Benchmark of Fuzzy Controllers + I Optimized by Bio Heuristics

Grace Ruiz-Rivera; John Chapaca-Garzón; William Montalvo-López

doi:10.1007/978-3-031-70981-4_34

Benchmark of Fuzzy Controllers + I Optimized by Bio Heuristics

Grace Ruiz-Rivera
, John Chapaca-Garzón
, William Montalvo-López

Research Group in Electronics Control and Automation (GIECA)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Fuzzy control systems are used in different industrial processes, they work under a model of fuzzy sets or also called membership functions (MF), which are expressed with linguistic terms based on the operator’s expertise, which is why they can have a considerable degree of uncertainty. Therefore, they present a severe optimization problem. In the present paper, three bio-heuristic algorithms are implemented for the optimization of the membership functions of a fuzzy + I controller, used for the level control in a tank. The selected algorithms are the genetic algorithm (GA), particle swarm optimization algorithm (PSO), and evolutionary programming algorithm (EP). The controllers are evaluated based on the response of the control system, using the Integral of Absolute Error (IAE) of each optimized fuzzy + I controller. Welch’s ANOVA statistical test is performed to define which algorithm obtained a better performance, having interesting results from the point of view of automatic control.

Original language	English
Title of host publication	Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2023) - Innovations in Industrial Engineering and Robotics in Industry - Bridging the Gap Between Theory and Practical Application
Editors	Marcelo V. Garcia, Carlos Gordón-Gallegos, Asier Salazar-Ramírez, Carlos Nuñez
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	507-521
Number of pages	15
ISBN (Print)	9783031709807
DOIs	https://doi.org/10.1007/978-3-031-70981-4_34
State	Published - 2024
Event	International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2023 - Ambato, Ecuador Duration: 6 Nov 2023 → 10 Nov 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	797 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2023
Country/Territory	Ecuador
City	Ambato
Period	6/11/23 → 10/11/23

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

EP
Fuzzy control
GA
optimization
PSO

CACES Knowledge Areas

417A Electronics, Automation and Sound

Access to Document

10.1007/978-3-031-70981-4_34

Cite this

Ruiz-Rivera, G., Chapaca-Garzón, J., & Montalvo-López, W. (2024). Benchmark of Fuzzy Controllers + I Optimized by Bio Heuristics. In M. V. Garcia, C. Gordón-Gallegos, A. Salazar-Ramírez, & C. Nuñez (Eds.), Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2023) - Innovations in Industrial Engineering and Robotics in Industry - Bridging the Gap Between Theory and Practical Application (pp. 507-521). (Lecture Notes in Networks and Systems; Vol. 797 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-70981-4_34

Ruiz-Rivera, Grace ; Chapaca-Garzón, John ; Montalvo-López, William. / Benchmark of Fuzzy Controllers + I Optimized by Bio Heuristics. Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2023) - Innovations in Industrial Engineering and Robotics in Industry - Bridging the Gap Between Theory and Practical Application. editor / Marcelo V. Garcia ; Carlos Gordón-Gallegos ; Asier Salazar-Ramírez ; Carlos Nuñez. Springer Science and Business Media Deutschland GmbH, 2024. pp. 507-521 (Lecture Notes in Networks and Systems).

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abstract = "Fuzzy control systems are used in different industrial processes, they work under a model of fuzzy sets or also called membership functions (MF), which are expressed with linguistic terms based on the operator{\textquoteright}s expertise, which is why they can have a considerable degree of uncertainty. Therefore, they present a severe optimization problem. In the present paper, three bio-heuristic algorithms are implemented for the optimization of the membership functions of a fuzzy + I controller, used for the level control in a tank. The selected algorithms are the genetic algorithm (GA), particle swarm optimization algorithm (PSO), and evolutionary programming algorithm (EP). The controllers are evaluated based on the response of the control system, using the Integral of Absolute Error (IAE) of each optimized fuzzy + I controller. Welch{\textquoteright}s ANOVA statistical test is performed to define which algorithm obtained a better performance, having interesting results from the point of view of automatic control.",

keywords = "EP, Fuzzy control, GA, optimization, PSO",

author = "Grace Ruiz-Rivera and John Chapaca-Garz{\'o}n and William Montalvo-L{\'o}pez",

