A new concept of Artificial Neural Networks in estimating speed and controlling a separately excited DC motor. A three-layer feed forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer.
National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute NEURAL NETWORK BASED SPEED CONTROL FOR A DC MOTOR Samartsev I. V. Abstract neural motor speed controlling This paper introduces a new concept of Artificial Neural Networks (ANNs) in estimating speed and controlling a separately excited DC motor. The neural control scheme consists of two parts. One is a neural estimator, which is used to estimate the motor speed. The other is a neural controller, which is used to generate a control signal for a converter. These two networks are developed by Levenberg-Marquardt back propagation algorithm. A standard three-layer feed forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer is used. Simulation results are presented to demonstrate the effectiveness and advantages of the control system of the DC motor with the ANNs in comparison with the conventional control scheme. INTRODUCTION Nowadays, the fields of an electrical power
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