Abstract
Mathematical modeling is an important feature concerning the analysis and control of dynamic systems. Also, system identification is an approach for building mathematical expressions from experimental data taken from processes performance. In this context, the contemporaneous state of the art describes several modelling and identification techniques which are excellent alternatives to determine systems behavior through time. This paper presents a comprehensive review of the main techniques for modeling and identification from a parametric and no parametric perspective. Experimental data are taken from an electrical machine that is a DC motor from a didactic platform. The paper concludes with the analysis of results taken from different identification procedures.
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