Model of a neural network adaptive system for a digital control loop of an electric drive

Abstract

The problem of increasing the efficiency of power units of autonomous electric transport vehicles is considered. The task of creating a promising power system control device has been singled out. It is determined that in creating such devices, significant results can be obtained by using an intelligent module in the control loop of the electric drive. Goal. It is necessary to develop a power plant model with intelligent control, allowing to obtain data sets about currents, voltages and engine speeds in different modes of operation. The architecture of an intelligent control device, a PID controller based on a neural network, has been proposed; it has been proposed to exclude rotor angular velocity sensors from the classical feedback loop. The type and architecture of the neural network is defined. In the software environment MatLab the model of neuroemulator of the engine for formation of a training sample of a neural network by a method of Levenberg – Marquardt is developed. The trained neural network is implemented in the developed model of the electric motor control loop. The results of simulation of the intelligent control device showed a good convergence of the output influences generated by the neuroemulator with the actual parameters of the electric motor.