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

Grupo de Investigación en Electrónica Control y Automatización (GIECA)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	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
Editores	Marcelo V. Garcia, Carlos Gordón-Gallegos, Asier Salazar-Ramírez, Carlos Nuñez
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	507-521
Número de páginas	15
ISBN (versión impresa)	9783031709807
DOI	https://doi.org/10.1007/978-3-031-70981-4_34
Estado	Publicada - 2024
Evento	International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2023 - Ambato, Ecuador Duración: 6 nov. 2023 → 10 nov. 2023

Serie de la publicación

Nombre	Lecture Notes in Networks and Systems
Volumen	797 LNNS
ISSN (versión impresa)	2367-3370
ISSN (versión digital)	2367-3389

Conferencia

Conferencia	International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2023
País/Territorio	Ecuador
Ciudad	Ambato
Período	6/11/23 → 10/11/23

Nota bibliográfica

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

Areas de Conocimiento del CACES

417A Electrónica, automatización y sonido

Acceder al documento

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

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Ruiz-Rivera, G., Chapaca-Garzón, J., & Montalvo-López, W. (2024). Benchmark of Fuzzy Controllers + I Optimized by Bio Heuristics. En 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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title = "Benchmark of Fuzzy Controllers + I Optimized by Bio Heuristics",

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. En 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución de conferencia › revisión exhaustiva

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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M3 - Conference contribution

AN - SCOPUS:85214348973

SN - 9783031709807

T3 - Lecture Notes in Networks and Systems

SP - 507

EP - 521

BT - 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

A2 - Garcia, Marcelo V.

A2 - Gordón-Gallegos, Carlos

A2 - Salazar-Ramírez, Asier

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PB - Springer Science and Business Media Deutschland GmbH

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Ruiz-Rivera G, Chapaca-Garzón J, Montalvo-López W. Benchmark of Fuzzy Controllers + I Optimized by Bio Heuristics. En Garcia MV, Gordón-Gallegos C, Salazar-Ramírez A, Nuñez C, editores, 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

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