Fuzzy controller theory: limit theorems for linear fuzzy control rules
Automatica (Journal of IFAC)
Fuzzy control theory: The linear case
Fuzzy Sets and Systems
Fuzzy control theory: a nonlinear case
Automatica (Journal of IFAC)
Pad-analysis of fuzzy control stability
Fuzzy Sets and Systems
Design of fuzzy logic controllers based on generalized T-operators
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
Fuzzy control rules and their natural laws
Fuzzy Sets and Systems
Analysis and design of fuzzy control system
Fuzzy Sets and Systems
Studies on the output of fuzzy controller with multiple inputs
Fuzzy Sets and Systems
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Fuzzy control of unknown multiple-input—multiple-output plants
Fuzzy Sets and Systems
Rule mapping fuzzy controller design
Fuzzy Sets and Systems
A neural fuzzy framework for system mapping applications
Knowledge-Based Systems
A novel fuzzy framework for nonlinear system control
Fuzzy Sets and Systems
Development of a Novel Robotic Catheter Manipulating System with Fuzzy PID Control
International Journal of Intelligent Mechatronics and Robotics
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In this paper, the output structure of multiple-input-single-output fuzzy logic controller (FLCr) is studied. The input and output variables of the FLCr are all characterized by normal triangular-shaped membership functions and fuzzy partitions are considered for corresponding universes of discourse. A special mapping, linear rule mapping [(Fuzzy Sets and Systems 57 (1993) 149)], which describes the relationship between the input variables and the output variables of one rule is used. The conclusion proposed is that the output of multiple-input-single-output FLCr can be represented by the convex linear combination of its crisp input variables. A precise mathematical model of FLCr, which is helpful to analyze fuzzy control and tune the fuzzy logic controller, is given.