Abstract
The control of the variable reluctance machine raises a certain number of constraints, among which we can cite: the effect of the external disturbances, the nature of the nonlinearities, the ripple of its torque, and the modeling errors. The different ordering approaches proposed in this article dealt with all these constraints. 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 flexible. In the opposite case, particularly when the controlled part is submitted to strong nonlinearity and temporal variations, control techniques must be designed to ensure the process's robustness concerning the uncertainties on the parameters and their variations. These techniques include artificial intelligence-based techniques constituted of neural networks and fuzzy logic. This technique can replace PID regulators with nonlinear ones using the human brain’s reasoning and functioning and is simulated using MATLAB/Simulink software. Finally, by using obtained waveforms, these results will be compared.
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