Abstract
This paper proposes a new approach for the identification of a DC machine (DCM) parameters to build a mathematical model considering different dynamic regimes, which characterize the operation of the studied machine. The proposed solution is simple and is based on the combination of classical identification methods and those available in the identification toolbox of MATLAB. The results obtained experimentally are significantly better and clearly show that the proposed approach is simple to implement and the DCM model is obtained quickly with a reasonable accuracy.
Highlights
Nowadays, there are several real systems from different disciplines such as mechanics, electronics, thermal, chemistry, physical, etc. around us
The approach allows the calculation of all the DC machine (DCM) parameters using the following steps: - Determination of the electrical parameters by classical methods. - Determination of the DCM transfer function using MATLAB Identification toolbox
We have studied the contribution of techniques identifications such as classical methods and identification methods available in the software MATLAB, applied to the identification of DCM parameters
Summary
There are several real systems from different disciplines such as mechanics, electronics, thermal, chemistry, physical, etc. around us. For the identification of the DCM parameters, most researchers use classical methods because of their simplicity These methods require a lot of testing on the machine which increases the risk to damage the actual system and they do not give best results, especially when using them to determine the specific inductances (Le, La) and the mechanical parameters (f, J). Other researchers have studied the digital identifiers methods to reduce the number of tests on the machine and to ensure rapid detection of parameters change in real time and under variable environmental conditions (variation in load, machine overheating, short-circuit fault, etc.) Among these works, the recursive least squares method [3], constrained optimization technique [4], Kalman filter [5], identification of nested loop systems [6], artificial neurons networks [7], Tabu research technique [8], adaptive Tabu search technique [9] and genetic algorithms [10,11].
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