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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2019-Jun-20
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  1/2015 - 4

FPGA-Based Embedded System Architecture for Micro-Genetic Algorithms Applied to Parameters Optimization in Motion Control

JAEN-CUELLAR, A. Y. See more information about JAEN-CUELLAR, A. Y. on SCOPUS See more information about JAEN-CUELLAR, A. Y. on IEEExplore See more information about JAEN-CUELLAR, A. Y. on Web of Science, MORALES-VELAZQUEZ, L. See more information about  MORALES-VELAZQUEZ, L. on SCOPUS See more information about  MORALES-VELAZQUEZ, L. on SCOPUS See more information about MORALES-VELAZQUEZ, L. on Web of Science, ROMERO-TRONCOSO, R. See more information about  ROMERO-TRONCOSO, R. on SCOPUS See more information about  ROMERO-TRONCOSO, R. on SCOPUS See more information about ROMERO-TRONCOSO, R. on Web of Science, OSORNIO-RIOS, R. A. See more information about OSORNIO-RIOS, R. A. on SCOPUS See more information about OSORNIO-RIOS, R. A. on SCOPUS See more information about OSORNIO-RIOS, R. A. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,535 KB) | Citation | Downloads: 522 | Views: 2,165

Author keywords
control design, genetic algorithms, field programmable gate arrays, microprocessors, servo systems

References keywords
genetic(22), algorithm(17), optimization(12), systems(9), design(8), controller(8), control(8), applications(7), system(6), implementation(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 23 - 32
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01004
Web of Science Accession Number: 000352158600004
SCOPUS ID: 84924804553

Abstract
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Full text preview
Meta-heuristic techniques are powerful tools used to find an optimal solution for complex problems to which classical techniques find difficult to solve. The features among all the meta-heuristic techniques are the high amount of computational resources spent on their implementation and the computing effort generated on their execution. For this reason, many works have proposed their use on the base of software methodologies without achieving online or real-time performance. In the present work, two strategies that implement the Genetic Algorithms are presented by using the micro-population concept with the objective of reducing computational resources, increasing the heuristic search speed, and providing simplicity in its design. Both strategies are implemented in hardware architecture; the first, as a software strategy in a proprietary embedded processor, the second, as a hardware co-processor unit. In order to validate the proposed approaches, several tests to optimize a motion controller in a servo system are presented and compared with a classical tuning technique.


References | Cited By  «-- Click to see who has cited this paper

[1] S. Panda, and N. P. Padhy, "Comparison of Particle Swarm Optimization and Genetic Algorithm for FACTS-Based Controller Design." Applied Soft Computing, vol. 8, no. 4, pp. 1418-1427, 2008.
[CrossRef] [Web of Science Times Cited 189] [SCOPUS Times Cited 237]


[2] M. El Semelawy, A. O. Nassef, and A. A. El Damatty, "Design of Prestressed Concrete Flat Slab Using Modern Heuristic Optimization Techniques." Expert Systems with Applications, vol. 39, no. 5, pp. 5758-5766, 2012.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 18]


[3] M. W. Bloomfield, J. E. Herencia, and P. M. Weaver, "Analysis and Benchmarking of Meta-Heuristic Techniques for Lay-up Optimization." Computers & Structures, vol. 88, no. 5-6, pp. 272-282, 2010.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 28]


[4] K. Hammouche, M. Diaf, and P. Siarry, "A Comparative Study of Various Meta-Heuristic Techniques Applied to the Multilevel Thresholding Problem." Engineering Applications of Artificial Intelligence, vol. 23, no. 5, pp. 676-688, 2010.
[CrossRef] [Web of Science Times Cited 100] [SCOPUS Times Cited 124]


[5] D. E. Goldberg, "Genetic Algorithms in Search, Optimization, and Machine Learning" Addison-Wesley Longman Publishing Co, Inc., 1989.

[6] J. Kennedy, and R. Eberhart., "Particle Swarm Optimization", pp. 1942-1948. Proc. ICNN, 1995.

