Non-linear Grey Box Models Applied to DC Motor Identification

Abstract

Grey-box modeling integrates both qualitative (expert-based) and quantitative knowledge (measurement data). Grey box model combines a formal structure of the phenomenon along with a data-driven generic model approximation, until a complete and precise representation is achieved. In this paper, a grey-model based plant identification is applied in order to estimate the parameters of a Pitman GM9413-3 DC motor. The model is partially obtained by fitting the values of the internal resistance and inductance of the stator using a linear regression from DC test and rotor blocked test, respectively, and assuming that no loses are present due to electromagnetic conversion. Further, a data driven model fitting is carried out using the information acquired using a FPGA-based data acquisition system tailored for the application. Results show that the model is precise, giving an fit to estimation data of more than 84%, and final prediction error of less than 1%.