Simplifying fuzzy rule base of multiple input multiple output systems by constructing multi-layer fuzzy controller

2002 ◽  
Author(s):  
M. Rohmanuddin ◽  
Houw-Liong The ◽  
A.S. Ahmad ◽  
Y.Y. Nazaruddin
Keyword(s):  
Fuzzy Controller ◽  
Multiple Input Multiple Output ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Fuzzy Rule Base ◽  
Multiple Input ◽  
Input Multiple Output

A Fuzzy Driver on the Handling Track

1996 ◽  
Vol 118 (2) ◽  
pp. 380-386 ◽  
Author(s):  
M. Wheeler ◽  
R. Shoureshi
Keyword(s):  
High Speed ◽  
Multiple Input Multiple Output ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Good Test ◽  
System A ◽  
Logic Control ◽  
Human Driver ◽  
Multiple Input ◽  
Input Multiple Output

Maneuvering a car around a handling track in minimum time is a challenging task for a driver. The car at high speed is a complex nonlinear Multiple-Input-Multiple-Output dynamic system. A driver must spend many hours learning the skills necessary to control this system proficiently. This complex task is a good test for fuzzy logic control, supporting the premise: humans control complex systems using simple rules. A few fuzzy driving rules are devised to operate on the same inputs and outputs a human driver would use. These rules are encoded in a fuzzy rule base and used to control the system. The resulting fuzzy handling controller is demonstrated in a simulation. A single set of rules is shown to perform well on many different track geometries, illustrating the robustness of the system to a changing environment.

Implementation of a hexapod mobile robot with a fuzzy controller

Robotica ◽  
2005 ◽  
Vol 23 (6) ◽  
pp. 681-688 ◽  
Author(s):  
Makoto Kern ◽  
Peng-Yung Woo
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Robot Navigation ◽  
Fuzzy Rule ◽  
Sensor Data ◽  
Rule Base ◽  
Fuzzy Rule Base ◽  
Different Types ◽  
Autonomous Robot Navigation

Fuzzy logic has features that are particular attractive in light of the problems posed by autonomous robot navigation. Fuzzy logic allows us to model different types of uncertainty and imprecision. In this paper, the implementation of a hexapod mobile robot with a fuzzy controller navigating in unknown environments is presented. The robot, MKIII, interprets input sensor data through the comparison of values in its fuzzy rule base and moves accordingly to avoid obstacles. Results of trial run experiments are presented.

Fuzzy Rule Base Design With Probabilistic Weights

2016 ◽  
Author(s):  
Leonardo G. Melo ◽  
Luís A. Lucas ◽  
Myriam R. Delgado
Keyword(s):  
Fuzzy Rule ◽  
Rule Base ◽  
Fuzzy Rule Base ◽  
Base Design
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