Adaptive Fuzzy-Lyapunov Controller Using Biologically Inspired Swarm Intelligence | Zendy

Alejandro Carrasco Elizalde | Zendy; Peter Goldsmith | Zendy

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Adaptive Fuzzy-Lyapunov Controller Using Biologically Inspired Swarm Intelligence

Author(s) -

Alejandro Carrasco Elizalde,

Peter Goldsmith

Publication year - 2008

Publication title -

applied bionics and biomechanics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.397

H-Index - 23

eISSN - 1754-2103

pISSN - 1176-2322

DOI - 10.1155/2008/767680

Subject(s) - swarm behaviour , fuzzy logic , swarm intelligence , robustness (evolution) , computer science , fuzzy control system , lyapunov function , controller (irrigation) , adaptive neuro fuzzy inference system , flocking (texture) , artificial intelligence , control engineering , neuro fuzzy , control theory (sociology) , engineering , machine learning , control (management) , nonlinear system , biology , biochemistry , particle swarm optimization , agronomy , composite material , gene , physics , materials science , quantum mechanics

The collective behaviour of swarms produces smarter actions than those achieved by a single individual. Colonies of ants, flocks of birds and fish schools are examples of swarms interacting with their environment to achieve a common goal. This cooperative biological intelligence is the inspiration for an adaptive fuzzy controller developed in this paper. Swarm intelligence is used to adjust the parameters of the membership functions used in the adaptive fuzzy controller. The rules of the controller are designed using a computing-with-words approach called Fuzzy-Lyapunov synthesis to improve the stability and robustness of an adaptive fuzzy controller. Computing-with-words provides a powerful tool to manipulate numbers and symbols, like words in a natural language.

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