Stochastic fuzzy control - part I: theoretical derivation

Keigo Watanabe

Research output: Contribution to conferencePaperpeer-review

7 Citations (Scopus)


A stochastic fuzzy control is proposed by applying the stochastic control theory, instead of using a traditional fuzzy reasoning. We first solve a control problem of one-step predicted output tracking for linear stochastic systems. Next, we consider a dynamic multiple model adaptive control (MMAC) for the initial data distribution, under the uncertainties of the initial states. We further consider a static MMAC that can be applied for a case of completely unknown plants. It is then shown that a stochastic fuzzy control has some Gaussian potential functions as membership functions and can assign some a priori probabilities to the fuzzy sets or to the control rules, if the probability density function with respect to the output error is replaced by simple characteristic function. It is also cleared that the stochastic fuzzy control becomes a fuzzy control by assuming that all of the a priori probabilities are set to be equal at any control instant.

Original languageEnglish
Number of pages8
Publication statusPublished - Jan 1 1995
Externally publishedYes
EventProceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5) - Yokohama, Jpn
Duration: Mar 20 1995Mar 24 1995


OtherProceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5)
CityYokohama, Jpn

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics


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