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JACKING AND ENERGY CONSUMPTION CONTROL OVER NETWORK FOR JACK-UP RIG: SIMULATION AND EXPERIMENT

Abstract

Oil and gas projects differ from regular investment projects in that they are frequently large-scale, categorised as vital national projects, highly technological, and associated with significant risks. Drilling rigs are a crucial component of the oil and gas sector and the majority of the systems and equipment aboard drilling rigs are operated automatically. Consequently, it is crucial to address the topic of an advanced control theory for off-shore systems. Network technology connected to control is progressively being used to replace outdated technologies, together with other contemporary technologies. In this study, we examine how to adapt a networked control jacking system to the effects of internal and external disturbances with a time delay, using a Fuzzy controller (FC)-based particle swarm optimisation. To demonstrate the benefit of the proposed approach, the developed Fuzzy Particle Swarm Optimisation (FPSO) controller is compared with the fuzzy controller. Finally, the results from simulations and experiments utilising Matlab software and embedded systems demonstrate the suitability of the proposed approach.

Keywords:

Networked control system, Environmental forces, Energy consumption, Fuzzy Particle swarm optimization, Jacking system, Time-delay

Details

Issue
Vol. 29 No. 3 (2022)
Section
Latest Articles
Published
25-11-2022
DOI:
https://doi.org/10.2478/pomr-2022-0029
Licencja:
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Open Access License

This journal provides immediate open access to its content under the Creative Commons BY 4.0 license. Authors who publish with this journal retain all copyrights and agree to the terms of the CC BY 4.0 license.

 

Authors

  • Viet-Dung Do

    Artificial Intelligent Transportation Research Group, Ho Chi Minh City University of Transport, Vietnam, Dong An Polytechnic, Vietnam
  • Xuan-Kien Dang

    Artificial Intelligent Transportation Research Group, Ho Chi Minh City University of Transport, Vietnam
  • Tien-Dat Tran

    Artificial Intelligent Transportation Research Group, Ho Chi Minh City University of Transport, Vietnam
  • Thi Duyen-Anh Pham

    Artificial Intelligent Transportation Research Group, Ho Chi Minh City University of Transport, Vietnam

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