Parameter Estimation of Stable and Unstable Aircraft using Extreme Learning Machine

Verma, Hari Om; Peyada, N. K.

doi:10.2514/6.2018-0526

Cited by 6 publications

(8 citation statements)

References 15 publications

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“…The raw flight data went through the data compatibility check to improve its quality in terms of biases, scale factors, etc. The flight data contains the motion and control variables, such as etc., whereas the other necessary variables and the coefficients of forces and moments are derived by using the measured quantities and geometrical values (37) . For the modelling of C D , input variables α and δ e are considered, whereas are considered for the modelling of the coefficients C L and C m .…”

Section: Resultsmentioning

confidence: 99%

“…The data contain the essential motion and control variables, such as , gathered at a sample rate of 0.04 seconds. The aerodynamic force and moment coefficients for postulation of an aerodynamic model are computed from the measured and geometrical quantities (37) . The aerodynamic model, whose parameters must be determined, is expressed as follows: …”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

Verma

Peyada

2019

Aeronaut. j.

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“…In the flight test, the longitudinal state quantity is not strictly equal to 0, so the longitudinal variables u, w, q, u are also considered in the lateral motion equations ( 29) and ( 30), where the inertia and dimension parameters are as follows: m = 17,631 kg, J xx = 133,367 kg m 2 , J zz = 359,593 kg m 2 , J xz = 1.14420 kg m 2 , S = 64.0 m 2 , b = 10.75 m and these longitudinal variables are replaced by the measurements in flight test. Of course, the amplitudes of these longitudinal variables are small so the longitudinal state variables can be omitted directly, just as treated in by Verma and Peyada (2018):…”

Section: Extracting Longitudinal Parameters From Simulation Datamentioning

confidence: 99%

Aerodynamic coefficients modeling using Levenberg–Marquardt algorithm and network

Wang

et al. 2021

AEAT

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Purpose The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm. Design/methodology/approach Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives. Findings Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data. Research limitations/implications The proposed method requires iterative calculation and can only identify parameters offline. Practical implications The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems. Originality/value In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

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“…The data carries the motion and control variables such as β , ϕ , ψ , p , r , a y , δ a and δ r . The other quantities such as C Y , C l and C n are derived by using the measured and geometrical values for the specific case of the parameter estimation approach (Verma and Peyada, 2018).…”

Section: Estimation Of Lateral-directional Aerodynamic Parametersmentioning

confidence: 99%

“…Using such a concept, the ELM network provides a better nonlinear mapping for the chosen input-output data set with minimum computational time and the smallest norm of the weights (Bartlett, 1998). Therefore, the ELM network has gained research attention in solving the real-world problems of forecasting, classifications, pattern recognition, etc., because of the faster learning and prediction and the superior generalization performance (Pal, 2009; Zong and Huang, 2011; Zhao et al , 2013; Verma and Peyada, 2017, 2018).…”

Section: Introductionmentioning

confidence: 99%

Aircraft parameter estimation using ELM network

Verma

Peyada

2020

AEAT

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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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Parameter Estimation of Stable and Unstable Aircraft using Extreme Learning Machine

Cited by 6 publications

References 15 publications

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

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

Aerodynamic coefficients modeling using Levenberg–Marquardt algorithm and network

Aircraft parameter estimation using ELM network

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