Off-policy reinforcement-learning-based fault-tolerant H∞ control for topside separation systems with time-varying uncertainties

Abstract

The topside separation system plays a pivotal role in the treatment of produced water within offshore oil and gas production operations. Due to high-humidity and salt-infested marine environments, topside separation systems are susceptible to dynamic model variations and valve faults. In this work, fault-tolerant control (FTC) of topside separation systems subject to structural uncertainties and slugging disturbances is studied. The system is configured as a cascade structure, comprising a water level control subsystem and a pressure-drop-ratio (PDR) control subsystem. A fault-tolerant $H_{\infty }$ control framework is developed to cope with actuator faults and slugging disturbances. To enhance control performance in the presence of actuator faults and model uncertainties while reducing sensitivity to slugging disturbances, the fault-tolerant $H_{\infty }$ control problem for the topside separation system is established as the two-player differential game problem. In addition, a Nash equilibrium solution for the fault-tolerant $H_{\infty }$ control problem is achieved through the solution of the game algebraic Riccati equation (GARE). A model-free approach is presented to implement the proposed fault-tolerant $H_{\infty }$ control method using off-policy reinforcement learning (RL). Simulation studies demonstrate the effectiveness of the solution.