle based on active disturbance rejection control and feedforward compensation

Sun

Chen

et al. 2019

Intl J Robust & Nonlinear

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“…However, there are no accurate aerodynamic data. In [9,10] there are aerodynamic parameters on the basis of modeling in the ANSYS-CFD package, while the synthesis of the adjustable aerial vehicle control system does not consider the uncertainty of the high-order mathematical model, as well as changes in environmental parameters with the appearance of such natural factors as wind, rain, etc.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Fig. 4 shows the dependences of lift (Cy), drag (Cx) coefficients of the UAV on the angle of attack α for a number at a velocity of 10 m/s compared to the experiment and modeling [9][10][11][12][13][14].…”

Section: Determination Of Uav Aerodynamic Characteristics In the Amentioning

confidence: 99%

“…By simple transformations, for the subsequent synthesis, on the basis of the reference model, the transfer function (9) was synthesized: 3 2 1 2 3 4 4 3 2 1 3 3 4 . On the basis of the obtained analytical dependences, the block diagram of the reference model of the adaptive control system in the velocity channel is shown in Fig.…”

Section: Synthesis Of the Adjustable Pid Controller Of The Uav Automamentioning

confidence: 99%

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Designing adaptive PID controller non-sensitive to changes in aerodynamic characteristics of an unmanned aerial vehicle

Boudiba

Firsov

2019

EEJET

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Представлено метод реалiзацiї адаптивного PID-регулятора за допомогою еталонної моделi безпiлотного лiтального апарату. Безпiлотний лiтальний апарат має нелiнiйну характеристику i високу чутливiсть до зовнiшнiх впливiв. Робота класичного регулятора в нелiнiйнiй моделi при виникненнi впливiв, що обурюють, не задовольняє заданi критерiї якостi. Проблеми, якi впливають на час польоту безпiлотного лiтального апарату, представленi варiацiями аеродинамiчних коефiцiєнтiв у вiдомих дiапазонах. При цьому змiнюються аеродинамiчнi параметри, i система стає нестiйкою. Для усунення не бажає вiдхилень в систему керування лiтального апарату вводитися адаптивний контур PID-регулятора. Використовуючи еталонну модель об'єкта керування, порiвнюючий пристрiй адаптацiї видає необхiднi параметри налаштування PID-регулятора. Введення такого роду корекцiї керуючого сигналу дозволяє парирувати всiлякi вiдмови i обурення, якi призводять до неконтрольованого управлiння. Було встановлено, що цей спосiб формування керуючого впливу на безпiлотний лiтальний апарат дуже ефективний, так як отриманий результат ближчий до експериментального. Дослiдження вiдмов здiйснювалося через спостереження змiни аеродинамiчних коефiцiєнтiв. Завдяки дослiдженню змiни аеродинамiчних коефiцiєнтiв можна визначити номiнальнi значення коефiцiєнтiв об'єкта без присутностi вiдмов. Такий пiдхiд до моделювання безпiлотного лiтального апарату також дає можливiсть вирiшити економiчну сторону питання -провести експерименти в аеродинамiчному додатку ANSYS-CFX без витрат на вiдновлення втрачених пiд час експерементальних випробувань апаратiв та його елементiв Ключовi слова: адаптивне PID, еталонна модель адаптивного керування (ЕМАУ), аеродинамiчнi коефiцiєнти, невизначена модельUDC 62-519

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“…Parafoil systems play a significant role and have wide applications in the area of disaster assistance, aerospace, and military fields [5][6][7][8]. Due to their slow airspeed in comparison to conventional fixed-wing aircrafts, the parafoil systems are extremely susceptible to wind fields [9,10]. In particular, disturbed by both the speed and direction of the ambient wind field, the parafoil system easily deviates from its desired flight path and generates position drifts, even leading to a stall [1,11,12].…”

Section: Introductionmentioning

confidence: 99%

In-flight Wind Field Identification and Prediction of Parafoil Systems

et al. 2020

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The wind field is an essential factor that affects accurate homing and flare landing of parafoil systems. In order to obtain the ambient wind field during the descent of a parafoil system, a combination method of in-flight wind field identification and prediction is proposed. First, a wind identification method only using global position system information is derived based on the flight dynamics of parafoil systems. Then a wind field prediction model is constructed using the atmospheric dynamics, and the low-altitude wind field is predicted based on the identified wind field of high-altitude. Finally, simulations of wind field identification and prediction are conducted. The results demonstrate that the proposed method can identify the wind fields precisely and also predict the wind fields reasonably. This method can potentially be applied in practical parafoil systems to provide wind field information for homing tasks.

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Modeling and Control of a Powered Parafoil in Wind and Rain Environments

Cited by 20 publications

References 29 publications

Altitude control for flexible wing unmanned aerial vehicle based on active disturbance rejection control and feedforward compensation

Altitude control for flexible wing unmanned aerial vehicle based on active disturbance rejection control and feedforward compensation

Designing adaptive PID controller non-sensitive to changes in aerodynamic characteristics of an unmanned aerial vehicle

In-flight Wind Field Identification and Prediction of Parafoil Systems

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