<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 Network technology connected to control is progressively being used to replace outdated technologies, together with other contemporary technologies.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.</p>
</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 Network technology connected to control is progressively being used to replace outdated technologies, together with other contemporary technologies.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.

Jacking and Energy Consumption Control Over Network for Jack-Up Rig: Simulation and Experiment

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

Oil and gas projects differ from regular investment projects in that they are frequently large-scale, categorised as vital national projects, highly...

Jacking and Energy Consumption Control Over Network for Jack-Up Rig: Simulation and Experiment

Publié en ligne: 29 oct. 2022

Pages: 89 - 98

DOI: https://doi.org/10.2478/pomr-2022-0029

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

© 2022 Viet-Dung Do et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Jacking and Energy Consumption Control Over Network for Jack-Up Rig: Simulation and Experiment

Publié en ligne: 29 oct. 2022

Pages: 89 - 98

DOI: https://doi.org/10.2478/pomr-2022-0029

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

© 2022 Viet-Dung Do et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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