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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.

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.

The blur caused by vibration can be reduced via constrained control of helicopter.

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.

Tighter constrained controllers can be implemented to take higher quality images by helicopters.

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.

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.

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.

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 (C_lβ) and maximum lift/drag ratio (E_max). 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 E_max 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 C_lβ and E_max, i.e. the ABC algorithm is used for two different purposes.

By using artificial intelligence methods for redesigning of morphing UAV, the objective function consisting of C*_lβ and E_max is maximized.

It takes quite a long time for E_max data to be obtained realistically by using the computational fluid dynamics approach.

Neural network incorporation with the optimization method idea is beneficial for improving C_lβ and E_max. By using this approach, low cost, time saving and practicality in applications are achieved.

This method based on artificial intelligence methods can be useful for better aircraft design and production.

It is creating a novel method in order to redesign of morphing UAV and improving UAV performance.

This paper aims to present an alternative airspeed computation method based on artificial neural networks (ANN) without requiring pitot-static system measurements.

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.

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.

The proposed alternative airspeed computation method can be used when the air data computer or pitot-static system has failed.

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.