Flatness-based adaptive fuzzy control of brushless doubly-fed reluctance machines

Abstract

The article proposes an adaptive control approach that is capable of compensating for model uncertainty and parametric changes of the doubly-fed reluctance machines (DFRMs), as well as for the lack of measurements about the DFRM's state vector elements. First it is proven that the DFRM's model is a differentially flat one. By exploiting differential flatness properties it is shown that the DFRM model can be transformed into the linear canonical form. For the latter description, the new control inputs comprise unknown nonlinear functions which can be identified with the use of neurofuzzy approximators. The estimated dynamics of the generator is used by a feedback controller thus establishing an indirect adaptive control scheme. Moreover, to robustify the control loop, a supplementary control term is computed using H-infinity control theory. Another problem that has to be dealt with comes from partial measurements of the state vector of the generator. Thus, a state observer is implemented in the control loop. The stability of the considered observer-based adaptive control approach is proven using Lyapunov analysis. Moreover, the performance of the control scheme is evaluated through simulation experiments.