Implementation of speed sensorless induction motor drives by fast learning neural network using recursive least squares approach

This paper presents a newly developed speed sensorless drive using RLS (recursive least squares) based on neural network training algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known backpropagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the backpropagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.