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Stable Adaptive Control Using Fuzzy Systems and Neural Networks

  • Jeffrey T. Spooner
  • Kevin M. Passino
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 16)

Abstract

Fuzzy controllers have stirred a great deal of excitement in some circles since they allow for the simple inclusion of heuristic knowledge about how to control a plant rather than requiring exact mathematical models. This can sometimes lead to good controller designs in a very short period of time. In situations where heuristics do not provide enough information to specify all the parameters of the fuzzy controller a priori, researchers have introduced adaptive schemes that use data gathered during the on-line operation of the controller, and special adaptation heuristics, to automatically learn these parameters (see e.g. [1] – [12] or the References therein). To date, stability conditions have not been provided for any of the approaches in [1] – [12], but Langari and Tomizuka [13] and others have developed stability conditions for (non-adaptive) fuzzy controllers and recently several innovative stable adaptive fuzzy control schemes have been introduced [14] – [17]. Moreover, closely related neural control approaches have been studied [18] – [23]. In this article, we seek to introduce an adaptive fuzzy or neural control approach which is guaranteed to operate properly under less restrictive assumptions and for more general continuous-time nonlinear systems.

Keywords

Adaptive Control Fuzzy System Fuzzy Control Fuzzy Controller Linear Quadratic Regulator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Jeffrey T. Spooner
    • 1
  • Kevin M. Passino
    • 1
  1. 1.Dept. Electrical EngineeringThe Ohio State UniversityColumbusUSA

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