An introduction to fuzzy control
An introduction to fuzzy control
IEEE Spectrum
Optimal adaptive fuzzy control for a class of unknown nonlinear systems
WSEAS Transactions on Systems and Control
Control of electrical drives based on fuzzy logic
WSEAS Transactions on Systems and Control
Stabilization of fuzzy control systems
WSEAS Transactions on Systems and Control
Adaptive fuzzy tracking control of nonlinear systems
WSEAS Transactions on Systems and Control
On some properties of fuzzy systems
ISPRA'09 Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation
An approach to fuzzy control of nonlinear systems: stability and design issues
IEEE Transactions on Fuzzy Systems
An application of fuzzy time series to improve ISE forecasting
WSEAS Transactions on Mathematics
Transformation of fuzzy state space model of a boiler system: a graph theoretic approach
WSEAS Transactions on Mathematics
A fuzzy controller with various T-norms applied in robot navigation
WSEAS Transactions on Systems and Control
Torque control strategy for parallel hybrid electric vehicles using fuzzy logic
WSEAS TRANSACTIONS on SYSTEMS
Green supply implementation based on fuzzy QFD: an application in GPLM system
WSEAS TRANSACTIONS on SYSTEMS
Methods to design fuzzy controllers
ACMOS'09 Proceedings of the 11th WSEAS international conference on Automatic control, modelling and simulation
Fuzzy time series model incorporating predictor variables and interval partition
WSEAS Transactions on Mathematics
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The paper presents a short review of some main properties of the most general fuzzy systems used in a large class of practical applications. The fuzzy systems, implemented using different rule bases, fuzzy values, membership functions, fuzzyfication and defuzzification methods, may be classified based on these fuzzy methods used in their development. This paper proposes a unitary theory for describing Mamdani fuzzy controllers based on their characteristics. Fuzzy rules bases may be seen as fuzzy applications between fuzzy sets. These rule bases have algebraic properties as: commutative law, neutral element and symmetric elements. The fuzzy systems may be analytical described with MISO, SISO and gain transfer characteristics, which may be calculated using computer programs. The fuzzy systems have algebraic properties as: commutative law and symmetric elements. These algebraic properties may be noticed on their transfer characteristics. The fuzzy systems have a variable gain with their inputs. Some gain characteristics values are presented. The rule bases and the fuzzy systems have the sector property. The paper emphasizes, based on the transfer characteristics, planar and spatial sector properties useful in stability analysis with Lyapunov techniques. The transfer characteristics, system linear characteristic around the origin and the gain in origin are usefull in the design of fuzzy PID controllers. Based on transient characteristics of fuzzy control systems some quality criteria are presented. The fuzzy systems used in control assure better control quality criteria and a greater robustness at disturbances effects and at the error at the identification of controlled processes parameters.