| Abstract |
The reduction of the energetic consumption over the
dynamic regime period of the induction motor (IM) is an
open question, available solutions being researched
regarding both the industrial users and domestical ones.
Optimal control and adaptive control fields are joint by
a feed forward neural network, regarding the latter it
was used for approximation of optimal control. The rotor
field oriented IM is controlled at constant flux, the
optimal control synthesis consisting of the determination
of the statoric three-phase currents system, based on the
longitudinal and transversal components of the statoric
phasor current. The optimal control law provides
dynamic regimes with minimal energy consumption. The
parameter variation problem cannot be incorporated in
the network with on-line training. In this paper, it will be
shown the inference of the approximation optimal
control solution by using a model reference adaptive
control (MRAC). Thus, the huge advantages of these
control fields are combined The experimental results
show the advantage of application this control strategy
versus classical control system in AC drives. The
adaptive structure was used in direct form, such that the
parametric estimator could provide the controller
parameters on-line. The adjustment of parameters law
used is obtained by additive composing of two terms: the
first will support a gradient adjustment law (which
assures the asymptotic performances) and the second will
comport an adjustment that includes a sigmoid function
(which depends on a single parameter, named k-sigmoid)
specific for variable structure control. This component
improves the transient response and eliminates the small
oscillations of the loop response around the equilibrium
state of zero tracking error. This additive composing of
the adaptive law assures the robustness to the external
disturbances and to the unmodelled dynamics. |
