Genetic algorithms with variable mutation rates: Application to fuzzy logic controller design

1997 ◽  
Vol 211 (2) ◽  
pp. 157-167 ◽  
Author(s):  
D T Pham ◽  
D Karaboga
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Mutation Rate ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Mutation Rates ◽  
Fuzzy Logic Controllers ◽  
Delayed System ◽  
Simulation Results ◽  
Logic Controllers

Three variable mutation rate strategies for improving the performance of genetic algorithms (GAs) are described. The problem of optimizing fuzzy logic controllers is used to evaluate a GA adopting these strategies against a GA employing a static mutation regime. Simulation results for a second-order time-delayed system controlled by fuzzy logic controllers (FLCs) obtained using the different GAs are presented.

A survey of Type-2 fuzzy logic controller design using nature inspired optimization

2020 ◽  
Vol 39 (5) ◽  
pp. 6169-6179
Author(s):  
Fevrier Valdez ◽  
Oscar Castillo ◽  
Prometeo Cortes-Antonio ◽  
Patricia Melin
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Controller Design ◽  
Optimization Methods ◽  
Fuzzy Logic Controllers ◽  
Logic Control ◽  
Optimal Type ◽  
Logic Controllers

In this paper, we are presenting a survey of research works dealing with Type-2 fuzzy logic controllers designed using optimization algorithms inspired on natural phenomena. Also, in this review, we analyze the most popular optimization methods used to find the important parameters on Type-1 and Type-2 fuzzy logic controllers to improve on previously obtained results. To this end have included a summary of the results obtained from the web of science database to observe the recent trend of using optimization methods in the area of optimal type-2 fuzzy logic control design. Also, we have made a comparison among countries of the network of researchers using optimization methods to analyze the distribution and impact of the papers.

Control of a Projectile Smart Fin Using an Inverse Dynamics-Based Fuzzy Logic Controller

2007 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Woosoon Yim ◽  
Paul Weinacht ◽  
Venkat Mudupu
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Inverse Dynamics ◽  
Operating Conditions ◽  
Attractive Alternative ◽  
Fuzzy Logic Controllers ◽  
Novel Approach ◽  
External Moment ◽  
Element Approach ◽  
Simulation Results

The objective of this paper is to explore a method for the design of fuzzy logic controller for a smart fin used to control the pitch and yaw attitudes of a subsonic projectile during flight. Piezoelectric actuators are an attractive alternative to hydraulic actuators commonly used in this application due to their simplicity. The proposed cantilever-shaped actuator can be fully enclosed within the hollow fin with one end fixed to the rotation axle of the fin while the other end is pinned at the trailing edge of the fin. The paper includes a dynamic model of the system based on the finite element approach. The model includes external moment due to aerodynamic effects. This paper presents a novel approach for automatically creating fuzzy logic controllers for the fin. This approach uses the inverse dynamics of the smart fin system to determine the ranges of the variables of the controllers. Simulation results show that the proposed controller can successfully drive smart fin under various operating conditions.

Optimization of Fuzzy Logic Controllers with Rule Base Size Reduction using Genetic Algorithms

2015 ◽  
Vol 14 (05) ◽  
pp. 1063-1092 ◽  
Author(s):  
Pintu Chandra Shill ◽  
M. A. H. Akhand ◽  
MD. Asaduzzaman ◽  
Kazuyuki Murase
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Degrees Of Freedom ◽  
Rule Learning ◽  
Size Reduction ◽  
Rule Base ◽  
Second Phase ◽  
Fuzzy Logic Controllers ◽  
Base Size ◽  
Logic Controllers

In this paper, we present the automatic design methods with rule base size reduction for fuzzy logic controllers (FLCs) through real and binary coded coupled genetic algorithms (GAs). The adaptive schema is divided into two phases: the first phase is concerned with optimizing the FLCs membership functions and second phase called rule learning and reducing phase which automatically generates the fuzzy rules as well as determines the minimum number of rules required for building the fuzzy models. In the second phase, the redundant rules are removed by setting their all consequent weight factor to zero and merging the conflicting rules during the learning process. The first and second phases are carried out by the real and binary coded coupled GAs, respectively. Optimizing the MFs with learning and reducing rule base concurrently represents a way to maximize the performance of a FLC. The control algorithm is successfully tested for intelligent control of two degrees of freedom inverted pendulum. Finally, the simulation studies exhibits the better or competitive performance of the proposed method when compared with the existing methods.

A Single Phase Anti-Swing Fuzzy Logic Controller for an Overhead Crane With Hoisting

2006 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Jamil M. Renno ◽  
Kamal A. F. Moustafa
Keyword(s):  
Fuzzy Logic ◽  
Single Phase ◽  
Fuzzy Logic Controller ◽  
Inverse Dynamics ◽  
Operating Conditions ◽  
Overhead Crane ◽  
Fuzzy Logic Controllers ◽  
Overhead Cranes ◽  
Novel Approach ◽  
Logic Controllers

This paper presents a novel approach for automatically creating anti-swing fuzzy logic controllers for overhead cranes with hoisting. This approach uses the inverse dynamics of the overhead crane to determine the ranges of the variables of the controllers. The control action is distributed among three fuzzy logic controllers (FLCs): travel controller, hoist controller, and anti-swing controller. Simulation examples show that the proposed controller can successfully drive overhead cranes under various operating conditions.

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