Servomotor Parameter Identification Using a Kalman Filter with Model Reference Estimator

In this work, a parameter estimation method is proposed to identify the parameters of a DC servomotor. While the parameters are obtained with a model reference estimator, a Kalman filter is used to get, at the same time, the states of the model. With this method, it is possible to get the process noise also and use it to obtain a better extrapolation of the states and covariance matrix. The performance of the method is measured with experimental results, and it is compared with the dual Kalman filter.