Разработка нейросетевого регулятора для дизеля

Abstract

The paper considers issues of using a neural network in the thermal engine controller in order to improve the quality of its control and setting conveniences. The study object was a speed controller for the D500 promising locomotive diesel engine. Tasks for design and development of a neural network controller were formulated, and the network input signals were determined. To adjust the neural network, the reinforcement learning method was introduced, where it interacted with the diesel engine computer model in a closed system. The criterion in setting up the network was the accuracy of the control program execution. A system of rewards was assigned, according to which the network was learning. Based on the results of studying the neural network controller influence on the quality of the control system operation, the network minimum possible composition for solving the problem presented was determined. Study results are presented in the form of graphs of rewards alteration during the learning process for various options of the neural network controller structure, as well as the control system simulated transient processes over the entire range of the diesel engine speed.