Control of heating processes in electro transport mechatronic system using sigmoidal feed forward neural network

In this article interest is concentrated on the climate parameters optimization in passengers’ interior of mechatronic systems (public electric transportation vehicles- train, tram or trolleybus). Idea is to use feed forward artificial neural network to create an algorithm and coordination mechanism for heating system parameters control to save electrical energy, and to increase the level of comfort for passengers. A special interest for investigations and further development is devoted to intelligent HVAC system allowing more flexible control of the system’s compressor, fan and heater operation, and, therefore, improvement of efficiency and energy saving. This paper provides the mathematical model and algorithm for optimal control of the climate control system.