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Ruiz-Rivera, G, Chapaca-Garzón, J & Montalvo-López, W 2024, Benchmark of Fuzzy Controllers + I Optimized by Bio Heuristics. in MV Garcia, C Gordón-Gallegos, A Salazar-Ramírez & C Nuñez (eds), Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2023) - Innovations in Industrial Engineering and Robotics in Industry - Bridging the Gap Between Theory and Practical Application. Lecture Notes in Networks and Systems, vol. 797 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 507-521, International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2023, Ambato, Ecuador, 6/11/23. https://doi.org/10.1007/978-3-031-70981-4_34

Benchmark of Fuzzy Controllers + I Optimized by Bio Heuristics. / Ruiz-Rivera, Grace; Chapaca-Garzón, John; Montalvo-López, William.
Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2023) - Innovations in Industrial Engineering and Robotics in Industry - Bridging the Gap Between Theory and Practical Application. ed. / Marcelo V. Garcia; Carlos Gordón-Gallegos; Asier Salazar-Ramírez; Carlos Nuñez. Springer Science and Business Media Deutschland GmbH, 2024. p. 507-521 (Lecture Notes in Networks and Systems; Vol. 797 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Benchmark of Fuzzy Controllers + I Optimized by Bio Heuristics

AU - Ruiz-Rivera, Grace

AU - Chapaca-Garzón, John

AU - Montalvo-López, William

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - Fuzzy control systems are used in different industrial processes, they work under a model of fuzzy sets or also called membership functions (MF), which are expressed with linguistic terms based on the operator’s expertise, which is why they can have a considerable degree of uncertainty. Therefore, they present a severe optimization problem. In the present paper, three bio-heuristic algorithms are implemented for the optimization of the membership functions of a fuzzy + I controller, used for the level control in a tank. The selected algorithms are the genetic algorithm (GA), particle swarm optimization algorithm (PSO), and evolutionary programming algorithm (EP). The controllers are evaluated based on the response of the control system, using the Integral of Absolute Error (IAE) of each optimized fuzzy + I controller. Welch’s ANOVA statistical test is performed to define which algorithm obtained a better performance, having interesting results from the point of view of automatic control.

AB - Fuzzy control systems are used in different industrial processes, they work under a model of fuzzy sets or also called membership functions (MF), which are expressed with linguistic terms based on the operator’s expertise, which is why they can have a considerable degree of uncertainty. Therefore, they present a severe optimization problem. In the present paper, three bio-heuristic algorithms are implemented for the optimization of the membership functions of a fuzzy + I controller, used for the level control in a tank. The selected algorithms are the genetic algorithm (GA), particle swarm optimization algorithm (PSO), and evolutionary programming algorithm (EP). The controllers are evaluated based on the response of the control system, using the Integral of Absolute Error (IAE) of each optimized fuzzy + I controller. Welch’s ANOVA statistical test is performed to define which algorithm obtained a better performance, having interesting results from the point of view of automatic control.

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KW - PSO

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SN - 9783031709807

T3 - Lecture Notes in Networks and Systems

SP - 507

EP - 521

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A2 - Salazar-Ramírez, Asier

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Ruiz-Rivera G, Chapaca-Garzón J, Montalvo-López W. Benchmark of Fuzzy Controllers + I Optimized by Bio Heuristics. In Garcia MV, Gordón-Gallegos C, Salazar-Ramírez A, Nuñez C, editors, Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2023) - Innovations in Industrial Engineering and Robotics in Industry - Bridging the Gap Between Theory and Practical Application. Springer Science and Business Media Deutschland GmbH. 2024. p. 507-521. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-70981-4_34

Benchmark of Fuzzy Controllers + I Optimized by Bio Heuristics

Abstract

Publication series

Conference

Bibliographical note

Keywords

CACES Knowledge Areas

Access to Document

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