Parameter estimation of UAV from flight data using neural network

2018 ◽  
Vol 90 (2) ◽  
pp. 302-311 ◽  
Author(s):  
Dhayalan R. ◽  
Subrahmanyam Saderla ◽  
Ajoy Kanti Ghosh
Keyword(s):  
Parameter Estimation ◽  
Design Methodology ◽  
Maximum Likelihood Method ◽  
Estimation Method ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Process Noise ◽  
Content Type ◽  
Flight Data ◽  
Aerial Vehicle

Purpose The purpose of this paper is to present the application of the neural-based estimation method, Neural-Gauss-Newton (NGN), using the real flight data of a small unmanned aerial vehicle (UAV). Design/methodology/approach The UAVs in general are lighter in weight and their flight is usually influenced by the atmospheric winds because of their relatively lower cruise speeds. During the presence of the atmospheric winds, the aerodynamic forces and moments get modified significantly and the accurate mathematical modelling of the same is highly challenging. This modelling inaccuracy during parameter estimation is routinely treated as the process noise. Furthermore, because of the limited dimensions of the small UAVs, the measurements are usually influenced by the disturbances caused by other subsystems. To handle these measurement and process noises, the estimation methods based on neural networks have been found reliable in the manned aircrafts. Findings Six sets of compatible longitudinal flight data of the designed UAV have been chosen to estimate the parameters using the NGN method. The consistency in the estimates is verified from the obtained mean and the standard deviation and the same has been validated by the proof-of-match exercise. It is evident from the results that the NGN method was able to perform on a par with the conventional maximum likelihood method. Originality/value This is a partial outcome of the research carried out in estimating parameters from the UAVs.

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

2016 ◽  
Vol 4 (1) ◽  
pp. 2-22 ◽  
Author(s):  
Subrahmanyam Saderla ◽  
Dhayalan R ◽  
Ajoy Kanti Ghosh
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Rectangular Cross Section ◽  
Flight Test ◽  
Least Square ◽  
Likelihood Method ◽  
Content Type ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Estimated Parameters

Purpose – The purpose of this paper is to describe the longitudinal aerodynamic characterization of an unmanned cropped delta configuration from real flight data. In order to perform this task an unmanned configuration with cropped delta planform and rectangular cross-section has been designed, fabricated, instrumented and flight tested at flight laboratory in Indian Institute of Technology Kanpur (IITK), India. Design/methodology/approach – As a part of flight test program a real flight database, through various maneuvers, have been generated for the designed unmanned configuration. A dedicated flight data acquisition system, capable of onboard logging and telemetry to ground station, has been used to record the flight data during these flight test experiments. In order to identify the systematic errors in the measurements, the generated flight data has been processed through data compatibility check. Findings – It is observed from the flight path reconstruction that the obtained biases are negligible and the scale factors are almost close to unity. The linear aerodynamic model along with maximum likelihood and least-square methods have been used to perform the parameter estimation from the obtained compatible flight data. The lower values of Cramer-Rao bounds obtained for various parameters has shown significant confidence in the estimated parameters using maximum likelihood method. In order to validate the aerodynamic model used and to increase the confidence in the estimated parameters a proof-of-match exercise has been carried out. Originality/value – The entire work presented is original and all the experiments have been carried out in Flight laboratory of IITK.

A novel parameter estimation method for the Weibull distribution on heavily censored data

2019 ◽  
pp. 1748006X1988764 ◽  
Author(s):  
Renyan Jiang
Keyword(s):  
Parameter Estimation ◽  
Censored Data ◽  
Estimation Method ◽  
Least Square ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Distribution Models ◽  
Data Set ◽  
Hybrid Censoring ◽  
Parameter Estimation Method

It is desired to build the life distribution models of critical components (which are assumed to be non-repairable) of a repairable system as early as possible based on field failure data in order to optimize the operation and maintenance decisions of the components. When the number of the systems under observation is large and the observation duration is relatively short, the samples obtained for modeling are large and heavily censored. For such samples, the classical parameter estimation methods (e.g. maximum likelihood method and least square method) do not provide robust estimates. To address this issue, this article develops a hybrid censoring index to quantitatively describe censoring characteristics of a data set, proposes a novel parameter estimation method based on information extracted from censored observations, and evaluates the accuracy and robustness of the proposed method through a numerical experiment. Its applicable range in terms of the hybrid censoring index is determined through an accuracy analysis. The experiment results show that the proposed approach provides much accurate estimates than the classical methods for heavily censored data. A real-world example is also included.

