论文标题
耐断层的控制系统通过policy钢筋学习
Fault-Tolerant Control of Degrading Systems with On-Policy Reinforcement Learning
论文作者
论文摘要
我们提出了一种新型的自适应增强学习控制方法，用于对降解系统的耐受控制控制方法，该方法不在故障检测和诊断步骤之前。 Therefore, \textit{a priori} knowledge of faults that may occur in the system is not required.自适应方案结合了在线和离线学习，以提高探索和样本效率，同时确保稳定学习。离线学习阶段是使用系统的数据驱动模型执行的，该模型经常更新以跟踪系统的操作条件。我们在飞机燃油转移系统上进行实验，以证明我们的方法的有效性。
We propose a novel adaptive reinforcement learning control approach for fault tolerant control of degrading systems that is not preceded by a fault detection and diagnosis step. Therefore, \textit{a priori} knowledge of faults that may occur in the system is not required. The adaptive scheme combines online and offline learning of the on-policy control method to improve exploration and sample efficiency, while guaranteeing stable learning. The offline learning phase is performed using a data-driven model of the system, which is frequently updated to track the system's operating conditions. We conduct experiments on an aircraft fuel transfer system to demonstrate the effectiveness of our approach.
