论文标题
用于故障自适应控制
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control
论文作者
论文摘要
故障是所有系统特有的。与不安全的条件或灾难性事件相反，自适应断层控制会保持降解性能。在具有突然故障和严格时间限制的系统中，控制必须迅速适应系统更改以维护系统操作。我们提出了一种元强化学习方法，该方法迅速使其控制政策适应不断变化的条件。该方法建立在模型不足的元学习（MAML）的基础上。控制器维持在系统故障下学习的先前政策的补充。在新故障后，在系统上评估了此“库”，以初始化新策略。这与MAML形成鲜明对比的是，控制器从类似系统的分布中重新衍生出中间策略，以初始化新策略。我们的方法提高了增强学习过程的样本效率。我们在突然故障下在飞机燃油转移系统上评估我们的方法。
Faults are endemic to all systems. Adaptive fault-tolerant control maintains degraded performance when faults occur as opposed to unsafe conditions or catastrophic events. In systems with abrupt faults and strict time constraints, it is imperative for control to adapt quickly to system changes to maintain system operations. We present a meta-reinforcement learning approach that quickly adapts its control policy to changing conditions. The approach builds upon model-agnostic meta learning (MAML). The controller maintains a complement of prior policies learned under system faults. This "library" is evaluated on a system after a new fault to initialize the new policy. This contrasts with MAML, where the controller derives intermediate policies anew, sampled from a distribution of similar systems, to initialize a new policy. Our approach improves sample efficiency of the reinforcement learning process. We evaluate our approach on an aircraft fuel transfer system under abrupt faults.
