Non-linear aerodynamic modelling of unmanned cropped delta configuration from experimental data

2017 ◽  
Vol 121 (1237) ◽  
pp. 320-340 ◽  
Author(s):  
S. Saderla ◽  
R. Dhayalan ◽  
A.K. Ghosh
Keyword(s):  
Parameter Estimation ◽  
Flow Separation ◽  
Linear Models ◽  
Rectangular Cross Section ◽  
Flight Test ◽  
Point Mapping ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Non Linear ◽  
High Angles Of Attack

ABSTRACTThe paper presents the aerodynamic characterization of a low-speed unmanned aerial vehicle, with cropped delta planform and rectangular cross section, at and around high angles-of-attack using flight test methods. Since the linear models used for identification from flight data at low and moderate angles of attack become unsuitable for accurate parameter estimation at high angles of attack, a non-linear aerodynamic model has to be considered. Therefore, the Kirchhoff's flow separation model was used to incorporate the non-linearity in the aerodynamic model in terms of flow separation point and stall characteristic parameters. The Maximum Likelihood (ML) and Neural Gauss-Newton (NGN) methods were used to perform the parameter estimation on one set of low angle-of-attack and one set of near-stall flight data. It is evident from the estimates that the NGN method, which does not involve solving equations of motion, performs on a par with the classical ML method. This may be attributed to the reason that NGN method uses a neural network which has been trained by performing point to point mapping of the measured flight data. This feature of NGN method enhances its application over a wider envelope of high angles of attack flight data.

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.

Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

2018 ◽  
Vol 122 (1252) ◽  
pp. 889-912
Author(s):  
Subrahmanyam Saderla ◽  
Dhayalan Rajaram ◽  
A. K. Ghosh
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Unmanned Aerial Vehicle ◽  
Rectangular Cross Section ◽  
A Priori ◽  
Flight Data ◽  
Aerodynamic Model ◽  
Estimated Parameters ◽  
Aerial Vehicle ◽  
Cramer Rao Bound

ABSTRACTThe current research paper describes the lateral-directional parameter estimation from flight data of a miniature Unmanned Aerial Vehicle (UAV) using Maximum Likelihood (ML), and Neural-Gauss-Newton (NGN) methods. An unmanned configuration with a cropped delta planform and thin rectangular cross-section has been designed, fabricated and instrumented. Exhaustive full-scale wind-tunnel tests were performed on the UAV to extract the form of aerodynamic model that has to be postulated a priori for parameter estimation. Rigorous flight tests have been performed to acquire the flight data for several prescribed manoeuvres. Four sets of compatible flight data have been used to carry out parameter estimation using classical ML and neural-network-based NGN methods. It is observed that the estimated parameters are consistent and the lower values of the Cramer-Rao bound for the corresponding estimates have shown significant confidence in the obtained parameters. Furthermore, to validate the aerodynamic model used and to enhance the confidence in the estimated parameters, a proof of match exercise has been carried out.

Parameter Estimation from Near Stall Flight Data using Conventional and Neural-based Methods

2016 ◽  
Vol 67 (1) ◽  
pp. 03 ◽  
Author(s):  
S. Saderla ◽  
R. Dhayalan ◽  
A.K. Ghosh
Keyword(s):  
Flow Separation ◽  
Equations Of Motion ◽  
Break Point ◽  
Nonlinear Parameters ◽  
Flight Data ◽  
Flight Vehicles ◽  
Aerodynamic Model ◽  
Separation Theory ◽  
Solving Equations ◽  
High Angles Of Attack

