Development of Autonomous Control System using Self-Organizing Map and Autoregressive Self-Organizing Map

Aqila Dzikra Ayu, Hansel Matthew, Iman Herlambang Suherman, Aries Subiantoro, Benyamin Kusumoputro

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Autonomous control system is a controller that can make its own decision when performing control functions. An autonomous control system is needed because of the increasing complexity of dynamic system and other controller requirements. This research uses the pressure process rig® because of its dynamic and non-linear properties to develop an autonomous control system. The pressure process rig is equipment to demonstrate pressure measurement and control in the process industry. However, because of its non-linear characteristics, the system will have different responses under different operating conditions. The non-linear system cannot utilize conventional control methods such as the proportional-integral-derivative (PID). This research implements Self-Organizing Map (SOM) and Autoregressive Self-Organizing Map (ARSOM)-based control to overcome the nonlinearity problem. In addition, variations in the number of network parameters, such as the number of mapping neurons, alpha, and beta, are carried out to determine the effect of each parameter. The network performance is measured by the MSE training value, the MSE testing value, the number of epochs used, and the training duration. The results showed that the SOM and ARSOM models resulted in low MSE training and testing with fast training times. In addition, the performance of the ARSOM model is better than the SOM model with fewer mapping neurons. The MSE value for training and testing of the ARSOM model is better than the SOM model with fewer mapping neurons.

Original languageEnglish
Title of host publicationProceedings - 9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
EditorsMochammad Facta, Mohammad Syafrullah, Munawar Agus Riyadi, Imam Much Ibnu Subroto, Irawan Irawan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages407-413
Number of pages7
ISBN (Electronic)9786239213558
DOIs
Publication statusPublished - 2022
Event9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022 - Jakarta, Indonesia
Duration: 6 Oct 20227 Oct 2022

Publication series

NameInternational Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
Volume2022-October
ISSN (Print)2407-439X

Conference

Conference9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
Country/TerritoryIndonesia
CityJakarta
Period6/10/227/10/22

Keywords

  • Artificial Neural Networks
  • Autonomous Control System
  • Autoregressive Self-Organizing Map
  • Self-Organizing Map

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