The objective of this work is to present recent advances in the
theory of neural control for discrete-time nonlinear systems with
multiple inputs and multiple outputs. The results that appear in
each chapter include rigorous mathematical analyses, based on the
Lyapunov approach, that guarantee its properties; in addition, for
each chapter, simulation results are included to verify the
successful performance of the corresponding proposed schemes. In
order to complete the treatment of these schemes, the final chapter
presents experimental results related to their application to a
electric three phase induction motor, which show the applicability
of such designs. The proposed schemes could be employed for
different applications beyond the ones presented in this book.
The book presents solutions for the output trajectory tracking
problem of unknown nonlinear systems based on four schemes. For the
first one, a direct design method is considered: the well known
backstepping method, under the assumption of complete sate
measurement; the second one considers an indirect method, solved
with the block control and the sliding mode techniques, under the
same assumption. For the third scheme, the backstepping technique
is reconsidering including a neural observer, and finally the block
control and the sliding mode techniques are used again too, with a
neural observer. All the proposed schemes are developed in
discrete-time. For both mentioned control methods as well as for
the neural observer, the on-line training of the respective neural
networks is performed by Kalman Filtering.
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