• DocumentCode
    2902551
  • Title

    Developing a type-2 FLC through embedded type-1 FLCs

  • Author

    Hagras, Hani

  • Author_Institution
    German Univ. in Cairo, Cairo
  • fYear
    2008
  • fDate
    1-6 June 2008
  • Firstpage
    148
  • Lastpage
    155
  • Abstract
    Type-1 fuzzy logic controllers (FLCs) have been widely employed in many control applications as they give a good performance and it is relatively easy to extract the type-1 FLC parameters from experts. However, type-1 FLCs cannot fully handle the encountered uncertainties in changing unstructured environments as they use crisp type-1 fuzzy sets. Consequently, in order for type-1 FLCs to provide a satisfactory performance in face of high levels of uncertainties, some common practices are followed including continuously tuning the type-1 FLC or providing a set of type-1 FLCs where each FLC handles specific operation conditions. Alternatively, type-2 FLCs can handle uncertainties to give a better control performance. However, it is relatively challenging to extract from experts the footprint of uncertainty (FOU) information and consequently the type-2 fuzzy sets for type-2 FLCs. In this paper, we will present a novel method for generating the input and output type-2 fuzzy sets so that their FOUs can capture the faced uncertainties. The proposed method will generate a type-2 FLC that will try to embed the type-1 FLCs corresponding to the various operation conditions faced so far besides embedding a large number of other embedded type-1 FLCs. This will allow the type-2 FLC to handle the uncertainties trough a big number of embedded type-1 FLCs to produce a smooth and robust control performance. We will show through real world experiments how the developed type-2 FLC will handle the uncertainties and give a smooth control response that outperforms the individual and aggregated type-1 FLCs.
  • Keywords
    fuzzy control; fuzzy set theory; robust control; uncertain systems; footprint of uncertainty information; fuzzy logic controllers; fuzzy sets; robust control performance; uncertainty levels; Cities and towns; Data mining; Degradation; Fuzzy logic; Fuzzy sets; Humans; Robust control; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1098-7584
  • Print_ISBN
    978-1-4244-1818-3
  • Electronic_ISBN
    1098-7584
  • Type

    conf

  • DOI
    10.1109/FUZZY.2008.4630358
  • Filename
    4630358