A GPR Based Novel Approach for Aerodynamic Parameter Estimation from Flight Data

2018 ◽  
Vol 11 (6) ◽  
pp. 252
Author(s):  
Ajit Kumar ◽  
Ajoy Kanti Ghosh
Keyword(s):  
Parameter Estimation ◽  
Aerodynamic Parameter ◽  
Flight Data ◽  
Novel Approach

ANFIS-Delta method for aerodynamic parameter estimation using flight data

2018 ◽  
Vol 233 (8) ◽  
pp. 3016-3032 ◽  
Author(s):  
Ajit Kumar ◽  
Ajoy Kanti Ghosh
Keyword(s):  
Fuzzy Inference ◽  
Simulated Data ◽  
Delta Method ◽  
Feed Forward Neural Network ◽  
Aerodynamic Parameter ◽  
Inference System ◽  
Flight Data ◽  
Novel Approach ◽  
Neuro Fuzzy ◽  
Aerodynamic Parameters

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.

Inflight Aerodynamic Parameter Estimation for Fixed Wing Unmanned Aerial Vehicle

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
R. Jaganraj ◽  
R. Velu
Keyword(s):  
Parameter Estimation ◽  
Unmanned Aerial Vehicle ◽  
Aerodynamic Parameter ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Aerial Vehicle ◽  
Frame Work ◽  
Aerodynamic Parameters

This paper presents the frame work for aerodynamic parameter estimation for small fixed wing unmanned aerial vehicle (UAV). The recent development in autopilot hardware for small UAV enables the in-flight data collection of flight characteristics. A methodology is outlined to collect, process and arrive at a conclusion from the in-flight data using commercial flight controller of under 2kg (micro) fixed wing aircraft, ‘VAF01’ for which a Fault Detection and Identification (FDI) system is under development. As a part of the FDI, the linear longitudinal (3 DOF) aerodynamic model is developed and in-flight experimental data is used to estimate the longitudinal aerodynamic parameters. The Flight Path Reconstruction is completed with the acquired parameters from in-flight experiments and results are discussed for further utilization of them.

GPR-based novel approach for non-linear aerodynamic modelling from flight data

2018 ◽  
Vol 123 (1259) ◽  
pp. 79-92
Author(s):  
A. Kumar ◽  
A. K. Ghosh
Keyword(s):  
Aerodynamic Force ◽  
Equations Of Motion ◽  
Gaussian Process Regression ◽  
Likelihood Estimation ◽  
Flight Data ◽  
Novel Approach ◽  
Non Linear ◽  
Research Aircraft ◽  
Novel Method ◽  
Aerodynamic Modelling

ABSTRACTIn this paper, a Gaussian process regression (GPR)-based novel method is proposed for non-linear aerodynamic modelling of the aircraft using flight data. This data-driven regression approach uses the kernel-based probabilistic model to predict the non-linearity. The efficacy of this method is examined and validated by estimating force and moment coefficients using research aircraft flight data. Estimated coefficients of aerodynamic force and moment using GPR method are compared with the estimated coefficients using maximum-likelihood estimation (MLE) method. Estimated coefficients from the GPR method are statistically analysed and found to be at par with estimated coefficients from MLE, which is popularly used as a conventional method. GPR approach does not require to solve the complex equations of motion. GPR further can be directed for the generalised applications in the area of aeroelasticity, load estimation, and optimisation.

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