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
- Shaobao Li
- Xiaoyuan Luo (ORCID: https://orcid.org/0000-0003-0404-9533)
- Juan Wang (ORCID: https://orcid.org/0000-0002-1596-2179)
- Zhenyu Yang (ORCID: https://orcid.org/0000-0002-0773-0298)
- Xinping Guan (ORCID: https://orcid.org/0009-0006-6233-8762)
- Yuguang Zhang (ORCID: https://orcid.org/0009-0009-4917-8268)
Institutions
- Shanghai Jiao Tong University (CN)
- Yanshan University (CN)
- Aalborg University (DK)
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
- National Natural Science Foundation of China