Fuzzy Adaptive Controller for a DFI-Motor - Complex System Modelling and Control Through Intelligent Soft Computations - page 89

Therefore, the motivation of this chapter is the design of a nonlinear controller
for DFI-Motor drives which guarantees speed tracking and reactive power regu-
lation at stator side. The DFI-Motor con

guration taken in this work uses one
converter in the rotor and the stator is directly connected to the line grid. Our
approach is based on the decomposition of the machine model in two coupled
subsystems; the stator flux subsystems. First, the stator

voltage vector oriented reference frame is adopted, and the stator reactive power
regulation purpose is converted into a stator

fl ux and the speed-rotor fl

fl flux regulation problem. In fact, the

time varying stator flux vector is required to be orthogonal to line voltage. In fact,

the d-axis component of rotor

fl

fl flux appears as the control input for the stator fl ux

subsystem. Then, with an appropriate choice of the stator fl flux reference and a strict

control of d-axis component of rotor ux error

dynamics become linear and exponentially stable independently of the speed
dynamics. Consequently, the DFI-Motor stator unity power factor control and the
speed tracking problems are converted into a rotor

fl flux to a suitable value, the stator fl

flux control problem. The
controller design is based on combination of sliding-mode control, fuzzy control
and adaptive backstepping control approaches. The adaptive fuzzy systems are used
to reasonably approximate the unknown nonlinear functions appearing in the DFI-
Motor model and the tracking errors dynamics and the uncertainties. While, the
sliding-mode control is used to effectively compensate for the unavoidable fuzzy
approximation error. The adaptive laws, which are used to estimate on-line the load
torque and the fuzzy parameters, are derived in the sense of Lyapunov stability
theorem. Brie

fl

y, the nonlinear control approach described in this paper has the
following important advantages:

fl

The motor-generated torque becomes linear with respect to system control

￿

states.

The rotor fl flux can be easily regulated in order to increase the machine ef ciency.

￿

The system robustness can be achieved against the uncertain parameters of
DFI-Motor (rotor resistance, stator resistance), perturbations (i.e. the unknown
load torque), functional uncertainties, etc.

￿

The controller design does not strongly depend on the model of DFI-Motor.

￿

best knowledge, there is no result reported in the
literature on the fuzzy adaptive control design for doubly-fed induction machine. It
is worth noting that the design of the adaptive control based on state-all

Moreover, to the authors '

flux model,
for a DFI-Motor controlled by acting on the rotor winding and with a stator which is
directly connected to the grid, is very challenge.
This chapter is organized as follows: Section 2 introduces the state-all-

fl

ux DFI-
Motor model. In Sect. 3 , the DFI-Motor control problem is presented. In Sect. 4 , the
fuzzy logic system used for approximating the unknown nonlinear function is
described. In Sect. 5 , the proposed fuzzy adaptive backstepping controller (FABC)
is presented. In Sect. 6 , the effectiveness of our FABC for a DFI-Motor is dem-
onstrated via some simulations results. Conclusions are drawn in Sect. 7 .

fl