An important subject in fuzzy control theory is tuning of a fuzzy controller. If one wants to tune a fuzzy controller, one can focus on the choice of rules, membership functions, number of input and output fuzzy sets and their degree of overlapping, implication, and connection operations, and defuzzification method. All these choices are closely related and in no way independent of each other. We describe six important defuzzification methods and their respective merits and shortcomings, dependent on the rules, domains, etc. Further, we give an alternative approach for the case in which the output fuzzy sets have different shapes or are asymmetrical. We illustrate this by several examples.
[1]
Ebrahim H. Mamdani,et al.
A linguistic self-organizing process controller
,
1979,
Autom..
[2]
E. H. Mamdani,et al.
Learning Control Algorithms in Real Dynamic Systems
,
1974
.
[3]
J. Hellendoorn,et al.
Reasoning with fuzzy logic
,
1990
.
[4]
Reza Langari,et al.
A defuzzification strategy for a fuzzy logic controller employing prohibitive information in command formulation
,
1992,
[1992 Proceedings] IEEE International Conference on Fuzzy Systems.
[5]
T. Yamakawa.
Stablization of an inverted pendulum by a high-speed fuzzy logic controller hardware system
,
1989
.