Flight test of flying-wing type unmanned aerial vehicle with partial wing-loss

Kim, Kijoon; Kim, Taegyun; Suk, Jinyoung; Ahn, Jongmin; Kim, Nakwan; Kim, Byoungsoo

doi:10.1177/0954410018758497

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…The signal is sent to the aircraft actuator through an RC transmitter. Doublet inputs are used by researchers and manufacturers to identify aircraft [66][67][68][69][70][71][72][73][74][75][76]. Fig.…”

Section: Flight Data Acquisitionmentioning

confidence: 99%

Validation of Quad Tail-sitter VTOL UAV Model in Fixed Wing Mode

Priyambodo

Majid²,

Shouran³

2023

Journal of Robotics and Control

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Vertical take-off and landing (VTOL) is a type of unmanned aerial vehicle (UAV) that is growing rapidly because its ability to take off and land anywhere in tight spaces. One type of VTOL UAV, the tail-sitter, has the best efficiency. However, besides the efficiency offered, some challenges must still be overcome, including the complexity of combining the ability to hover like a helicopter and fly horizontally like a fixed-wing aircraft. This research has two contributions: in the form of how the analytical model is generated and the tools used (specifically for the small VTOL quad tail-sitter UAV) and how to utilize off-the-shelf components for UAV empirical modeling. This research focuses on increasing the speed and accuracy of the UAV VTOL control design in fixed-wing mode. The first step is to carry out analysis and simulation. The model is analytically obtained using OpenVSP in longitudinal and lateral modes. The next step is to realize this analytical model for both the aircraft and the controls. The third step is to measure the flight characteristics of the aircraft. Based on the data recorded during flights, an empirical model is made using system identification technique. The final step is to vali-date the analytical model with the empirical model. The results show that the characteristics of the analytical mode fulfill the specified requirements and are close to the empirical model. Thus, it can be concluded that the analytical model can be implemented directly, and consequently, the VTOL UAV design and development process has been shortened.

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Section: Flight Data Acquisitionmentioning

confidence: 99%

Validation of Quad Tail-sitter VTOL UAV Model in Fixed Wing Mode

Priyambodo

Majid²,

Shouran³

2023

Journal of Robotics and Control

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“…In equation (17), the term À is the adaptation gain and! 0 , 0 , 0 , 0 are the initial values of pertinent variables, which is guessed for initialization of the algorithm.…”

Section: Robust Adaptive Control Algorithmmentioning

confidence: 99%

“…An effective fault-tolerant feedback controller is required to compensate the damage, and to guarantee the safety of aircraft. References [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] investigated adaptive control schemes to compensate the structural uncertainties due to aircraft failures or damage. References 3,13 examined FTC algorithm for an aircraft that suffers from vertical tail loss.…”

Section: Introductionmentioning

confidence: 99%

“…[8][9][10][11] References [14][15][16] proposed reconfigurable adaptive control algorithm for a wing damaged airplane. Kim et al 17 investigate experimental evaluation of an adaptive neural network controller to a flying-wing type unmanned aerial vehicle experiencing partial wing-loss. The flight test verifies that the damaged unmanned aerial vehicle shows drastic roll behavior with the unstable longitudinal response, and the neural network-based adaptive controller combined with feedback linearization successfully compensates for the wing damage.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear robust adaptive control of an airplane with structural damage

Asadi

Ahmadi

2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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This article investigates the design of a novel nonlinear robust adaptive control architecture to stabilize and control an airplane in the presence of left-wing damage. Damage effect is modeled by considering the sudden mass and inertia changes, center of gravity, and aerodynamic variations. The novel nonlinear control algorithm applies a state predictor as well as the error between the real damaged dynamics and a virtual model based on the nominal aircraft dynamics in the control loop of the adaptive strategy. The projection operator is used for the purpose of robustness of the adaptive control algorithm. The stability of the proposed nonlinear robust adaptive controller is demonstrated applying the Lyapunov stability theory. The performance of the proposed controller is compared with two previous successful algorithms, which are implemented on the Generic Transport Model airplane to accommodate wing damage. Numerical simulations demonstrate the effectiveness and advantages of the proposed robust adaptive algorithm regarding two other algorithms of adaptive sliding mode and L 1 adaptive control.

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“…This controller eliminates the need to redesign another controller owing to distributing control signals among redundant actuators. An ANN-based adaptive controller using Feedback Linearization was developed in [17] to address the dramatic roll and unsteady longitudinal behavior in the condition of partial wing damage. In another study, to achieve hydraulic fault tolerance in the longitudinal axis, [18] evaluated the performance of three controllers, including Adaptive Back-stepping, Robust Sliding Mode, and PID.…”

Section: Introductionmentioning

confidence: 99%

Robust Attitude Control of an Agile Aircraft Using Improved Q-Learning

et al. 2022

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Attitude control of a novel regional truss-braced wing (TBW) aircraft with low stability characteristics is addressed in this paper using Reinforcement Learning (RL). In recent years, RL has been increasingly employed in challenging applications, particularly, autonomous flight control. However, a significant predicament confronting discrete RL algorithms is the dimension limitation of the state-action table and difficulties in defining the elements of the RL environment. To address these issues, in this paper, a detailed mathematical model of the mentioned aircraft is first developed to shape an RL environment. Subsequently, Q-learning, the most prevalent discrete RL algorithm, will be implemented in both the Markov Decision Process (MDP) and Partially Observable Markov Decision Process (POMDP) frameworks to control the longitudinal mode of the proposed aircraft. In order to eliminate residual fluctuations that are a consequence of discrete action selection, and simultaneously track variable pitch angles, a Fuzzy Action Assignment (FAA) method is proposed to generate continuous control commands using the trained optimal Q-table. Accordingly, it will be proved that by defining a comprehensive reward function based on dynamic behavior considerations, along with observing all crucial states (equivalent to satisfying the Markov Property), the air vehicle would be capable of tracking the desired attitude in the presence of different uncertain dynamics including measurement noises, atmospheric disturbances, actuator faults, and model uncertainties where the performance of the introduced control system surpasses a well-tuned Proportional–Integral–Derivative (PID) controller.

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Flight test of flying-wing type unmanned aerial vehicle with partial wing-loss

Cited by 8 publications

References 15 publications

Validation of Quad Tail-sitter VTOL UAV Model in Fixed Wing Mode

Validation of Quad Tail-sitter VTOL UAV Model in Fixed Wing Mode

Nonlinear robust adaptive control of an airplane with structural damage

Robust Attitude Control of an Agile Aircraft Using Improved Q-Learning

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