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DOI: 10.14569/IJACSA.2017.080648
PDF

Fault-Tolerant Model Predictive Control for a Z(TN)-Observable Linear Switching Systems

Author 1: Abir SMATI
Author 2: Wassila CHAGRA
Author 3: Moufida KSSOURI

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 6, 2017.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: This work considers the control and the state observation of a linear switched systems with actuators faults. A particular problem is studied: the occurrence of non-observable subsystem in the switching sequence. Hence, the accuracy of the state estimations will decrease affecting the observer-based fault detection algorithms. In this paper, we propose a solution based on a constrained switching control in a predictive scheme. An extension to fault-tolerant control is derived, using several hybrid observers for estimation and fault detection and a reconfigurable finite control set model-predictive controller. The paper includes experimental results applied to a multicellular converter to demonstrate the efficiency of the method.

Keywords: Switching systems; Z(TN)-observability; finite control set predictive control; fault tolerant control; multicellular converter

Abir SMATI, Wassila CHAGRA and Moufida KSSOURI, “Fault-Tolerant Model Predictive Control for a Z(TN)-Observable Linear Switching Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 8(6), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080648

@article{SMATI2017,
title = {Fault-Tolerant Model Predictive Control for a Z(TN)-Observable Linear Switching Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080648},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080648},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {6},
author = {Abir SMATI and Wassila CHAGRA and Moufida KSSOURI}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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