Altan A. | Aslan O. | Hacioglu R.

Conference Object | 2018 | 2018 6th International Conference on Control Engineering and Information Technology, CEIT 2018

The effective control of gimbal, Vertical Take-Off and Landing (VTOL) and Load Transporting System (LTS) in Unmanned Aerial Vehicles (UAV), which are widely used in mapping, search-and-rescue, exploration and surveillance, border security, real-time image transfer by tracking target and leaving payloads to specified targets in hazardous regions directly affect task performance. In this study, Model Reference Adaptive Control (MRAC) of LTS which has an important role to be able to leave payloads on UAV in real time with minimum error to specified targets is carried out and its effect on duty performance is investigated. The three pay . . .loads in the cubic structure are transported by LTS originally designed for real-time specified targets. DC gear motors are used in the LTS so that payloads can be left to real time specified targets. Environmental testing is conducted taking into account the limitations of the physical properties of the LTS on the autonomously moving UAV, and the impact on MRAC's mission performance is examined. © 2018 IEEE Daha fazlası Daha az

Altan A. | Hacioglu R.

Conference Object | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this study, focuses on the non-linear Hammerstein model under external disturbance with white Gaussian noise based on the experimental input (motor velocities) and output (end effector position) data of the three axes gimbal system on the Unmanned Aerial Vehicle (UAV), which is autonomously moving for route tracking. The performance of UAV in reaching the target point on a planned route in sinusoidal form in avoiding obstacles, depends on the route tracking performance of the three axes gimbal system on the UAV. In intelligence activities such as exploration and surveillance, Hammerstein and Nonlinear AutoRegressive and Moving Av . . .erage (NARMA) models of the gimbal system with great importance for UAV that real time image transmission and in the tasks of leaving payloads to targets with unknown coordinates with the least mistake are obtained. The parameters of the obtained models are estimated by Recursive Least Squares (RLS) algorithm and the model performances are compared. © 2018 IEEE Daha fazlası Daha az

Karasu S. | Altan A. | Sarac Z. | Hacioglu R.

Conference Object | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this study, Bitcoin prediction is performed with Linear Regression (LR) and Support Vector Machine (SVM) from machine learning methods by using time series consisting of daily Bitcoin closing prices between 2012-2018. The prediction model with include the least error is obtained by testing with different parameter combinations such as SVM with including linear and polynomial kernel functions. Filters with different weight coefficients are used for different window lengths. For different window lengths, Bitcoin price prediction is made using filters with different weight coefficients. 10-fold cross-validation method in training ph . . .ase is used in order to construct a model with high performance independent of the data set. The performance of the obtained model is measured by means of statistical indicators such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Pearson Correlation. It is seen that the price prediction performance of the proposed SVM model for Bitcoin data set is higher than that of the LR model. © 2018 IEEE Daha fazlası Daha az

Altan A. | Aslan O. | Hacioglu R.

Conference Object | 2018 | 2018 6th International Conference on Control Engineering and Information Technology, CEIT 2018 , pp.1 - 4

The control of equipment such as camera gimbal, Vertical Take-Off and Landing (VTOL) and Load Transporting System (LTS) on Unmanned Aerial Vehicle (UAV) with its own flight control directly affects the performance of the mission in tasks such as tracking the target along the specified path and leaving payloads on the targets specified in the dangerous areas. In this study, neural network based real-time control of a hexarotor UAV is performed so that the payloads on the targets determined by path tracking can be left with minimum error. The Nonlinear AutoRegressive eXogenous (NARX) model of the UAV is obtained after the flight data . . .are passed through the pre-processing, feature extraction and feature selection stages. The obtained neural network model is embedded in the flight control card to realize real time path tracking of the UAV. The three payloads in the cubic structure are both transported by the originally designed LTS and left with the help of LTS to targets on the path. Environmental testing is conducted taking into account the limitations of the physical properties of the LTS and specified path tracking on the autonomously moving UAV, and the impact on proposed NARX control algorithm's mission performance is examined. © 2018 IEEE Daha fazlası Daha az