Efficient Replay Deep Meta-Reinforcement Learning for Active Fault-Tolerant Control of Solid Oxide Fuel Cell Systems Considering Multivariable Coordination
Jiawen Li, Tao Zhou
Abstract
A data-driven integrated active fault-tolerant control (IAFT) strategy for controlling the solid oxide fuel cell (SOFC) output voltage is proposed, which maintains satisfactory dynamic performance and eliminates constraint violations in the event of system failure. In addition, this article introduces an efficient replay deep meta-deterministic policy gradient (ER-DMDPG) for IAFTs, which combines priority experience replay and meta-learning techniques to improve the robustness and multitask cooperative learning capability of the IAFTs. The algorithm combines the controllers of the fuel reformer and direct current-direct current (dc-dc) converter into a single independent agent, which is trained by a cooperative meta-learner and a base learner to achieve multiobjective optimal active fault-tolerant control (FTC). It is experimentally demonstrated that the proposed method can maintain better dynamic performance and prevent constraint violations of fuel utilization across a wide range of working conditions.