[7] M. Dorigo, and C. Blum, "Ant Colony Optimization Theory: A Survey." Theoretical Computer Science, vol. 344, no. 2-3, pp. 243-278, 2005.
[CrossRef] [Web of Science Times Cited 942] [SCOPUS Times Cited 1276]


[8] L. Ingber, "Simulated Annealing: Practice versus Theory." Mathematical and Computer Modelling, vol. 18, no. 11, pp. 29-57, 1993.
[CrossRef] [Web of Science Times Cited 532] [SCOPUS Times Cited 740]


[9] B. Nagaraj, and N. Murugananth, "A Comparative Study of PID Controller Tuning Using GA, EP, PSO and ACO." IEEE International Conference on Communication Control and Computing Technologies (ICCCCT), pp. 305-313, 2010.
[CrossRef] [SCOPUS Times Cited 66]


[10] I. J. Graham, K. Case, and R. L. Wood, "Genetic Algorithms in Computer-Aided Design." Journal of Materials Processing Technology, vol. 117, no. 1-2, pp. 216-221, 2001.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 14]


[11] S. S. Rao, "Engineering Optimization Theory and Practice", pp. 693-730, John Wiley & Sons Inc, 2009.

[12] L. Di, Z. Lei, and L. Xiang, "PID Parameter Optimization of Shunting and Winch Control System in Coal Transportation Based Online Adaptive Genetic Algorithm." International Conference on E-Product E-Service and E-Entertainment (ICEEE), pp. 1-4, 2010.
[CrossRef] [SCOPUS Times Cited 3]


[13] A. J. A. Nazir, Gautham, R. Surajan, and L. S. Binu, "A Simplified Genetic Algorithm for Online Tuning of PID Controller in LabView." World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 1516-1519, 2009.
[CrossRef] [SCOPUS Times Cited 7]


[14] Z. Weiping, D. Yu, and Z. Hu. "Parameters Optimization for Small Helicopter Highly Controller Based on Genetic Algorithm." In World Automation Congress (WAC), 2012, pp. 1-4, 2012.

[15] K. A. Mohideen, G. Saravanakumar, K. Valarmathi, D. Devaraj, and T. K. Radhakrishnan, "Real-Coded Genetic Algorithm for System Identification and Tuning of a Modified Model Reference Adaptive Controller for a Hybrid Tank System." Applied Mathematical Modelling, vol. 37, no. 6, pp. 3829-3847, 2013.
[CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 46]


[16] H. V. Hultmann-Ayala, and L. dos Santos-Coelho, "Tuning of PID Controller Based on a Multiobjective Genetic Algorithm Applied to a Robotic Manipulator." Expert Systems with Applications, vol. 39, no. 10, pp. 8968-8974, 2012.
[CrossRef] [Web of Science Times Cited 81] [SCOPUS Times Cited 104]


[17] L. Hong-Yan, "The Adaptive Niche Genetic Algorithm for Optimum Design of PID Controller." International Conference on Machine Learning and Cybernetics, pp. 487-491, 2007.
[CrossRef] [SCOPUS Times Cited 2]


[18] T. Wu, Y. Cheng, J. Tan, and T. Zhou, "The Application of Chaos Genetic Algorithm in the PID Parameter Optimization." 3rd International Conference on Intelligent System and Knowledge Engineering (ISKE), vol. 1, pp. 230-234, 2008.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 7]


[19] Z. S. Abo-Hammour, O. M. K. Alsmadi, S. I. Bataineh, M. A. Al-Omari, and N. Affach, "Continuous Genetic Algorithms for Collision-free Cartesian Path Planning of Robot Manipulators", International Journal of Advanced Robotic Systems, vol. 8, no. 6, pp. 14-36, 2011.
[CrossRef] [Web of Science Times Cited 17]


[20] A. Ghanbari, and SMRS. Noorani, "Optimal Trajectory Planning for Design of a Crawling Gait in a Robot Using Genetic Algorithm." International Journal of Advanced Robotic Systems, vol. 8, no. 1, pp. 29-36, 2011.
[CrossRef] [Web of Science Times Cited 17]


[21] X. Wang, T. Lu, and P. Zhang, "State Generation Method for Humanoid Motion Planning Based on Genetic Algorithm." International Journal of Advanced Robotic Systems, vol. 9, no. 23, pp. 1-8, 2012, .
[CrossRef] [Web of Science Times Cited 1]


