Neural network-based MRAC control of dynamic nonlinear systems

Ghania Debbache; Abdelhak Bennia; Noureddine Golea

International Journal of Applied Mathematics and Computer Science (2006)

  • Volume: 16, Issue: 2, page 219-232
  • ISSN: 1641-876X

Abstract

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This paper presents direct model reference adaptive control for a class of nonlinear systems with unknown nonlinearities. The model following conditions are assured by using adaptive neural networks as the nonlinear state feedback controller. Both full state information and observer-based schemes are investigated. All the signals in the closed loop are guaranteed to be bounded and the system state is proven to converge to a small neighborhood of the reference model state. It is also shown that stability conditions can be formulated as linear matrix inequalities (LMI) that can be solved using efficient software algorithms. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation results are presented to show the effectiveness of the approach.

How to cite

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Debbache, Ghania, Bennia, Abdelhak, and Golea, Noureddine. "Neural network-based MRAC control of dynamic nonlinear systems." International Journal of Applied Mathematics and Computer Science 16.2 (2006): 219-232. <http://eudml.org/doc/207787>.

@article{Debbache2006,
abstract = {This paper presents direct model reference adaptive control for a class of nonlinear systems with unknown nonlinearities. The model following conditions are assured by using adaptive neural networks as the nonlinear state feedback controller. Both full state information and observer-based schemes are investigated. All the signals in the closed loop are guaranteed to be bounded and the system state is proven to converge to a small neighborhood of the reference model state. It is also shown that stability conditions can be formulated as linear matrix inequalities (LMI) that can be solved using efficient software algorithms. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation results are presented to show the effectiveness of the approach.},
author = {Debbache, Ghania, Bennia, Abdelhak, Golea, Noureddine},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {LMI; neural networks; reference model; observer; adaptivecontrol; stability; nonlinear systems; adaptive control},
language = {eng},
number = {2},
pages = {219-232},
title = {Neural network-based MRAC control of dynamic nonlinear systems},
url = {http://eudml.org/doc/207787},
volume = {16},
year = {2006},
}

TY - JOUR
AU - Debbache, Ghania
AU - Bennia, Abdelhak
AU - Golea, Noureddine
TI - Neural network-based MRAC control of dynamic nonlinear systems
JO - International Journal of Applied Mathematics and Computer Science
PY - 2006
VL - 16
IS - 2
SP - 219
EP - 232
AB - This paper presents direct model reference adaptive control for a class of nonlinear systems with unknown nonlinearities. The model following conditions are assured by using adaptive neural networks as the nonlinear state feedback controller. Both full state information and observer-based schemes are investigated. All the signals in the closed loop are guaranteed to be bounded and the system state is proven to converge to a small neighborhood of the reference model state. It is also shown that stability conditions can be formulated as linear matrix inequalities (LMI) that can be solved using efficient software algorithms. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation results are presented to show the effectiveness of the approach.
LA - eng
KW - LMI; neural networks; reference model; observer; adaptivecontrol; stability; nonlinear systems; adaptive control
UR - http://eudml.org/doc/207787
ER -

References

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