Title :
Adaptive fuzzy logic control
Author :
Kang, Hoon ; Vachtsevanos, George
Author_Institution :
Sch. of Electr. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
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;
Conference_Titel :
Fuzzy Systems, 1992., IEEE International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-0236-2
DOI :
10.1109/FUZZY.1992.258648
