Research of theoretical basis of implementation of intelligent control systems for locomotive traction transmission

The paper presents an analysis of existing automated control systems based on artificial intelligence theory. These systems employ methods such as fuzzy logic, artificial neural networks, and genetic algorithms. The application of these techniques enables the development of more adaptive and efficient control systems compared to traditional approaches. The main areas of artificial intelligence application in railway transport are identified, particularly in locomotive control systems and optimization of operational modes. The fundamental stages of artificial intelligence-based model development are outlined, including data collection and model training. Key directions for modeling intelligent systems are established. A generalized approach is proposed for the development of an intelligent traction transmission control system for shunting locomotives, taking into account the rolling stock characteristics and operational conditions. For solving control tasks, the use of a production model is proposed, which integrates elements of both logical and network-based approaches. A production model is proposed for solving control tasks.