Tugrul Oktay, Harun Celik and Ilke Turkmen
The purpose of this paper is to examine the success of constrained control on reducing motion blur which occurs as a result of helicopter vibration.
Abstract
Purpose
The purpose of this paper is to examine the success of constrained control on reducing motion blur which occurs as a result of helicopter vibration.
Design/methodology/approach
Constrained controllers are designed to reduce the motion blur on images taken by helicopter. Helicopter vibrations under tight and soft constrained controllers are modeled and added to images to show the performance of controllers on reducing blur.
Findings
The blur caused by vibration can be reduced via constrained control of helicopter.
Research limitations/implications
The motion of camera is modeled and assumed same as the motion of helicopter. In model of exposing image, image noise is neglected, and blur is considered as the only distorting effect on image.
Practical implications
Tighter constrained controllers can be implemented to take higher quality images by helicopters.
Social implications
Recently, aerial vehicles are widely used for aerial photography. Images taken by helicopters mostly suffer from motion blur. Reducing motion blur can provide users to take higher quality images by helicopters.
Originality/value
Helicopter control is performed to reduce motion blur on image for the first time. A control-oriented and physic-based model of helicopter is benefited. Helicopter vibration which causes motion blur is modeled as blur kernel to see the effect of helicopter vibration on taken images. Tight and soft constrained controllers are designed and compared to denote their performance in reducing motion blur. It is proved that images taken by helicopter can be prevented from motion blur by controlling helicopter tightly.
Details
Keywords
Tugrul Oktay, Seda Arik, Ilke Turkmen, Metin Uzun and Harun Celik
The aim of this paper is to redesign of morphing unmanned aerial vehicle (UAV) using neural network for simultaneous improvement of roll stability coefficient and maximum…
Abstract
Purpose
The aim of this paper is to redesign of morphing unmanned aerial vehicle (UAV) using neural network for simultaneous improvement of roll stability coefficient and maximum lift/drag ratio.
Design/methodology/approach
Redesign of a morphing our UAV manufactured in Faculty of Aeronautics and Astronautics, Erciyes University is performed with using artificial intelligence techniques. For this purpose, an objective function based on artificial neural network (ANN) is obtained to get optimum values of roll stability coefficient (Clβ) and maximum lift/drag ratio (Emax). The aim here is to save time and obtain satisfactory errors in the optimization process in which the ANN trained with the selected data is used as the objective function. First, dihedral angle (φ) and taper ratio (λ) are selected as input parameters, C*lβ and Emax are selected as output parameters for ANN. Then, ANN is trained with selected input and output data sets. Training of the ANN is possible by adjusting ANN weights. Here, ANN weights are adjusted with artificial bee colony (ABC) algorithm. After adjusting process, the objective function based on ANN is optimized with ABC algorithm to get better Clβ and Emax, i.e. the ABC algorithm is used for two different purposes.
Findings
By using artificial intelligence methods for redesigning of morphing UAV, the objective function consisting of C*lβ and Emax is maximized.
Research limitations/implications
It takes quite a long time for Emax data to be obtained realistically by using the computational fluid dynamics approach.
Practical implications
Neural network incorporation with the optimization method idea is beneficial for improving Clβ and Emax. By using this approach, low cost, time saving and practicality in applications are achieved.
Social implications
This method based on artificial intelligence methods can be useful for better aircraft design and production.
Originality/value
It is creating a novel method in order to redesign of morphing UAV and improving UAV performance.
Details
Keywords
This paper aims to present an alternative airspeed computation method based on artificial neural networks (ANN) without requiring pitot-static system measurements.
Abstract
Purpose
This paper aims to present an alternative airspeed computation method based on artificial neural networks (ANN) without requiring pitot-static system measurements.
Design/methodology/approach
The data set used to train proposed neural model is obtained from the Digital Flight Data Acquisition Unit records of a Boeing 737 type commercial aircraft for real flight routes. The proposed method uses the flight parameters as inputs of the ANN. The Levenberg–Marquardt training algorithm was used to train the neural model.
Findings
The predicted airspeed values obtained with ANN are in good agreement with the measured airspeed values. The proposed neural model can be used as an alternative airspeed computation method.
Practical implications
The proposed alternative airspeed computation method can be used when the air data computer or pitot-static system has failed.
Originality/value
The proposed method uses flight parameters as inputs for the ANN. As such, airspeed is calculated using flight parameters instead of the pitot-static system measurements.