Method for constructing an adaptive suboptimal stationary train traffic controller based on artificial neural networks

The article discusses the modern methodology for performing the synthesis of a suboptimal train controller for the purpose of energy saving. The existing methods of optimal traction control have a number of disadvantages, the main one of which is the lack of direct use in the control program of the data obtained during train operation. Mathematical models used to solve the op- timal problem can be used correctly only in the case of sufficient adequacy. Adequacy check is not part of the known methods of optimal control theory. To eliminate this drawback, it is proposed to use the method of optimal (suboptimal) traction calculations based on artificial neural networks. It improves the accuracy of traction calculations, which is especially important in the aspect of considering energy savings, while reducing the need for computing power. When using this method, it is possible not only to achieve results close to the classical Bellman method, but also to train or verify the network using the recorded data. The article discusses the process of creating and training an artificial neural network based on model data to solve the problem of suboptimal control. The train motion modes obtained by Bellman's method were used as reference data for training the neural network. The presented comparative results of the two methods show the applicability of artificial neural networks for solving applied problems of train traction with the possibility of continuous learning, including the use of trip data, which can be directly included in the training or testing set.