Longitudinal parameter estimation from real flight data of unmanned cropped delta flat plate configuration

Saderla, Subrahmanyam; Dhayalan, R.; Ghosh, Arnab

doi:10.1108/ijius-07-2015-0008

Cited by 13 publications

(15 citation statements)

References 8 publications

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“…Thus, it is imperative that a data compatibility check is carried out before using the data for aerodynamic modelling and parameter estimation. In other words, a data compatibility check, which is also called flight path reconstruction (FPR), is an integral part of aircraft parameter estimation (2,32) . The main aim of a data compatibility check is to ensure that the measurements, used for subsequent aerodynamic model identification, are consistent and, as far as possible, error free.…”

Section: Generation Of Flight Datamentioning

confidence: 99%

Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

2018

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The current research paper describes the lateral-directional parameter estimation from flight data of a miniature Unmanned Aerial Vehicle (UAV) using Maximum Likelihood (ML), and Neural-Gauss-Newton (NGN) methods. An unmanned configuration with a cropped delta planform and thin rectangular cross-section has been designed, fabricated and instrumented. Exhaustive full-scale wind-tunnel tests were performed on the UAV to extract the form of aerodynamic model that has to be postulated a priori for parameter estimation. Rigorous flight tests have been performed to acquire the flight data for several prescribed manoeuvres. Four sets of compatible flight data have been used to carry out parameter estimation using classical ML and neural-network-based NGN methods. It is observed that the estimated parameters are consistent and the lower values of the Cramer-Rao bound for the corresponding estimates have shown significant confidence in the obtained parameters. Furthermore, to validate the aerodynamic model used and to enhance the confidence in the estimated parameters, a proof of match exercise has been carried out.

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Section: Generation Of Flight Datamentioning

confidence: 99%

Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

2018

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“…These parameters also have a significant role in characterizing the aircraft stability and control behavior and so they are also termed as stability and control parameters. 1–4…”

Section: Introductionmentioning

confidence: 99%

“…These parameters also have a significant role in characterizing the aircraft stability and control behavior and so they are also termed as stability and control parameters. [1][2][3][4] Aerodynamic parameters are usually estimated by wind tunnel method, analytical method, and flight test method. Wind tunnel method has been a great source for validating and fixing the initial design of aircraft.…”

Section: Introductionmentioning

confidence: 99%

“…It is the most widely applied method to estimate aircraft parameters from flight data. [1][2][3][4][5] Research work reported in the literature shows the potential application of soft computing algorithms such as artificial neural networks, fuzzy logic, and neuro-fuzzy-based methods for aircraft modeling and parameter identification. Feed forward neural network (FFNN) has very promising application in aircraft modeling since it captures the nonlinear complexity and as general function approximator.…”

Section: Introductionmentioning

confidence: 99%

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ANFIS-Delta method for aerodynamic parameter estimation using flight data

Kumar

Ghosh

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, aerodynamic parameters have been estimated using neuro-fuzzy-based novel approach (ANFIS-Delta). ANFIS-Delta is an extension of a feed-forward neural network based Delta method. This method augments the philosophies of an adaptive neuro-fuzzy inference system (ANFIS) in the Delta method. The current work studies the comparison of ANFIS-Delta estimated results with the existing methods using the flight data gathered on the Hansa-3 research aircraft at IIT Kanpur and also, demonstrates the efficacy of the algorithm on DLR HFB-320 aircraft data. Further, the robustness of the ANFIS-Delta is examined using simulated data with known measurement noise of various strength and estimated parameters are compared with the wind tunnel extracted aerodynamic parameters.

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“…2. The data acquisition system is embedded with a two quad core ARM (Advanced RISC Machines) processors capable of performing onboard logging at 20 Hz and telemetry at 11 Hz 15,16 . A dedicated graphical user interface (GUI) has been developed using lab-view platform to perform data logging at the ground station.…”

Section: Generation Of Flight Datamentioning

confidence: 99%

Parameter Estimation from Near Stall Flight Data using Conventional and Neural-based Methods

Saderla¹,

Dhayalan²,

Ghosh³

2016

Def. Sc. Jl.

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The current research paper is an endeavour to estimate the parameters from near stall flight data of manned and unmanned research flight vehicles using conventional and neural based methods. For an aircraft undergoing stall, the aerodynamic model at these high angles of attack becomes non linear due to the influence of unsteady, transient and flow separation phenomena. In order to address these issues the Kirchhoff's flow separation theory was used to incorporate the nonlinearity in the aerodynamic model in terms of flow separation point and stall characteristic parameters. The classical Maximum Likelihood (MLE) method and Neural Gauss-Newton (NGN) method have been employed to estimate the nonlinear parameters of two manned and one unmanned research aircrafts. The estimated static stall parameter and the break point, for the flight vehicles under consideration, were observed to be consistent from both the methods. Moreover the efficacy of the methods is also evident from the consistent estimates of post stall hysteresis time constant. It can also be inferred that the considered quasi steady model is able to adequately capture the drag and pitching moment coefficients in the post stall regime. The confidence in these estimates have been significantly enhanced with the observed lower values of Cramer-Rao bounds. Further the estimated nonlinear parameters were validated by performing a proof of match exercise for the considered flight vehicles. Interestingly the NGN method, which doesn't involve solving equations of motion, was able to perform on a par with the MLE method.

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Longitudinal parameter estimation from real flight data of unmanned cropped delta flat plate configuration

Cited by 13 publications

References 8 publications

Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

ANFIS-Delta method for aerodynamic parameter estimation using flight data

Parameter Estimation from Near Stall Flight Data using Conventional and Neural-based Methods

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