Identification of Pneumatic Cylinder Friction Parameters Using Genetic Algorithms

J. Wang; J.D. Wang; N. Daw; Q.H. Wu

doi:10.1109/tmech.2004.823883

Identification of Pneumatic Cylinder Friction Parameters Using Genetic Algorithms

IEEE/ASME Transactions on Mechatronics ◽

10.1109/tmech.2004.823883 ◽

2004 ◽

Vol 9 (1) ◽

pp. 100-107 ◽

Cited By ~ 33

Author(s):

J. Wang ◽

J.D. Wang ◽

N. Daw ◽

Q.H. Wu

Keyword(s):

Genetic Algorithms ◽

Pneumatic Cylinder ◽

Friction Parameters

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Friction parameters identification and compensation of LuGre model base on genetic algorithms

Proceedings of the 2015 International Symposium on Computers and Informatics ◽

10.2991/isci-15.2015.33 ◽

2015 ◽

Cited By ~ 3

Author(s):

Yuqin Wen ◽

Ming Chu ◽

Hanxu Sun

Keyword(s):

Genetic Algorithms ◽

Parameters Identification ◽

Lugre Model ◽

Model Base ◽

Friction Parameters

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Hierarchical time-optimal control of a continuous copolymerization reactor during start-up or grade change operation using genetic algorithms

Computers & Chemical Engineering ◽

10.1016/s0098-1354(97)00186-5 ◽

1997 ◽

Vol 21 (1-2) ◽

pp. S1037-S1042

Author(s):

H Moo

Keyword(s):

Optimal Control ◽

Genetic Algorithms ◽

Time Optimal Control ◽

Grade Change ◽

Start Up ◽

Time Optimal ◽

Change Operation ◽

Grade Change Operation

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Genetic Algorithms—a Tool for OR?

Journal of the Operational Research Society ◽

10.1057/palgrave.jors.0470406 ◽

1996 ◽

Vol 47 (4) ◽

pp. 550-561 ◽

Cited By ~ 2

Author(s):

Kathryn A Dowsland

Keyword(s):

Genetic Algorithms

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Road Horizontal Alignment Design Method for Continuous Loop Area in Autonomous Vehicles Proving Ground Based on Genetic Algorithms

CICTP 2020 ◽

10.1061/9780784483053.163 ◽

2020 ◽

Author(s):

Junyi Chen ◽

Rubing Li ◽

Xingyu Xing ◽

Lu Xiong

Keyword(s):

Genetic Algorithms ◽

Autonomous Vehicles ◽

Design Method ◽

Horizontal Alignment ◽

Loop Area

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Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

Al-Kitab Journal for Pure Sciences ◽

10.32441/kjps.v1i1.87 ◽

2018 ◽

Vol 1 (1) ◽

pp. 2-19

Author(s):

Mahmood Sh. Majeed ◽

Raid W. Daoud

Keyword(s):

Genetic Algorithms ◽

Photonic Crystal ◽

Photonic Crystal Fiber ◽

New Method ◽

Proper Solution ◽

Artificial Intelligent ◽

Crystal Fiber ◽

Fitness Value ◽

First Pass ◽

Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

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