[22] W. Tang, and L. Yip, "Hardware Implementation of Genetic Algorithms Using FPGA." The 47th Midwest Symposium on Circuits and Systems (MWSCAS), vol. 1, pp. 549-552, 2004.
[CrossRef]


[23] Y. Chen, and Q. Wu, "Design and Implementation of PID Controller Based on FPGA and Genetic Algorithm." International Conference on Electronics and Optoelectronics (ICEOE), vol. 4, pp. 308-311, 2011.
[CrossRef] [SCOPUS Times Cited 12]


[24] Z. Yan-Cong, G. Jun-Hua, D. Yong-Feng, and H. Huan-Ping, "Implementation of Genetic Algorithm for TSP Based on FPGA." In Control and Decision Conference, pp. 2226-2231, 2011.
[CrossRef] [SCOPUS Times Cited 4]


[25] B. Vasumathi, and S. Moorthi, "Implementation of Hybrid ANN-PSO Algorithm on FPGA for Harmonic Estimation." Engineering Applications of Artificial Intelligence, vol. 25, no. 3, pp. 476-483, 2012.
[CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 48]


[26] G. Mamdoohi, A. F. Abas, K. Samsudin, N. H. Ibrahim, A. Hidayat, and M. A. Mahdi, "Implementation of Genetic Algorithm in an Embedded Microcontroller-Based Polarization Control System." Engineering Applications of Artificial Intelligence, vol. 25, no. 4, pp. 869-873, 2012.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 11]


[27] H. R. Mahdiani, A. Banaiyan, M. H. S. Javadi, S. M. Fakhraie, and C. Lucas, "Defuzzification Block: New Algorithms, and Efficient Hardware and Software Implementation Issues." Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 162-172, 2013.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 18]


[28] B. H. Dennis, and G. S. Dulikravich, "Optimization of Magneto-Hydrodynamic Control of Diffuser Flows Using Micro-Genetic Algorithms and Least-Squares Finite Elements." Finite Elements in Analysis and Design, vol. 37, no. 5, pp. 349-363, 2001.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 26]


[29] A. C. Coello-Coello, and G. Pulido-Toscano., "A micro-Genetic Algorithm for multi-objective optimization," Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2001(1993), pp. 126-140.

[30] P. C. Ribas, L. Yamamoto, H. L. Polli, L. V. R. Arruda, and F. Neves-Jr, "A Micro-Genetic Algorithm for Multi-Objective Scheduling of a Real World Pipeline Network." Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 302-313, 2013.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 17]


[31] A. Patelli, and L. Ferariu, "Elite Based Multiobjective Genetic Programing in Nonlinear Systems Identification." Advances in Electrical and Computer Engineering, vol. 10, no. 1, pp. 94-99, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 4]


[32] A. Rezazadeh, "Genetic Algorithm Based Servo System Parameter Estimation During Transients." Advances in Electrical and Computer Engineering, vol. 10, no. 2, pp. 77-81, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 7]


[33] A. Melnyk, and V. Melnyk, "Self-Configurable FPGA-Based Computer Systems." Advances in Electrical and Computer Engineering, vol. 13, no. 2, pp. 33-38, 2013.
[CrossRef] [Full Text] [Web of Science Times Cited 6] [SCOPUS Times Cited 8]


[34] J. Tal, "Step by Step Design of Motion Control Systems." Galil Motion Control Inc, 1994.

[35] R. A. Osornio-Rios, R. J. Romero-Troncoso, G. Herrera-Ruiz, and R. Castaneda-Miranda, "The Application of Reconfigurable Logic to High Speed CNC Milling Machines Controllers." Control Engineering Practice, vol. 16, no. 6, pp. 674-684, 2008.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 39]




References Weight

Web of Science® Citations for all references: 2,108 TCR
SCOPUS® Citations for all references: 2,866 TCR

Web of Science® Average Citations per reference: 59 ACR
SCOPUS® Average Citations per reference: 80 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-09-12 20:04 in 205 seconds.




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