针对长航时可复用飞行器舵回路故障检测、诊断、预测中存在的可量测数据少和数据不全面的问题，本文将数字孪生引入至舵回路健康管理中。首先，本文提出了一种舵回路系统的数字孪生框架，通过AMESim和FLUENT对舵回路进行了机、电、控制和飞行动态负载的精细化建模，并实现了三相机电控制和飞行实时动态负载模型的耦合集成。然后，为解决舵回路数字孪生与物理实体在正常、舵面缺损和松浮故障等多种运行状态中保持虚实一致的问题，本文构建了舵回路虚实一致性感知方法及数字孪生动态调整机制，使得舵回路数字孪生通过在线故障感知与加载，具备持续跟踪实体变化并保持虚实一致性的能力。最后，实验表明，正常和故障下所建舵回路数字孪生的虚实电流和舵角的多项时域指标的幅值与趋势均能与物理实体保持虚实一致，且经孪生扩展的数据维度可为舵回路健康管理提供更全面的数据支撑。

To address the challenges of limited and incomplete measurable data in fault detection, diagnosis, and prediction for long-endurance reusable vehicle’s rudder-loop-system, this study introduces the concept of digital twins into the health management of rudder loops. First, a digital twin framework for the rudder-loop-system is proposed. Through the use of AMESim and FLUENT, precise modeling of the rudder loop is conducted, covering mechanical, electrical, control, and flight dynamic load aspects. The integration of the three-phase electromechanical control model and the real-time flight dynamic load model is also achieved. Subsequently, to ensure the digital twin of the rudder loop remains consistent with the physical entity under various operating conditions, such as normal opera-tion, rudder surface damage, and floating faults, a virtual-physical consistency perception method and a dynamic adjustment mechanism for the digital twin are developed. These enable the rudder loop digital twin to maintain virtual-physical consistency by continuously tracking physical changes through online fault perception and adaptive adjustments. Finally, experimental results demonstrate that under both normal and fault conditions, the digital twin maintains a high level of virtual-physical consistency with the physical entity, with multiple time-domain metrics such as current and rudder angle errors, variances, and peak-to-peak values aligning in both magnitude and trend. Moreover, the extended data dimensions provided by the digital twin offer more comprehensive data support for the health management of rudder loops.