Hybrid Fuzzy Logic Strategy for Soccer Robot Game

This paper presents a control strategy for the robot soccer game goalie using hybrid fuzzy logic systems. The strategy adopts the divide and conquer concept by decomposing the goalie’s task of defending the goal into four (4) categories. Consequently, each category is characterized by different goalie-ball situations and will be handled by a separate rule-base. The first fuzzy system takes the x-coordinates of the robot and the ball then outputs the category number that determines the rule-base to be used in the second fuzzy system. The second fuzzy system handles the goalie’s movements. It takes the current y-coordinate of the goalie-robot and the ball then outputs the y-coordinate of the goalie’s next position (destination) which is located along its line-of-action (a line with predefined x-coordinate where the goalie moves back and forth just in front of the goal it is defending). Experiment shows that this strategy is feasible, efficient, and robust.

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Magnetic Bearing Control Using Interval Type-2 Fuzzy Logic

Dynamic Systems and Control, Parts A and B ◽

10.1115/imece2005-82507 ◽

2005 ◽

Author(s):

Andra´s Simon ◽

George T. Flowers

Keyword(s):

Fuzzy Logic ◽

Expert Knowledge ◽

Magnetic Bearing ◽

Innovative Technology ◽

Rule Base ◽

Fuzzy Logic Systems ◽

Limited Effectiveness ◽

Logic Systems ◽

Radial Magnetic Bearing

Magnetic bearings are an exciting and innovative technology that has seen considerable advances in recent years. Being unstable by nature, these systems require active control. Most often linear techniques are used very successfully. On the other hand, there are applications where linear methods have limited effectiveness. Fuzzy logic control performs very well in nonlinear control situations where the plant parameters are either partially or mostly unidentified. Its effectiveness for nonlinear systems also offers advantages to magnetic bearing systems. Type-2 fuzzy logic systems represent significant advances over traditional fuzzy logic systems in general. These fuzzy logic systems are capable to deal with uncertainties which can be found in almost every practical system. Uncertainties stem from several sources; noise present in the position input signals, the location and shape of fuzzy sets and the fuzzy rule-base describing the operation of the fuzzy controller, among others. Since a mathe-matical model of the controlled plant is often only a conveniently close approximation of the real process at hand, a major challenge lies in the application of the control methods to real plants. Type-2 fuzzy logic and fuzzy logic systems in general tackle the control problem at hand using human reasoning based on rules and expert knowledge of the plant described by human expressions. The current work consist of model development, controller design, simulation and experimental validation. The basic simulation model consist of a horizontal shaft supported by a radial magnetic bearing. The magnetic bearing is modeled as a nonlinear element. The controller designs are implemented and tested using a bench-top rotor rig equipped with a radial magnetic bearing. Some representative results are presented and discussed.

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Special Issue: Fuzzy logic systems for transportation engineering

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189957 ◽

2021 ◽

pp. 1-8

Author(s):

Dalin Zhang ◽

Sabah Mohammed ◽

Alessandro Calvi

Keyword(s):

Fuzzy Logic ◽

Transportation Engineering ◽

Special Issue ◽

Fuzzy Logic Systems ◽

Logic Systems

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Design of back propagation optimized Nagar-Bardini structure-based interval type-2 fuzzy logic systems for fuzzy identification

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211006635 ◽

2021 ◽

pp. 014233122110066

Author(s):

Yang Chen ◽

Jiaxiu Yang

Keyword(s):

Fuzzy Logic ◽

Back Propagation ◽

Membership Functions ◽

Identification Problems ◽

Fuzzy Logic Systems ◽

Fuzzy Identification ◽

Logic Systems ◽

Academic Topic ◽

Interval Type

In recent years, fuzzy identification based on system identification theory has become a hot academic topic. Interval type-2 fuzzy logic systems (IT2 FLSs) have become a rising technology. This paper designs a type of Nagar-Bardini (NB) structure-based singleton IT2 FLSs for fuzzy identification problems. The antecedents of primary membership functions of IT2 FLSs are chosen as Gaussian type-2 primary membership functions with uncertain standard deviations. Then, the back propagation algorithms are used to tune the parameters of IT2 FLSs according to the chain rule of derivation. Compared with the type-1 fuzzy logic systems, simulation studies show that the proposed IT2 FLSs can obtain better abilities of generalization for fuzzy identification problems.

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