Online parameter estimation of PMDC motors using quantized output observations

Obtaining the dynamic behavior of permanent magnet direct current (PMDC) motors during operation is of essential importance for control adaptation, condition monitoring, and diagnosis. Quantized observations stem from either using low-cost sensors or using communication channels in remote control applications. In this paper, new estimation algorithms are developed to identify the motor speed and estimate the model parameters of PMDC motors using quantized output observations. This technique provides accurate estimation with lower costs on sensors or reduced communication resources. To validate the proposed estimator, periodic input dithers are used, and estimator accuracy and performance are demonstrated by simulation.