Optimized Neuro PI Based Speed Control of Sensorless Induction Motor
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In this paper a sensorless vector control system of induction motor using Neural Networks is presented. Neural network is used to control the non linear dynamic systems to get desired degree of accuracy. A feed forward neural network with one input, two units in the hidden layer and one output is used for the speed controller. The tracking of the rotor speed is done by a neural PI controller and is realized by adjusting the new weights of the network depending on the difference between the actual speed and the command speed. The use of the controller tracks the rotor speed command smoothly and rapidly, without overshoot and with zero steady state error without the sensor. GA has been recognized as an effective and efficient technique to solve optimization problems. Finally this controller can be optimized using a Genetic Algorithm Technique. When compared to Neuro PI controller Genetic Algorithm produces better performance. Computer simulation results are carried out with various tool boxes in MATLAB to verify the effectiveness of the proposed controller. The result concludes that the efficiency and reliability of the proposed speed controller is good.
KeywordsSensorless vector control Genetic Algorithm Neural Network Backpropogation Network
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