Synthesis of neuroregulator of power in system of sensorless traction electric drive

Abstract

In this paper we solve the problem of synthesizing a power regulator in a traction electric drive system using artificial neural networks. To control the vehicle and obtain the desired quality of transients, neural network observers have been developed that allow the measurement of indirect parameters to determine the immutable coordinates of the system. For this purpose, this paper uses dynamic neural networks. When developing the neural network observer, experimental data obtained by the authors on an operating vehicle in real operating conditions are used. To test the effectiveness of using the created artificial neural network, an object is simulated with a random nature of the supply voltage change. A comparative analysis of
transients in a system with a power neuroregulator and classical regulators in a subordinate control system shows a fairly high convergence of the results.