Fault-Tolerant <i>H</i> <sub>∞</sub> Control for Topside Separation Systems via Output-Feedback Reinforcement Learning

The topside separation system is an important device installed on offshore oil exploration platforms for the treatment of produced water. Due to its operation in high-moisture and salt-infested environments, the system is susceptible to valve malfunctions. Additionally, the presence of strong couplings and slugging disturbances in the system further complicate the development of fault-tolerant control (FTC). To achieve this, this article investigates the fault-tolerant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ H_{\infty } $ </tex-math></inline-formula> control problem in the topside separation system. To recover control performance against actuator faults while reducing disturbance sensitivity, the fault-tolerant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ H_{\infty } $ </tex-math></inline-formula> control problem is formulated for the topside separation system and is expressed as a two-player differential game problem. A Nash equilibrium solution to the fault-tolerant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ H_{\infty } $ </tex-math></inline-formula> control problem is derived by solving the game algebraic Riccati equation (GARE). Considering the tailor-made property and difficulty in full-state sensing in industry, an output feedback reinforcement learning (RL) algorithm is proposed to implement the fault-tolerant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ H_{\infty } $ </tex-math></inline-formula> control method without the need for system dynamics. Simulation studies are performed to verify the effectiveness of the proposed algorithm.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Systems Man and Cybernetics Systems
Published
2025-01-22
DOI
https://doi.org/10.1109/tsmc.2024.3523904
Citations
4
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
4.86

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fault-Tolerant H ∞ Control for Topside Separation Systems via Output-Feedback Reinforcement Learning

Shaobao Li, Xiaoyuan Luo, Juan Wang, Zhenyu Yang et al.
4 citations
IEEE Transactions on Systems Man and Cybernetics Systems
Fault Detection and Control Systems
4.86
article

Fault-Tolerant H ∞ Control for Topside Separation Systems via Output-Feedback Reinforcement Learning

Shaobao Li, Xiaoyuan Luo, Juan Wang, Zhenyu Yang, Xinping Guan, Yuguang Zhang
article en
4 citations

Abstract

The topside separation system is an important device installed on offshore oil exploration platforms for the treatment of produced water. Due to its operation in high-moisture and salt-infested environments, the system is susceptible to valve malfunctions. Additionally, the presence of strong couplings and slugging disturbances in the system further complicate the development of fault-tolerant control (FTC). To achieve this, this article investigates the fault-tolerant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ H_{\infty } $ </tex-math></inline-formula> control problem in the topside separation system. To recover control performance against actuator faults while reducing disturbance sensitivity, the fault-tolerant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ H_{\infty } $ </tex-math></inline-formula> control problem is formulated for the topside separation system and is expressed as a two-player differential game problem. A Nash equilibrium solution to the fault-tolerant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ H_{\infty } $ </tex-math></inline-formula> control problem is derived by solving the game algebraic Riccati equation (GARE). Considering the tailor-made property and difficulty in full-state sensing in industry, an output feedback reinforcement learning (RL) algorithm is proposed to implement the fault-tolerant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ H_{\infty } $ </tex-math></inline-formula> control method without the need for system dynamics. Simulation studies are performed to verify the effectiveness of the proposed algorithm.

IEEE Transactions on Systems Man and Cybernetics SystemsVol. 55(4)
Shanghai Jiao Tong University (CN), Yanshan University (CN), Aalborg University (DK)
National Natural Science Foundation of China
Openalex Percentile: Top 6%
Fault Detection and Control Systems
4.86
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.