Artificial Neural Networks applied to Sensorless Control in a Switched Reluctance Motor
نویسندگان
چکیده
This work analyzes the advantages of Artificial Neural Networks applied to sensorless control for Switched Reluctance Motors. Due to the non-linear electrical characteristics of Switched Reluctance Motors, Artificial Neural Networks perform a good tool and for torque ripple minimization in Switched Reluctance Motors. The simulation results to prove the efficiency of a multilayer perceptron in order to estimate the rotor position and to reduce the ripple torque of this machines. The data employed to train the network has been obtained by numerical simulation. Key-Words: Artificial Neural Networks, Modeling, Multilayer Perceptron, Ripple Minimization, Sensorless Control, Switched Reluctance Motors.
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