ANFIS-Delta method for aerodynamic parameter estimation using flight data

Kumar, Ajit; Ghosh, Arnab

doi:10.1177/0954410018791621

Cited by 23 publications

(14 citation statements)

References 22 publications

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“…Uçakların aerodinamik modellenmesi ve parametrelerinin tahmini üzerine yapılan bir çalışmada ise ANFIS-tabanlı parçacık sürü optimizasyon algoritması kullanılmıştır [13]. Diğer bir çalışmada da uçuş verileri kullanılarak ANFIS-DELTA yöntemiyle aerodinamik parametrelerin tahmini yapılmıştır [14]. Konar ve Bağiş' ın yaptığı çalışmada ise uçuş kontrol sisteminin hız parametresinin ANFIS ile belirlenmesi üzerinde durulmuştur [15].…”

Section: Introductionunclassified

Uçağin Tri̇m Parametresi̇ni̇n Anfis Kullanilarak Tahmi̇n Edi̇lmesi̇

Dalkıran¹,

Yildirim²

2020

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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Uçaklar, hızlı, güvenli ve konforlu olmalarından dolayı ulaşım sektöründe önemli bir yere sahiptir. Uçaklar, en çok kalkış ve iniş esnasında, uçuş konfor ve güvenliğini etkileyen hava olaylarına ve dış etmenlere maruz kalmaktadır. Bu etmenlerin meydana getirdiği sarsıntı ve salınımları önlemek, uçağın denge ve kararlılığını korumak için yatay stabilize ile trim verilir. Bu trim değerinin belirlenmesi, uçuş konforu için önemlidir. Bu çalışmada uçağın trim değerinin uyarlamalı ağ tabanlı bulanık çıkarım sistemi (Adaptive Network Based Fuzzy Inference System, ANFIS) ile tahmini üzerinde durulmuştur. Oluşturulan ANFIS modellerinde 4 farklı üyelik fonksiyonu kullanılarak, eğitim ve test işlemleri yapılmıştır. Bu eğitim ve test işlemlerinde, Airbus A319 uçağının kara kutusundan alınan gerçek uçuş verileri kullanılmıştır. Bu çalışmada ANFIS kullanılarak yapılan modellemelerin, gerçek trim değerinin tahmininde başarılı olduğu görülmüştür.Aircrafts have an important role in transportation because they are fast, safe and comfortable. Aircrafts are mostly exposed to weather conditions and external cases affecting flight comfort and safety during take-off and landing. To prevent shocks and oscillations caused by these cases and to maintain the balance and stability of an aircraft, horizontal stabilization is adjusted using trim value. Determination of suitable trim value is important for flight comfort. In this study, the prediction of the trim value of an Airbus A319 using the Adaptive Network-Based Fuzzy Inference System (ANFIS) is emphasized. Different ANFIS models are trained and tested using four different membership functions. Actual flight data taken from black box of an Airbus A319 are used in these training and test procedures. In this study, it is shown that the models designed using ANFIS have been successful to predict real trim values.

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Section: Introductionunclassified

Uçağin Tri̇m Parametresi̇ni̇n Anfis Kullanilarak Tahmi̇n Edi̇lmesi̇

Dalkıran¹,

Yildirim²

2020

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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“…A non-linear least-square-based optimisation methodology was also investigated in the field of parameter estimation, (18) and the parameters of a flight manoeuvre at a high angle-of-attack regime were computed (19,20) . Variants of FFNN such as radial basis function neural network (RBFNN), recurrent neural network (RNN) and adaptive neuro-fuzzy inference system (ANFIS) were also applied in the non-linear mapping of the aerodynamic forces and moments to estimate the aerodynamic stability and control derivatives (21)(22)(23)(24) . It is evident from the investigation carried out using the conventional FFNN and its variants mentioned above that the training of FFNN is a cumbersome task due to the number of epochs, slow convergence of learning strategies and trapping of the error function in local minima.…”

Section: Introductionmentioning

confidence: 99%

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

Verma

Peyada

2019

Aeronaut. j.

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The 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.

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“…Some of the methods which are an integral part of this community are neural-network, fuzzy-logic, neuro-fuzzy, and genetic algorithm based methods. The methods, which are part of the community of data-driven methods, do experience the challenge of training the network and supplemental complexity during the genesis of logic [15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method

Srivastava¹,

Kumar²,

Ghosh³

2019

AJETM

Self Cite

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This paper presents the estimation of longitudinal aerodynamic parameters by using Genetic Algorithm (GA) optimized method from simulated and real flight data of ATTAS aircraft. The simulated flight data is deliberately contaminated with 5%, 10%, and 15% of random noise for creating flight data, which bears similarity to real flight data. The proposed methodology utilizes the general notion of output error method, i.e., minimizing the response error between the measured response and estimated response, and the genetic algorithm as the optimization technique for an iterative update of the parameter vector. The longitudinal parameters are estimated by using the proposed method from both simulated data (without and with random noise) and real flight data. The parameter estimates obtained by using the proposed method is compared with the estimates from the Maximum-Likelihood method and data-driven methods viz. Delta method and GPR -Delta method for assessing the efficacy of the methodology. The statistical analysis of the parameter estimates has further cemented the confidence in the estimates obtained by using the proposed method.

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ANFIS-Delta method for aerodynamic parameter estimation using flight data

Cited by 23 publications

References 22 publications

Uçağin Tri̇m Parametresi̇ni̇n Anfis Kullanilarak Tahmi̇n Edi̇lmesi̇

Uçağin Tri̇m Parametresi̇ni̇n Anfis Kullanilarak Tahmi̇n Edi̇lmesi̇

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

Estimation of Longitudinal Aerodynamic Derivatives Using Genetic Algorithm Optimized Method

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