This paper considers a hybrid approach to the design of adaptive fuzzy controllers in which two different learning algorithms are combined together to achieve an unproved global design strategy. Namely, a GA is devised to optimize all the configuration parameters of the fuzzy controller, including the number of membership functions and rules, while a Lyapunov-based adaptation law is used to perform a fast and fine tuning of the output singletons of the controller. A hardware non-linear benchmark is used to emphasize the particular effectiveness of the proposed approach in attacking experimental problems.
|Titolo:||A hybrid approach to adaptive fuzzy control based on genetic algorithms|
|Data di pubblicazione:||2004|
|Nome del convegno:||2004 IEEE International Conference on Systems, Man & Cybernetics|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/ICSMC.2004.1400902|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|