Estimation of the longitudinal aerodynamic parameters from flight data for the NASA F/A-18 HARV

1996 ◽  
Author(s):  
Marcello Napolitano ◽  
Alfonso Paris ◽  
Brad Seanor ◽  
Albion Bowers
Keyword(s):  
Flight Data ◽  
Aerodynamic Parameters

A hybrid frequency-time domain technique for ground effect identification

2010 ◽  
Vol 114 (1156) ◽  
pp. 377-385
Author(s):  
A. Vitale ◽  
N. Genito ◽  
L. Garbarino ◽  
U. Ciniglio ◽  
F. Corraro
Keyword(s):  
Time Domain ◽  
Flight Control ◽  
Ground Effect ◽  
Model Parameters ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Hybrid Frequency ◽  
Effect Model ◽  
Aerodynamic Derivatives ◽  
Aerodynamic Parameters

Abstract The estimation from flight data of aerodynamic parameters for vehicle in steady-state conditions, perturbed by an identification manoeuvre, is a well-established technology, whereas system identification from dynamic flight data is a subject of continuous interest. This paper presents a hybrid frequency and time domain technique for identification of vehicle longitudinal aerodynamic model, including the ground effect. Identification is performed in the framework of a multi-step approach, in which, first aerodynamic coefficients are estimated in the frequency domain, using an equation error method; then time domain techniques are applied to identify out of ground effect aerodynamic derivatives and ground effect model parameters. The technique was successfully applied to flight data of an experimental ultra light aircraft. Identification results showed that the proposed method works properly also in the dynamic phases of the flight or when no dedicated identification manoeuvres are executed. Moreover, the identified longitudinal aerodynamic model was used to design the flight control system that successfully performed many autonomous landings.

scholarly journals Fuel Consumption Model of the Climbing Phase of Departure Aircraft Based on Flight Data Analysis

2019 ◽  
Vol 11 (16) ◽  
pp. 4362 ◽  
Author(s):  
Zhang ◽  
Huang ◽  
Liu ◽  
Zhang
Keyword(s):  
Wind Tunnel ◽  
Fuel Consumption ◽  
Operating Cost ◽  
Fuel Load ◽  
Accurate Estimation ◽  
Flight Data ◽  
Proposed Model ◽  
Aircraft Performance ◽  
Aerodynamic Parameters ◽  
Consumption Model

Accurate estimation of the fuel consumed during aircraft operation is key for determining the fuel load, reducing the airline operating cost, and mitigating environmental impacts. Aerodynamic parameters in current fuel consumption models are obtained from a static diagram extracted from the outcomes of wind tunnel experiments. Given that these experiments are performed in a lab setting, the parameters cannot be used to estimate additional fuel consumption caused by aircraft performance degradation. In addition, wind tunnel experiment results rarely involve the influence of crosswind on fuel consumption; thus, the results could be inaccurate when compared with field data. This study focuses on the departure climbing phase of aircraft operation and proposes a new fuel consumption model. In this model, the relationships between aerodynamic parameters are extracted by fitting quick access recorder (QAR) actual flight data, and the crosswind effect is also considered. Taking QAR data from two airports in China, the accuracy of the proposed model and its transferability are demonstrated. Applying the proposed model, the fuel saving of a continuous climb operation (CCO) compared with the traditional climb operation is further quantified. Finally, how aircraft mass, climbing angle, and different aircraft models could affect the fuel consumption of the climbing phase of aircraft operation is investigated. The proposed fuel consumption model fills gaps in the existing literature, and the method can be used for developing specific fuel consumption models for more aircraft types at other airports.

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.

Parameter estimation of aircraft using extreme learning machine and Gauss-Newton algorithm

2019 ◽  
Vol 124 (1272) ◽  
pp. 271-295 ◽  
Author(s):  
H. O. Verma ◽  
N. K. Peyada
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Computational Cost ◽  
Feedforward Neural Network ◽  
Estimation Methods ◽  
Flight Data ◽  
Regression Methods ◽  
Directional Motion ◽  
Aerodynamic Parameters ◽  
Learning Machine

ABSTRACTThe research paper addresses the problem of estimating aerodynamic parameters using a Gauss-Newton-based optimisation method. The process of the optimisation method lies on the principle of minimising the residual error between the measured and simulated responses of the system. Usually, the simulated response is obtained by integrating the dynamic equations of the system, which is found to be susceptible to the initial values, and the integration method. With the advent of the feedforward neural network, the data-driven regression methods have been widely used for identification of the system. Among them, a variant of feedforward neural network, extreme learning machine, which has proven the performance in terms of computational cost, generalisation, and so forth, has been addressed to predict the responses in the present study. The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters. Furthermore, the estimates have been validated with the values of the classical estimation methods, such as the equation-error and filter-error methods. The sample standard deviations of the estimates demonstrate the effectiveness of the proposed method. Lastly, the proof-of-match exercise has been conducted with the other set of flight data to validate the estimated parameters.

Aircraft parameter estimation using ELM network

2020 ◽  
Vol 92 (6) ◽  
pp. 895-907
Author(s):  
Hari Om Verma ◽  
Naba Kumar Peyada
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Mathematical Formulation ◽  
Estimation Methods ◽  
Content Type ◽  
Flight Data ◽  
The Real ◽  
Aerodynamic Parameters ◽  
Hidden Layer ◽  
Error Method

Purpose The purpose of this paper is to investigate the estimation methodology with a highly generalized cost-effective single hidden layer neural network. Design/methodology/approach The aerodynamic parameter estimation is a challenging research area of aircraft system identification, which finds various applications such as flight control law design and flight simulators. With the availability of the large database, the data-driven methods have gained attention, which is primarily based on the nonlinear function approximation using artificial neural networks. A novel single hidden layer feed-forward neural network (FFNN) known as extreme learning machine (ELM), which overcomes the issues such as learning rate, number of epochs, local minima, generalization performance and computational cost, as encountered in the conventional gradient learning-based FFNN has been used for the nonlinear modeling of the aerodynamic forces and moments. A mathematical formulation based on the partial differentiation is proposed to estimate the aerodynamic parameters. Findings The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters using the proposed methodology. The efficacy of the estimates is verified with the results obtained through the conventional parameter estimation methods such as the equation-error method and filter-error method. Originality/value The present study is an outcome of the research conducted on ELM for the estimation of aerodynamic parameters from the real flight data. The proposed method is capable to estimate the parameters in the presence of noise.

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