Efficient Neural Network Control of a Brushless DC Motor

The paper considers the main trends in the development of electric motors for electric vehicles and mobile robots, as well as trends in the development of modern methods for calculating power electronics and electric drives based on an artificial neural network. Aspects of the efficiency development of modern synchronous and brushless DC motors are presented. Based on the mathematical model of a brushless DC motor, the architecture of a control unit with a neural network controller is built. A proactive calculation of the neural network was carried out, and the rules for adjusting the weighting coefficients were determined. Based on proactive calculation, a PID controller with self-adjusting parameters using a neural network was built, as well as a block diagram of the PID control system was built on the basis of the BP neural network; also, a speed controller was built using MATLAB modules. Besides, an S-activation function was built as a controller of the BP neural network; the function was based on the mathematical description of the neural network of the control unit of a brushless DC motor. The paper shows in detail the installation of a demultiplexer for better distribution of the S-function output. The resulting neural network encapsulates the S-function of the weight function. Based on the results of the neural network research and analysis of the BP neural network algorithm, a control algorithm has been established that is used to control the PID controller and is encapsulated in the simulation system. The theoretical possibilities of calculation based on a feedback neural network for constructing a simulation model of adaptive control of a brushless DC motor are demonstrated.

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