ELECTRIC DRIVE CONTROL BASED ON PERMANENT MAGNET BRUSHLESS DC MOTOR WITH MAGNETIC SATURATION BY NEURAL NETWORK COMPENSATOR

Proposes a mathematical model of a permanent magnet brushless motor (PMBM) taking into account the uneven gap and magnetic saturation. In the mathematical model of the PMBM, the change in the value of the inductance of the stator winding is considered when the position of the rotor angle changes. This mathematical model is a multidimensional set of functions of the electromagnetic torque and magnetic flux connecting the stator phase windings of the motor. On the basis of the constructed mathematical model, the article proposes a method for controlling the feedback of a neural network to compensate for nonlinear components due to magnetic saturation. The proportional integral (PI) control algorithm combines a nonlinear compensator with a neural network to control the speed of the PMBM motor in the low-speed range of operation with Gaussian disturbance of the load torque. To increase the efficiency and select a control method using the PMBM engine, it is necessary to consider its nonlinear elements. A modern approach to solving the problems posed is the use of a neural network compensator with a PI controller. The simulation results showed that the controller has a strong self-adaptation compared to the conventional PI controller.

Authors: M. P. Belov., D. D. Truong

Direction: Electrical Engineering

Keywords: Control, permanent magnet brushless motor, compensator, neural network, proportional integral controller, magnetic saturation

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