Identification of Parameters of DC Motor of Independent Excitation by Noisy Data

The identification of parameters of a DC motor of independent excitation is considered. Existing methods of parameter identification of the DC motor do not consider the presence of errors in the output and input signals. The paper uses a discrete model of a DC motor. One of the advantages of this model is the reduction of the parameter estimation problem to two problems of reduced dimension. A new algorithm for identifying the parameters of a DC motor is proposed. The proposed algorithm makes it possible to consider the errors in measurements of current, voltage, and rotation speed. The criterion for finding the parameters is a generalization of the total least squares (TLS) criterion. To find estimates, the augmented use of equivalent systems of systems is proposed. Augmented equivalent systems allow one to reduce the number and do not require computation of the right singular vector. The simulations of the algorithm’s operation showed its advantage over the ordinary least squares (OLS) method. The results of the work can be applied in the development of predictive diagnostics systems.