Model Reference Adaptive Neural Control
of a Variable Structure System
Moises Bonilla*, Ieroham Baruch*, Jose Martin Flores*, Maria M. Goire*, Boyka Nenkova**
* Department of Automatic Control, CINVESTAV-IPN, 07360 Mexico D.F., MEXICO
** Institute of Information Technologies, 1113 Sofia
Abstract: The aim of this paper is to propose a model reference adaptive neural
control of a variable structure plant, described by an implicit realization with
variable order and parameters, using only output feedback. The neural control
scheme proposed is composed by two recurrent neural networks, named: neuro-identifier
and neuro-controller. A variable structure plant model together with the realized
adaptive neural control are simulated by means of the MatLab-Simulink and the
obtained simulation results are compared with those obtained by the use of an
ideal implicit control, applying the true descriptor variable. The simulation
results show a great similarity of the obtained graphics for both control schemes,
which demonstrated the applicability of the proposed adaptive neural control.
Keywords: Model reference adaptive control, variable structure plant,
recurrent neural networks, backpropagation learning.