University of Anbar
  • Register
  • Login

Anbar Journal of Engineering Sciences

Notice

As part of Open Journals’ initiatives, we create website for scholarly open access journals. If you are responsible for this journal and would like to know more about how to use the editorial system, please visit our website at https://ejournalplus.com or
send us an email to info@ejournalplus.com

We will contact you soon

  1. Home
  2. Volume 13, Issue 2
  3. Author

Current Issue

By Issue

By Subject

Keyword Index

Author Index

Indexing Databases XML

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

Peer Review Process

News

Facts and Figures

Reviewers in AJES

A Review in Applications of Control Engineering Based on Genetic Algorithm

    yasameen kamil najm

Anbar Journal of Engineering Sciences, 2022, Volume 13, Issue 2, Pages 42-48
10.37649/aengs.2022.176356

  • Show Article
  • References
  • Download
  • Cite
  • Statistics
  • Share

Abstract

The most popular evolutionary search techniques are genetic algorithms (GAs). Even though they are frequently used to solve control engineering problems, they are currently not a common tool in the control engineer's toolbox. This may be due in part to the fact that there are currently few general overviews of the employment of GAs for control engineering problems, and that they are often reported on at computer science conferences rather than conferences for control engineers.
This review study is intended to assist researchers and practitioners in identifying prospective research issues, potential solutions, as well as advantages and disadvantages of each technique. This study gives a brief overview of contemporary a Genetic Algorithm (GA) in control systems. Additionally, offers a number of control techniques used with the GA that have undergone extensive research. The conclusion of this study listed in a table to show the effectiveness of GA in various control technique and which field didn’t used till the time of preparing this review.
Keywords:
    control engineering genetic algorithm evolutionary techniques
Main Subjects:
  • Control Engineering
  • Electrical Engineering
  • PDF (662 K)
  • XML
(2022). A Review in Applications of Control Engineering Based on Genetic Algorithm. Anbar Journal of Engineering Sciences, 13(2), 42-48. doi: 10.37649/aengs.2022.176356
yasameen kamil najm. "A Review in Applications of Control Engineering Based on Genetic Algorithm". Anbar Journal of Engineering Sciences, 13, 2, 2022, 42-48. doi: 10.37649/aengs.2022.176356
(2022). 'A Review in Applications of Control Engineering Based on Genetic Algorithm', Anbar Journal of Engineering Sciences, 13(2), pp. 42-48. doi: 10.37649/aengs.2022.176356
A Review in Applications of Control Engineering Based on Genetic Algorithm. Anbar Journal of Engineering Sciences, 2022; 13(2): 42-48. doi: 10.37649/aengs.2022.176356
  • RIS
  • EndNote
  • BibTeX
  • APA
  • MLA
  • Harvard
  • Vancouver

[1]        S. Katoch, S. S. Chauhan, and V. Kumar, A review on genetic algorithm: past, present, and future,” Multimedia Tools and Applications, vol. 80, no. 5. pp. 8091–8126, Oct. 2020, doi: 10.1007/s11042-020-10139-6.

[2]        P. Spronck, “An overview of genetic algorithmsapplied to control engineering problems,” Proc. Second Int. Conf. Mach. Learn. Cybern., no. November, pp. 1–6, 2004, doi: 10.1109/ICMLC.2003.1259761.

[3]        E. G. Shopova and N. G. Vaklieva-Bancheva, “BASIC—A genetic algorithm for engineering problems solution,” Computers & Chemical Engineering, vol. 30, no. 8, pp. 1293–1309, Jun. 2006, doi: 10.1016/j.compchemeng.2006.03.003.

[4]        D. E. F. Zbigniew Michalewicz, “How to solve it: modren heuristics,” Book, 2004.

[5]        A. Jayachitra and R. Vinodha, “Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor,” Adv. Artif. Intell., vol. 2014, pp. 1–8, 2014, doi: 10.1155/2014/791230.

[6]        A. Abdollahi, A. Forruzan Tabbar, and H. Khodadadi, “Optimal controller design for quadrotor by genetic algorithm with the aim of optimizing the response and control input signals,” Cumhur. Sci. J., vol. 36, no. 3, pp. 135–147, 2015, [Online]. Available: http://dergi.cumhuriyet.edu.tr/cumuscij/article/view/5000118362

[7]        L. Shao, N. Liu, and H. B. Zuo, “The Research on Temperature Control System of Heat Transfer Station Based on Genetic Algorithm PID Control,” Applied Mechanics and Materials, vol. 391, pp. 433–436, Sep. 2013, doi: 10.4028/www.scientific.net/amm.391.43

[8]        G. Mester, “Design of the fuzzy control systems based on genetic algorithm for intelligent robots,” Interdiscip. Descr. Complex Syst., vol. 12, no. 3, pp. 245–254, 2014, doi: 10.7906/indecs.12.3.4.

