Design and Simulation of a Neural Controller for MIMO Systems

Roberto S. Velazquez-Gonzalez; Julio C. Sosa-Savedra; Agustin Barrera-Navarro

doi:10.1007/978-3-030-82064-0_3

Design and Simulation of a Neural Controller for MIMO Systems

Roberto S. Velazquez-Gonzalez, Julio C. Sosa-Savedra, Agustin Barrera-Navarro

Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada (CICATA), Unidad Querétaro

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

Abstract

Classical control theory is widely used to regulate Simple Input, Simple Output (SISO) systems. However, when there are multiple inputs and multiple outputs, the complexity to control the systems increases significantly. Intelligent control theories such as artificial neural networks have proven to be an efficient alternative to implement complex systems. In addition, it is not necessary to obtain a mathematical model of the system. This paper shows the application of artificial neural networks as control elements in MIMO systems (Multiple Input, Multiple Outputs). The performance of the controller is shown in a simulation of a combined system, in which two first-order plants are interconnected. The experiments were carried out in Matlab software.

Original language	English
Title of host publication	Recent Trends in Sustainable Engineering - Proceedings of the 2nd International Conference on Applied Science and Advanced Technology
Editors	Karen Lizbeth Flores Rodríguez, Raymundo Ramos Alvarado, Masoud Barati, Veronica Segovia Tagle, Roberto Sostrand Velázquez González
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	25-36
Number of pages	12
ISBN (Print)	9783030820633
DOIs	https://doi.org/10.1007/978-3-030-82064-0_3
State	Published - 2022
Event	2nd International Conference on Applied Science and Advanced Technology, ICASAT 2021 - Virtual, Online Duration: 2 Jun 2021 → 3 Jun 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	297
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	2nd International Conference on Applied Science and Advanced Technology, ICASAT 2021
City	Virtual, Online
Period	2/06/21 → 3/06/21

Keywords

Artificial neural network
Backpropagation algorithm
MIMO system
Neural controller

Access to Document

10.1007/978-3-030-82064-0_3

Cite this

Velazquez-Gonzalez, R. S., Sosa-Savedra, J. C., & Barrera-Navarro, A. (2022). Design and Simulation of a Neural Controller for MIMO Systems. In K. L. Flores Rodríguez, R. Ramos Alvarado, M. Barati, V. Segovia Tagle, & R. S. Velázquez González (Eds.), Recent Trends in Sustainable Engineering - Proceedings of the 2nd International Conference on Applied Science and Advanced Technology (pp. 25-36). (Lecture Notes in Networks and Systems; Vol. 297). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-82064-0_3

Velazquez-Gonzalez, Roberto S. ; Sosa-Savedra, Julio C. ; Barrera-Navarro, Agustin. / Design and Simulation of a Neural Controller for MIMO Systems. Recent Trends in Sustainable Engineering - Proceedings of the 2nd International Conference on Applied Science and Advanced Technology. editor / Karen Lizbeth Flores Rodríguez ; Raymundo Ramos Alvarado ; Masoud Barati ; Veronica Segovia Tagle ; Roberto Sostrand Velázquez González. Springer Science and Business Media Deutschland GmbH, 2022. pp. 25-36 (Lecture Notes in Networks and Systems).

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abstract = "Classical control theory is widely used to regulate Simple Input, Simple Output (SISO) systems. However, when there are multiple inputs and multiple outputs, the complexity to control the systems increases significantly. Intelligent control theories such as artificial neural networks have proven to be an efficient alternative to implement complex systems. In addition, it is not necessary to obtain a mathematical model of the system. This paper shows the application of artificial neural networks as control elements in MIMO systems (Multiple Input, Multiple Outputs). The performance of the controller is shown in a simulation of a combined system, in which two first-order plants are interconnected. The experiments were carried out in Matlab software.",

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Velazquez-Gonzalez, RS, Sosa-Savedra, JC & Barrera-Navarro, A 2022, Design and Simulation of a Neural Controller for MIMO Systems. in KL Flores Rodríguez, R Ramos Alvarado, M Barati, V Segovia Tagle & RS Velázquez González (eds), Recent Trends in Sustainable Engineering - Proceedings of the 2nd International Conference on Applied Science and Advanced Technology. Lecture Notes in Networks and Systems, vol. 297, Springer Science and Business Media Deutschland GmbH, pp. 25-36, 2nd International Conference on Applied Science and Advanced Technology, ICASAT 2021, Virtual, Online, 2/06/21. https://doi.org/10.1007/978-3-030-82064-0_3

Design and Simulation of a Neural Controller for MIMO Systems. / Velazquez-Gonzalez, Roberto S.; Sosa-Savedra, Julio C.; Barrera-Navarro, Agustin.
Recent Trends in Sustainable Engineering - Proceedings of the 2nd International Conference on Applied Science and Advanced Technology. ed. / Karen Lizbeth Flores Rodríguez; Raymundo Ramos Alvarado; Masoud Barati; Veronica Segovia Tagle; Roberto Sostrand Velázquez González. Springer Science and Business Media Deutschland GmbH, 2022. p. 25-36 (Lecture Notes in Networks and Systems; Vol. 297).

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

TY - GEN

T1 - Design and Simulation of a Neural Controller for MIMO Systems

AU - Velazquez-Gonzalez, Roberto S.

AU - Sosa-Savedra, Julio C.

AU - Barrera-Navarro, Agustin

PY - 2022

Y1 - 2022

N2 - Classical control theory is widely used to regulate Simple Input, Simple Output (SISO) systems. However, when there are multiple inputs and multiple outputs, the complexity to control the systems increases significantly. Intelligent control theories such as artificial neural networks have proven to be an efficient alternative to implement complex systems. In addition, it is not necessary to obtain a mathematical model of the system. This paper shows the application of artificial neural networks as control elements in MIMO systems (Multiple Input, Multiple Outputs). The performance of the controller is shown in a simulation of a combined system, in which two first-order plants are interconnected. The experiments were carried out in Matlab software.

AB - Classical control theory is widely used to regulate Simple Input, Simple Output (SISO) systems. However, when there are multiple inputs and multiple outputs, the complexity to control the systems increases significantly. Intelligent control theories such as artificial neural networks have proven to be an efficient alternative to implement complex systems. In addition, it is not necessary to obtain a mathematical model of the system. This paper shows the application of artificial neural networks as control elements in MIMO systems (Multiple Input, Multiple Outputs). The performance of the controller is shown in a simulation of a combined system, in which two first-order plants are interconnected. The experiments were carried out in Matlab software.

KW - Artificial neural network

KW - Backpropagation algorithm

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KW - Neural controller

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BT - Recent Trends in Sustainable Engineering - Proceedings of the 2nd International Conference on Applied Science and Advanced Technology

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PB - Springer Science and Business Media Deutschland GmbH

T2 - 2nd International Conference on Applied Science and Advanced Technology, ICASAT 2021

Y2 - 2 June 2021 through 3 June 2021

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Velazquez-Gonzalez RS, Sosa-Savedra JC, Barrera-Navarro A. Design and Simulation of a Neural Controller for MIMO Systems. In Flores Rodríguez KL, Ramos Alvarado R, Barati M, Segovia Tagle V, Velázquez González RS, editors, Recent Trends in Sustainable Engineering - Proceedings of the 2nd International Conference on Applied Science and Advanced Technology. Springer Science and Business Media Deutschland GmbH. 2022. p. 25-36. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-82064-0_3

Design and Simulation of a Neural Controller for MIMO Systems

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