Aircraft parameter estimation using a new filtering technique based upon a neural network and Gauss-Newton method

2009 ◽  
Vol 113 (1142) ◽  
pp. 243-252 ◽  
Author(s):  
N. K. Peyada ◽  
A. K. Ghosh
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Dynamic Model ◽  
Newton Method ◽  
Initial Conditions ◽  
Estimation Method ◽  
Wind Tunnel Test ◽  
Neural Model ◽  
Estimation Methods ◽  
Flight Data

Abstract A new parameter estimation method based upon neural network is proposed. The method proposed here uses feed forward neural networks to establish a neural model that could be used to predict subsequent time histories given the suitable measured initial conditions. The proposed neural model would not represent a generic flight dynamic model. The neural model in this case develops point to point fitting of the input and the output data. Thus, it could at best be referred to as flight dynamic model in restricted sense. Gauss-Newton method is then used to obtain optimal values of the aerodynamic parameters by minimising a suitable defined error cost function. The method has been validated using longitudinal and lateral-directional flight data of various test aircraft. The results thus obtained were compared with those obtained through wind tunnel test, or those obtained using Maximum likelihood and/or Filter error methods. Unlike, most of the parameter estimation methods, the proposed method does not require a prior description of the model. It also bypasses the requirement of solving equations of motion. This feature of the proposed method may have special significance in handling flight data of an unstable aircraft.

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.

scholarly journals Selection of Efficient Parameter Estimation Method for Two-Parameter Weibull Distribution

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Mohammed M. A. Almazah ◽  
Muhammad Ismail
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Mean Square Error ◽  
Estimation Method ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Model Parameters ◽  
Mean Square ◽  
Two Parameter

Several studies have considered various scheduling methods and reliability functions to determine the optimum maintenance time. These methods and functions correspond to the lowest cost by using the maximum likelihood estimator to evaluate the model parameters. However, this paper aims to estimate the parameters of the two-parameter Weibull distribution (α, β). The maximum likelihood estimation method, modified linear exponential loss function, and Wyatt-based regression method are used for the estimation of the parameters. Minimum mean square error (MSE) criterion is used to evaluate the relative efficiency of the estimators. The comparison of the different parameter estimation methods is conducted, and the efficiency of these methods is observed, both mathematically and experimentally. The simulation study is conducted for comparison of samples sizes (10, 50, 100, 150) based on the mean square error (MSE). It is concluded that the maximum likelihood method was found to be the most efficient method for all sample sizes used in the research because it achieved the least MSE compared with other methods.

scholarly journals A New Probability Distribution for Modeling Failure and Service Times: Properties, Copulas and Various Estimation Methods

2021 ◽  
Vol 9 (3) ◽  
pp. 555-586
Author(s):  
Hanaa Elgohari ◽  
Mohamed Ibrahim ◽  
Haitham Yousof
Keyword(s):  
Maximum Likelihood ◽  
Bayesian Estimation ◽  
Maximum Likelihood Method ◽  
Estimation Method ◽  
Real Data ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Finite Sample ◽  
Simulation Experiments ◽  
Mean Squared Errors

In this paper, a new generalization of the Pareto type II model is introduced and studied. The new density canbe “right skewed” with heavy tail shape and its corresponding failure rate can be “J-shape”, “decreasing” and “upside down (or increasing-constant-decreasing)”. The new model may be used as an “under-dispersed” and “over-dispersed” model. Bayesian and non-Bayesian estimation methods are considered. We assessed the performance of all methods via simulation study. Bayesian and non-Bayesian estimation methods are compared in modeling real data via two applications. In modeling real data, the maximum likelihood method is the best estimation method. So, we used it in comparing competitive models. Before using the the maximum likelihood method, we performed simulation experiments to assess the finite sample behavior of it using the biases and mean squared errors.

scholarly journals Unlimited and protected memory for flight data recorders

2016 ◽  
Vol 88 (6) ◽  
pp. 866-872 ◽  
Author(s):  
Yair Wiseman
Keyword(s):  
Design Methodology ◽  
Memory Device ◽  
Social Implications ◽  
Embedded Memory ◽  
Content Type ◽  
Flight Data ◽  
Embedded Device ◽  
Use Of Internet ◽  
Practical Implications

Purpose The purpose of this paper is to study extensive enlargement and safety of flight data recorder memory. Design/methodology/approach The study involves the moving the memory of flight data recorders from an internal embedded device to a cloud. Findings The implementation has made the embedded memory device of flight data recorder effectively unlimited, and, hence, much more information can be stored. Research limitations/implications The possibility of a flight data recorder to be damaged or lost in a crash is not so high, but the implementation can be very helpful in cases such as aerial disappearances. Practical implications The implication is larger and protected memory for flight data recorders. Social implications Finding reasons for crashes is faster, and immediate actions can be taken to find remedy to the failures. Originality/value The use of internet and cellphones in airplanes is nothing special at present. It is suggested to take this technology for flight data recorders as well.