[9]        A. J. Ali, Z. Farej, and N. Sultan, “Performance evaluation of a hybrid fuzzy logic controller based on genetic algorithm for three phase induction motor drive,” Int. J. Power Electron. Drive Syst., vol. 10, no. 1, p. 117, 2019, doi: 10.11591/ijpeds.v10.i1.pp117-127.

[10]      A. Świć, D. Wołos, A. Gola, and G. Kłosowski,

 

“The use of neural networks and genetic algorithms to control low rigidity shafts machining,” Sensors (Switzerland), vol. 20, no. 17, pp. 1–23, 2020, doi: 10.3390/s20174683.

[11]      Q. Wang, H. Xi, F. Deng, M. Cheng, and G. Buja, “Design and analysis of genetic algorithm and BP neural network based PID control for boost converter applied in renewable power generations,” IET Renew. Power Gener., vol. 16, no. 7, pp. 1336–1344, 2022, doi: 10.1049/rpg2.12320.

[12]      J. P. Belletti Araque, A. Zavoli, D. Trotta, and G. De Matteis, “Genetic algorithm based parameter tuning for robust control of launch vehicle in atmospheric flight,” IEEE Access, vol. 9, pp. 108175–108189, 2021, doi: 10.1109/ACCESS.2021.3099006.

[13]      M. J. Mahmoodabadi, T. Soleymani, and M. A. Sahnehsaraei, “A hybrid optimal controller based on the robust decoupled sliding mode and adaptive feedback linearization,” Inf. Technol. Control, vol. 47, no. 2, pp. 295–309, 2018, doi: 10.5755/j01.itc.47.2.16288.

[14]      H. C. Tran, V. D. Tran, T. T. H. Le, M. T. Nguyen, and V. D. H. Nguyen, “Genetic algorithm implementation for optimizing linear quadratic algorithm to control acrobot robotic system,” Robot. Manag., vol. 23, no. 1, pp. 31–36, 2018.

[15]      K. Benbouabdallah and Z. Qi-Dan, “Improved genetic algorithm lyapunov-based controller for mobile robot tracking a moving target,” Res. J. Appl. Sci. Eng. Technol., vol. 5, no. 15, pp. 4023–4028, 2013, doi: 10.19026/rjaset.5.4471.

[16]      A. A. Taleizadeh, S. T. A. Niaki, M. B.   Aryanezhad, and A. F. Tafti, “A genetic algorithm to optimize multiproduct multiconstraint inventory control systems with stochastic replenishment intervals and discount,” Int. J. Adv. Manuf. Technol., vol. 51, no. 1–4, pp. 311–323, 2010, doi: 10.1007/s00170-010-2604-8.

[17]      M. Mossolly, K. Ghali, and N. Ghaddar, “Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm,” Energy, vol. 34, no. 1, pp. 58–66, 2009, doi: 10.1016/j.energy.2008.10.001.

[18]      J. Brown, M. Paternostro, and A. Ferraro, “Optimal quantum control via genetic algorithms,” pp. 1–11, 2022, [Online]. Available:  https://arxiv.org/abs/2206.14681v1

[19]      A. Zamuda and J. Brest, “Self-adaptive control parameters’ randomization frequency and propagations in differential evolution,” Swarm Evol. Comput., vol. 25, no. December, pp. 72–99, 2015, doi: 10.1016/j.swevo.2015.10.007.

[20]      E. Pellerin, L. Pigeon, and S. Delisle, “Self-adaptive parameters in genetic algorithms,” Data Min. Knowl. Discov. Theory, Tools, Technol. VI, vol. 5433, no. April 2004, p. 53, 2004, doi: 10.1117/12.542156.

[21]      N. Aguila-Camacho and M. A. Duarte-Mermoud, “Fractional adaptive control for an automatic voltage regulator,” ISA Trans., vol. 52, no. 6, pp. 807–815, 2013, doi: 10.1016/j.isatra.2013.06.005.

  • Article View: 31
  • PDF Download: 34
  • LinkedIn
  • Twitter
  • Facebook
  • Google
  • Telegram
  • Home
  • Glossary
  • News
  • Aims and Scope
  • Privacy Policy
  • Sitemap

This journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

This journal is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0)

Powered by eJournalPlus