Fuzzy clustering for multiple-model approaches in system identification and control

Authors:
R. Babuska;M. Oosterom
Affiliations:
Delft University of Technology, Department of Information Technology and Systems, Control Engineering Laboratory, P.O.Box 5031 2600 GA Delft, The Netherlands;Delft University of Technology, Department of Information Technology and Systems, Control Engineering Laboratory, P.O.Box 5031 2600 GA Delft, The Netherlands
Venue:
Granular computing
Year:
2001

Citing 9
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Abstract

A review of fuzzy clustering and its use in the data-driven construction of nonlinear models and controllers is given. The focus is on algorithms of the fuzzy c-means type. Two application examples are presented: automated design of operating points for gain scheduling in flight control systems and nonlinear black-box identification. In the latter case, a comparison with an alternative technique is given. It is shown that fuzzy clustering is an effective technique for the decomposition of a complex nonlinear problem into a set of simpler local problems.