System Identification of Circulation Control UAV Using X-Plane Flight Simulation Software and Flight Data

Agha, Mohammed; Kanistras, Konstantinos; Saka, Pranith Chander; Valavanis, Kimon P.; Rutherford, Matthew J.

doi:10.2514/6.2017-3154

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…To the authors' knowledge, only a few advanced fixed‐wing UAV control schemes are verified on HITL platforms [39], which presents an HITL experiment that applied a flight control board to control a known numerical model on Matlab directly. Distinguished from Yu's work [39], our HITL experiment utilizes the X‐plane software [47, 48], in which we built a novel miniature ducted‐fan fixed‐wing UAV model as the control object. Since the entire aerodynamic coefficients of the aircraft are unknown, we can verify the effectiveness and robustness of the INFT_OB controller thoroughly.…”

Section: Results and Analysismentioning

confidence: 99%

“…47) where 𝜆 l𝛽 , 𝜆 lp , 𝜆 lr , 𝜆 la , and 𝜆 lr are coefficients related to roll moment L, 𝜆 m0 , 𝜆 m𝛼 , 𝜆 mq , 𝜆 me , 𝜆 m1 , 𝜆 m2 , 𝜆 m3 , 𝜆 m4 , 𝜆 m5 , and 𝜆 m6 are coefficients related to pitch moment M, 𝜆 n𝛽 , 𝜆 np , 𝜆 nr , 𝜆 na , 𝜆 nr , 𝜆 n𝛽2 , and 𝜆 n𝛽3 are coefficients related to yaw moment N. p(k)…”

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confidence: 99%

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Incremental adaptive fixed‐wing unmanned aerial vehicle maneuver control with nonlinear disturbance observer: A finite‐time approach

Cao

Wang

2023

Asian Journal of Control

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This article proposes a versatile controller based on the incremental strategy to explore a finite‐time (FT) solution of fixed‐wing unmanned aerial vehicle (UAV) maneuver control considering the lumped disturbances such as unmodeled dynamics or exogenous perturbations. First, a finite‐time nonlinear disturbance observer augmented with a neural network item is developed to attenuate the adverse effect of the disturbances mentioned above. Then considering the estimated lumped disturbance, a finite‐time incremental controller in the quaternion form is proposed. Rigorous proof shows that the nonlinear disturbance observer and controller achieve finite‐time convergence. In the numerical simulation, the superiority of the proposed method compared to several advanced nonlinear control approaches is illustrated, despite the mismatches in aerodynamic coefficients, wind gusts, and actuator failures. Furthermore, based on a novel model identification approach, a hardware‐in‐the‐loop (HITL) experiment is performed in a high‐fidelity environment, wherein our method's feasibility and practicability are verified.

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Section: Results and Analysismentioning

confidence: 99%

mentioning

confidence: 99%

Incremental adaptive fixed‐wing unmanned aerial vehicle maneuver control with nonlinear disturbance observer: A finite‐time approach

Cao

Wang

2023

Asian Journal of Control

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“…The training and test data were collected from the commercially available flight simulator-X-plane 11. Apart from pilot training, X-plane is also extensively used for research and as an engineering tool by researchers, defense contractors, air forces, aircraft manufacturers, Cessna as well as NASA for applications ranging from flight training to concept design and flight testing [22][23][24][25][26][27][28][29][30]. Nanduri et al demonstrated the capability of X-plane in generating large volumes of Flight Operations Quality Assurance (FOQA) data which can be used for data mining algorithms and machine learning [31].…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

LSTM recurrent neural network assisted aircraft stall prediction for enhanced situational awareness

Saniat¹,

Goni²,

Galib³

2020

Preprint

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Since the dawn of mankind's introduction to powered flights, there have been multiple incidents which can be attributed to aircraft stalls. Most modern-day aircraft are equipped with advanced warning systems to warn the pilots about a potential stall, so that pilots may adopt the necessary recovery measures. But these warnings often have a short window before the aircraft actually enters a stall and require the pilots to act promptly to prevent it. In this paper, we propose a deep learning-based approach to predict an Impending stall, well in advance, even before the stall-warning is triggered. We leverage the capabilities of long short-term memory (LSTM) recurrent neural networks (RNN) and propose a novel approach to predict potential stalls from the sequential in-flight sensor data. Three different neural network architectures were explored. The neural network models, trained on 26400 seconds of simulator flight data are able to predict a potential stall with > 95% accuracy, approximately 10 seconds in advance of the stall-warning trigger. This can significantly augment the Pilot's preparedness to handle an unexpected stall and will add an additional layer of safety to the traditional stall warning systems.

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“…These software programs are also attractive from the engineer perspective for their accuracy in aerodynamic calculations and their ability to exchange data with external systems. X-Plane is one of the most popular realistic flight simulations software and has already been successfully used in many research projects on Unmanned Aerial Vehicle (UAV), such as UAV dynamics identification [2], UAV structure development [3] or control law validation [4], [5]. It offers a wide range of simulation diversity since its use as a simulation brick for the development of controllers has been realized for other various aircrafts such as helicopters [6], quadrotors [7], flapping-wing UAV [8], or tilt-rotor UAV [9].…”

Section: Introductionmentioning

confidence: 99%

A Neural Autopilot Training Platform based on a Matlab and X-Plane co-simulation

Pinguet

Feyel

Sandou

2021

2021 International Conference on Unmanned Aircraft Systems (ICUAS)

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The main objective of this paper is to describe a tool for the aircraft autopilot deployment only based on a flight database. Flight simulators such as X-Plane turn out to be powerful and efficient tools for creating such database and controller experimentation. The paper outlines the development of a co-simulation framework between Matlab and X-Plane using the User Datagram Protocol (UDP). The flight data collected during a first step are then used for the training of neural controllers. The approach is based on the neural network imitation ability to learn the piloting skills implicitly stored in the dataset. Also, in order to include fault-tolerant control, a Neural Multiple Model Adaptive Control (NMMAC) based on previously learned networks is implemented. This architecture consists of a bank of local controllers and a switching logic using a bank of estimators. As an illustration of the proposed platform, it is assumed that the airspeed is unmeasured for the flight director. A neural guidance autopilot based NMMAC is therefore performed on different airspeed values. Experiments show that the designed neural autopilot can successfully track both heading and altitude reference signals, while the method is not restricted to this scope.

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System Identification of Circulation Control UAV Using X-Plane Flight Simulation Software and Flight Data

Cited by 10 publications

References 6 publications

Incremental adaptive fixed‐wing unmanned aerial vehicle maneuver control with nonlinear disturbance observer: A finite‐time approach

Incremental adaptive fixed‐wing unmanned aerial vehicle maneuver control with nonlinear disturbance observer: A finite‐time approach

LSTM recurrent neural network assisted aircraft stall prediction for enhanced situational awareness

A Neural Autopilot Training Platform based on a Matlab and X-Plane co-simulation

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