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.

The task of resource saving is relevant for all transport companies and many world communities of scientists and engineers are engaged in search for ways to solve it. World railway companies, and especially large ones such as Russian Railways, are large consumers of energy resources and the problem of saving is the most urgent for them. One of directions for solving this task can be the use of an optimal energy regulation system for traction of each train – an operational rationing system. Such a task can only be solved by modelling the process of movement through the dynamic programming method. In modern conditions of development of engineering and technology, it has become possible to develop such operational standardization systems endowed with important properties: high performance, multitasking, solution accuracy, ease of use and maintenance. These requirements impose certain restrictions on the architecture of the operational rationing system. Typical system architecture should be built around a centralized node, which will act as a solver and storage, nodes for input and output of information can be geographically separated. The method of dynamic programming can be improved by using it in the process of training artificial neural networks, which will form not only a priori estimates of energy consumption for traction, but also an a posteriori estimate of train control (by a train driver or auto-driving system). Also, the use of artificial neural networks will allow us to continuously improve the method due to training using the accumulated amount of data from real trips, which will allow us to clarify the norms of energy consumption and to plan our costs in the future. The prototype of the operational standardization system was developed at the department of traction rolling stock of Russian Open Academy of Transport of Russian University of Transport and the results obtained allow us to state that the chosen approach to solving the problem of energy saving has been chosen correctly.

The article considers the challenges facing the transport complex, in particular, at the stage of digital transformation. The directions of development of the transport complex are defined, including the creation of a highly effective "trusted environment". The reasons why the transition to a new paradigm of building transport control systems and safety devices of a new generation is extremely necessary are identified and analyzed. The basic principles and architecture of the basic platform for advanced control systems, safety and diagnostics of traction rolling stock are formulated and justified.