Fault-Tolerant H ∞ Control for Topside Separation Systems via Output-Feedback Reinforcement Learning
针对海上油水分离系统阀门故障和扰动问题，将容错H∞控制转化为二人微分博弈，用输出反馈强化学习求解，无需系统模型。
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.