<p>The current research paper is an endeavour to estimate the parameters from near stall flight data of manned and unmanned research flight vehicles using conventional and neural based methods. For an aircraft undergoing stall, the aerodynamic model at these high angles of attack becomes non linear due to the influence of unsteady, transient and flow separation phenomena. In order to address these issues the Kirchhoff’s flow separation theory was used to incorporate the nonlinearity in the aerodynamic model in terms of flow separation point and stall characteristic parameters. The classical Maximum Likelihood (MLE) method and Neural Gauss-Newton (NGN) method have been employed to estimate the nonlinear parameters of two manned and one unmanned research aircrafts. The estimated static stall parameter and the break point, for the flight vehicles under consideration, were observed to be consistent from both the methods. Moreover the efficacy of the methods is also evident from the consistent estimates of post stall hysteresis time constant. It can also be inferred that the considered quasi steady model is able to adequately capture the drag and pitching moment coefficients in the post stall regime. The confidence in these estimates have been significantly enhanced with the observed lower values of Cramer-Rao bounds. Further the estimated nonlinear parameters were validated by performing a proof of match exercise for the considered flight vehicles. Interestingly the NGN method, which doesn’t involve solving equations of motion, was able to perform on a par with the MLE method.</p>

scholarly journals Efficient estimation of bounded gradient-drift diffusion models for affect on CPU and GPU

2021 ◽  
Author(s):  
Tim Loossens ◽  
Kristof Meers ◽  
Niels Vanhasbroeck ◽  
Nil Anarat ◽  
Stijn Verdonck ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Continuous Time ◽  
Graphics Processing Units ◽  
Linear Models ◽  
Likelihood Function ◽  
Efficient Estimation ◽  
Closed Form Expression ◽  
Central Processing ◽  
Research Fields ◽  
Non Linear

AbstractComputational modeling plays an important role in a gamut of research fields. In affect research, continuous-time stochastic models are becoming increasingly popular. Recently, a non-linear, continuous-time, stochastic model has been introduced for affect dynamics, called the Affective Ising Model (AIM). The drawback of non-linear models like the AIM is that they generally come with serious computational challenges for parameter estimation and related statistical analyses. The likelihood function of the AIM does not have a closed form expression. Consequently, simulation based or numerical methods have to be considered in order to evaluate the likelihood function. Additionally, the likelihood function can have multiple local minima. Consequently, a global optimization heuristic is required and such heuristics generally require a large number of likelihood function evaluations. In this paper, a Julia software package is introduced that is dedicated to fitting the AIM. The package includes an implementation of a numeric algorithm for fast computations of the likelihood function, which can be run both on graphics processing units (GPU) and central processing units (CPU). The numerical method introduced in this paper is compared to the more traditional Euler-Maruyama method for solving stochastic differential equations. Furthermore, the estimation software is tested by means of a recovery study and estimation times are reported for benchmarks that were run on several computing devices (two different GPUs and three different CPUs). According to these results, a single parameter estimation can be obtained in less than thirty seconds using a mainstream NVIDIA GPU.

Drag Coefficient Identification from Flight Data via Optimal Observer

2014 ◽  
Vol 687-691 ◽  
pp. 787-790
Author(s):  
Rong Jun Yang ◽  
Yao Ye
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Drag Coefficient ◽  
Dynamic Equation ◽  
Unscented Kalman Filter ◽  
Flight Test ◽  
Measurement Noise ◽  
Estimation Technique ◽  
Flight Data ◽  
Coefficient Identification

. For effectively using flight test data to extract drag coefficient, an optimal observer based on parameter estimation technique is proposed. The point mass dynamic equation is used to form the Unscented Kalman Filter (UKF) and the smoother (URTSS) for the estimation of a projectile’s flight states. The projectile flight states are then solved and utilized to extract the drag coefficient information using the observer techniques. The simulation verifies the feasibility of the method: with measurement noise, the accurate drag coefficient is obtained by using the smoother.

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.

G Optimal Design in Non linear Models to Increase Silicon Oxide Purity Levels and Electrical Conductivity

2018 ◽  
pp. 150-155
Author(s):  
Muklas Rivai
Keyword(s):  
Electrical Conductivity ◽  
Optimal Design ◽  
Linear Models ◽  
Silicon Oxide ◽  
Information Matrix ◽  
Relevant Information ◽  
Non Linear

Optimal design is a design which required in determining the points of variable factors that would be attempted to optimize the relevant information so that fulfilled the desired criteria. The optimal fulfillment criteria based on the information matrix of the selected model.

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