Adaptive Filters for DC Motor Identification: A Case Study

This paper investigates the practical application and evaluation of four adaptive algorithms: Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Square (RLS) and Affine Projection (AP) for the identification of a DC motor. The work focuses on dynamic system identification, where the adaptability and efficiency of numerical filters play an important role. To identify the parameters of a DC motor, these adaptive algorithms are used on measured data. With the aim of having a shorter processing time, the data was downsampled. Satisfactory results were obtained with all algorithms. Finally, the difficulty of implementing the algorithms, the errors obtained and the processing time were analyzed.