The paper presents two fuzzy logic control
algorithms: type-1 and type-2. These two nonlinear techniques
are used for adjust the speed control with a direct stator flux
orientation control of a doubly fed induction motor. The
effectiveness of the proposed control strategy is evaluated
under different operating conditions such as of reference speed
and for load torque step changes at nominal parameters and in
the presence of parameter variation (stator resistance, rotor
resistance and moment of inertia). The results of the simulation
of the doubly fed induction motor velocity control have shown
that fuzzy type-2 ensures better dynamic performances with
respect to fuzzy type-1 control, even by parametric variations
and external disturbances.
I

This paper presents a fuzzy logic controller destined to the doubly-fed induction motor (DFIM) speed controlling. It solves the problems associated with the conventional IP (Integral Proportional) controller. This fuzzy logic controller is based on the decoupling control to enhance robustness under different operating conditions such as load torque and in the presence of parameters variation.
The simulation results for various scenarios show the high performances of the proposed control in terms of piloting effectiveness, precision, rapidity and stability for the high powers DFIM operating at variable speeds.

In this paper, we are interested in the adaptive 
fuzzy control a technique has been studied and
 applied, namely adaptive fuzzy control based
on theory of Lyapunov. The system based on the 
stability theory is used to approximate the gains 
ke and kdce to ensure the stability of the control
 inparticular time, simulations results obtained by

using MATLAB environment gives that the fuzzy

adaptive control more robust, also it has superior

dynamicsperformances. The results and test

of robustness will be presented.

This paper deals with a nonlinear adaptive control design based on synergetic control,
which also uses fuzzy systems to approximate the dynamics of nonlinear systems. The stability of
the closed-loop system is ensured by the Lyapunov synthesis in the sense that all the signals are
bounded, and the controller parameters adjusted by adaptation laws. The proposed algorithm is
applied to an inverted pendulum to track a sinusoidal reference trajectory. Simulations and
discussion are presented to illustrate the new robust adaptive fuzzy synergetic control described
in this work.

In this paper, a new control strategy is developed;
an adaptive fuzzy controller based on Lyapunov’s
stability theory (AFLC) recalculates the real-time

PI-fuzzygains and combines the advantages of

two robust techniquesi.e. the fuzzy logic control

and the adaptive one. For the newadaptive fuzzy

control, we followed two steps: in the first one, a
PI-fuzzy controller is designed, in the second step,

the gains ofa fuzzy regulator are determined.

Extensive simulation resultsare presented to

validate the proposed technique. The system is

tested at different speeds and a very satisfactory

performance has been achieved.

In this paper, a new control strategy is developed;
an adaptive fuzzy controller based on Lyapunov’s
stability theory (AFLC) recalculates the real-time PI-fuzzy
gains and combines the advantages of two robust techniques
i.e. the fuzzy logic control and the adaptive one. For the new
adaptive fuzzy control, we followed two steps: in the first one, a
PI-fuzzy controller is designed, in the second step, the gains of
a fuzzy regulator are determined. Extensive simulation results
are presented to validate the proposed technique. The system is
tested at different speeds and a very satisfactory performance
has been achieved.