Motor speed identification via neural networks

Speed information is necessary for high-performance vector-controlled induction motor drives. This information is generally provided by a sensor, which spoils the ruggedness and simplicity of the induction motor. This article presents a newly developed speed sensorless drive based on neural network techniques. The backpropagation neural network technique is used to provide a real-time adaptive identification of the motor speed. The estimation objective is defined in terms of a reference or desired trajectory that the neural networks model output should match or track as closely as possible. The backpropagation algorithm is used to adjust the motor speed so that the neural model output follows the desired trajectory. This backpropagation mechanism forces the estimated speed to follow the actual motor speed precisely. This article describes both the theoretical analysis as well as the simulation results to verify the effectiveness of the new method