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Fuzzy Model Predictive Controller for Artificial Pancreas

International Journal of Power Electronics and Drive Systems · Vol. 9 No. 3 (2018) · pp. 1178–1185

DOI
10.11591/ijpeds.v9.i3.pp1178-1185
ISSN
2722-256X, 2088-8694
Published
2018-09-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This work consists on new tuning of Model Predictive Controllers using Fuzzy Logic method. Tree relevant parameters are automatically adjusted the prediction horizon Np, the input weight R and the output weight Q. The proposed controller is implemented in an Artificial Pancreas and tested under realistic conditions in a commercial platform of simulation. The result of the simulations revealed the success of such a method to improve the controller’s performances compared to the previous ones.

Collaboration network of the authors

12 authors · 23 co-authorship links

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  • University of Hassan II Casablanca
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Abdennaceur BaghdadUniversity of Hassan II Casablanca5
Abdelmajid BadriUniversity of Hassan II Casablanca2
Ikram DaanouneUniversity of Hassan II Casablanca2
Abir ZaidiUniversity of Hassan II Casablanca1
Aïcha SahelUniversity of Hassan II Casablanca1
Jihane OuchrifUniversity of Hassan II Casablanca1
Ilyas KhelafaUniversity of Hassan II Casablanca1
Abdenaceur BaghdadAffiliation unknown1
Sara El MattarUniversity of Hassan II Casablanca1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijpeds.iaescore.com/index.php/IJPEDS/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Fuzzy Model Predictive Controller for Artificial Pancreas</dc:title>
	<dc:creator>El Hachimi, Mohamed</dc:creator>
	<dc:creator>Ballouk, Abdelhakim</dc:creator>
	<dc:creator>Baghdad, AbdNaceur</dc:creator>
	<dc:description xml:lang="en-US">This work consists on new tuning of Model Predictive Controllers using Fuzzy Logic method. Tree relevant parameters are automatically adjusted the prediction horizon Np, the input weight R and the output weight Q. The proposed controller is implemented in an Artificial Pancreas and tested under realistic conditions in a commercial platform of simulation. The result of the simulations revealed the success of such a method to improve the controller’s performances compared to the previous ones.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">University Hassan II</dc:contributor>
	<dc:date>2018-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/9074</dc:identifier>
	<dc:identifier>10.11591/ijpeds.v9.i3.pp1178-1185</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Power Electronics and Drive Systems (IJPEDS); Vol 9, No 3: September 2018; 1178-1185</dc:source>
	<dc:source>2722-256X</dc:source>
	<dc:source>2088-8694</dc:source>
	<dc:source>10.11591/ijpeds.v9.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/9074/11314</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 Mohamed El Hachimi, Abdelhakim Ballouk, AbdNaceur Baghdad</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>