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In the paper, visual navigation of a drone is considered. The drone navigation problem consists of two parts. The first part is finding the real position and orientation of the drone. The second part is finding the difference between desirable and real position and orientation of the drone and creation of the correspondent control signal for decreasing the difference. For the first part of the drone navigation problem, the paper presents a method for determining the coordinates of the drone camera with respect to known three-dimensional (3D) ground objects using deep learning. The algorithm has two stages. It causes the algorithm to be easy for interpretation by artificial neural network (ANN) and consequently increases its accuracy. At the first stage, we use the first ANN to find coordinates of the object origin projection. At the second stage, we use the second ANN to find the drone camera position and orientation. The algorithm has high accuracy (these errors were found for the validation set of images as differences between positions and orientations, obtained from a pretrained artificial neural network, and known positions and orientations), it is not sensitive to interference associated with changes in lighting, the appearance of external moving objects and the other phenomena where other methods of visual navigation are not effective. For the second part of the drone navigation problem, the paper presents a method for stabilization of drone flight controlled by autopilot with time delay. Indeed, image processing for navigation demands a lot of time and results in a time delay. However, the proposed method allows to get stable control in the presence of this time delay. [ABSTRACT FROM AUTHOR]

In recent years, machine learning (ML) has received growing attention and it has been used in a wide range of applications. However, the ML application in renewable energies systems such as fuel cells is still limited. In this paper, a prognostic framework based on artificial neural network (ANN) is designed to predict the performance of proton exchange membrane (PEM) fuel cell system, aiming to investigate the effect of temperature and humidity on the stack characteristics and on tracking control improvements. A large part of the experimental database for various operating conditions has been used in the training operation to achieve an accurate model. Extensive tests with various ANN parameters such as number of neurons, number of hidden layers, selection of training dataset, etc., are performed to obtain the best fit in terms of prediction accuracy. The effect of temperature and humidity based on the predicted model are investigated and compared to the ones obtained from real-time experiments. The control design based on the predicted model is performed to keep the stack operating point at an adequate power stage with high-performance tracking. Experimental results have demonstrated the effectiveness of the proposed model for performance improvements of PEM fuel cell system. [ABSTRACT FROM AUTHOR]