Neural networks and fuzzy nonlinear controllers applied to an induction machine

This paper is concerned by the use of neural networks and fuzzy logic for controlling a non-linear process namely an induction machine. In the first case study, the design procedure uses a neural model trained with the inverse model of the process. Thus, the overall controlled system is formed using this inverse model. In the second case study, a fuzzy logic controller is implemented. In both cases, the controller is cascaded with the process ensuring the robustness and the stability of the controlled system regarding parameters uncertainties and disturbances. This work analyses the advantages and the drawbacks of each controller in terms of tracking and regulation. It is shown that the fuzzy logic controller is slightly better with respect to the neural network controller in the transient while they have quite similar behaviour in the steady-state regime.