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Publication date: 5 March 2018

Dhayalan R., Subrahmanyam Saderla and Ajoy Kanti Ghosh

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…

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Abstract

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.

Details

Aircraft Engineering and Aerospace Technology, vol. 90 no. 2
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 4 January 2016

Subrahmanyam Saderla, Dhayalan R and Ajoy Kanti Ghosh

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…

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Abstract

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.

Details

International Journal of Intelligent Unmanned Systems, vol. 4 no. 1
Type: Research Article
ISSN: 2049-6427

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