Dynamic endogeneity and corporate governance-performance relationship

2017 ◽  
Vol 44 (5) ◽  
pp. 727-744
Author(s):  
Sujani Thrikawala ◽  
Stuart Locke ◽  
Krishna Reddy
Keyword(s):  
Corporate Governance ◽  
National Level ◽  
Estimation Method ◽  
Estimation Methods ◽  
Governance System ◽  
Past Performance ◽  
Gmm Estimation ◽  
Content Type ◽  
Dynamic Framework

Purpose The purpose of this paper is to examine the relationship between corporate governance (CG) and microfinance institution (MFI) performance, using a dynamic panel generalised method of moments (GMM) estimator to mitigate the serious issues with endogeneity. Design/methodology/approach Inconsistent findings and a general lack of empirical results for the microfinance industry leave an unclear message regarding the impacts of CG on MFI performance, especially in emerging economies. The authors use GMM estimation techniques to examine whether CG has an influence on MFI performance. Findings This study confirms that the MFIs’ contemporaneous performance and CG characteristics are statistically significantly positively linked with their past performance. This study finds statistically significant governance effects on MFI performance, including the presence of international directors and/or donor representatives on the board, client representatives on the board, percentage of non-executive directors and the quality of the national governance system. Practical implications These findings provide some insights for policy-makers and practitioners to develop suitable policies and guidelines to streamline MFIs’ operations in emerging countries. Moreover, national and international investors and donors may use these finding as a benchmark for their investment and funding decisions. Originality/value This paper is the first to estimate the CG and performance relationship of MFIs in a dynamic framework by applying the GMM estimation method. This approach improves upon traditional estimation methods by controlling the likely sources of endogeneity. Further, this paper examines whether quality of national-level governance characteristics is related to performance measures of profitability and outreach of MFIs.

Autopilot design for an aircraft by using Luenberger observer design

2018 ◽  
Vol 90 (5) ◽  
pp. 858-868 ◽  
Author(s):  
Muhammad Taimoor ◽  
Li Aijun ◽  
Rooh ul Amin ◽  
Hongshi Lu
Keyword(s):  
Design Methodology ◽  
Research Work ◽  
Linear Quadratic Regulator ◽  
Observer Design ◽  
Linear Quadratic ◽  
Content Type ◽  
Luenberger Observer ◽  
Level Flight ◽  
Aerial Vehicle ◽  
The Cost

Purpose The purpose of this paper is to design linear quadratic regulator (LQR) based Luenberger observer for the estimation of unknown states of aircraft. Design/methodology/approach In this paper, the LQR-based Luenberger observer is deliberated for autonomous level flight of unmanned aerial vehicle (UAV) which has been attained productively. Various modes like phugoid and roll modes are exploited for controlling the rates of UAV. The Luenberger observer is exploited for estimation of the mysterious states of the system. The rates of roll, yaw and pitch are used as an input to the observer, while the remaining states such as velocities and angles have been anticipated. The main advantage of using Luenberger observer was to reduce the cost of the system which has been achieved lucratively. The Luenberger observer proposes sturdiness at the rate of completion to conquest over the turmoil and insecurities to overcome the privileged recital. The FlightGear simulator is exploited for the endorsement of the recital of the Luenberger observer-based autopilot. The level flight has been subjugated lucratively and has been legitimated by exploiting the FlightGear simulator. The authenticated and the validated results are offered in this paper. Microsoft Visual Studio has been engaged as a medium between the MATLAB and FlightGear Simulator. Findings The suggested observer based on LQR ensures the lucrative approximation of the unknown states of the system as well as the successful level flight of the system. The Luenberger observer is used for approximation of states while LQR is used as controller. Originality/value In this research work, not only the estimation of unknown states of both longitudinal and lateral model is made but also the level flight is achieved by using those estimated states and the autopilot is validated by using the FlightGear, while in most of the research work only the estimation is made of only longitudinal or lateral model.

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