Open AccessControl of variable reluctance machine (8/6) by artificiel intelligence techniques
Author(s) -
Chouitek Mama,
Noureddine Benouzza,
Benaissa Bekouche
Publication year - 2020
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v10i2.pp1893-1904
Subject(s) - magnetic reluctance , control theory (sociology) , computer science , robustness (evolution) , pid controller , nonlinear system , fuzzy logic , control engineering , variable (mathematics) , rotor (electric) , matlab , artificial neural network , artificial intelligence , control (management) , engineering , mathematics , magnet , mechanical engineering , temperature control , mathematical analysis , biochemistry , chemistry , physics , quantum mechanics , gene , operating system
The non-linearity of variable-Reluctance Machine (8/6) and the dependence of machine inductance on rotor position and applied current complicate the development of the control strategies of drives using variable-Reluctance Machine variable-Reluctance Machine (VRM). The classical-control algorithms for example of derived full proportional action may prove sufficient if the requirements on the accuracy and performance of systems are not too strict. In the opposite case and particularly when the controlled part is submitted to strong nonlinearity and to temporal variations, control techniques must be designed which ensure the robustness of the process with respect to the uncertainties on the parameters and their variations. These techniques include artificial-intelligence-based techniques constituted of neural networks and fuzzy logic. This technique has the ability to replace PID regulators by nonlinear ones using the human brain’s reasoning and functioning and is simulated by using MATLAB/Simulink software. Finally, by using obtained waveforms, these results will be compared.