Adaptive fuzzy logic control

Author

Kang, Hoon ; Vachtsevanos, George

Author_Institution

Sch. of Electr. Eng., Georgia Inst. of Technol., Atlanta, GA, USA

fYear

1992

fDate

8-12 Mar 1992

Firstpage

407

Lastpage

414

Abstract

A systematic design procedure for fuzzy linguistic controllers with adaptive or learning capability is introduced. The design is based on stability and hierarchy of identification and control. The fuzzy rule-base is stored in a fuzzy hypercube and the fuzzy control action is computed via a fuzzy inference mechanism. Initial conditions for the elements of a fuzzy hypercube are obtained by an offline fuzzy clustering mechanism with large-grain uncertainty. Two fuzzy algorithms are developed: the first one is a fuzzy identification-learning algorithm and the second is a fuzzy control-inferencing algorithm. The fuzzy identification-learning algorithm updates the membership functions on the action side of the rules and the fuzzy control-inferencing algorithm calculates fuzzy control data. This approach guarantees the stability, convergence, and robustness of the closed-loop feedback system

Keywords

adaptive control; control system synthesis; fuzzy control; identification; inference mechanisms; learning (artificial intelligence); stability; adaptive fuzzy logic control; closed-loop feedback system; control system synthesis; convergence; fuzzy hypercube; fuzzy inference mechanism; fuzzy linguistic controllers; fuzzy rule-base; identification; large-grain uncertainty; learning; offline fuzzy clustering mechanism; robustness; stability; Adaptive control; Clustering algorithms; Control systems; Fuzzy control; Fuzzy logic; Fuzzy systems; Hypercubes; Inference algorithms; Programmable control; Robust stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1992., IEEE International Conference on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0236-2

Type

conf

DOI

10.1109/FUZZY.1992.258648

Filename

258648