The Variable Flow Ducted Rocket (VFDR) serves as the preferred propulsion system for hypersonic aircraft. However, it is prone to health degradation issues due to component failures, aging, and changes in operational conditions, which significantly challenge flight safety. This study introduces a data-driven integrated control approach for modeling, diagnosing, and remedying performance deterioration. Initially, a method for open-set modeling of performance degradation is developed using the Linear Parameter-Varying (LPV) modeling technique and gap metric theory, tackling the hurdles of data collection, overlapping fault classes, and modeling of unidentified faults in the VFDR system; thus, providing vital data support for detecting performance degradation. Moreover, employing the LPV model for performance degradation, a hybrid deep neural network combining multi-scale CNN and LSTM is trained to diagnose system performance issues online using multi-dimensional time-domain sequential data. Subsequently, a smooth transition control method utilizing sliding mode compensation is applied to regain control of system performance based on diagnostic outcomes and a pre-established VFDR performance recuperation controller. Simulations of three types of performance deterioration scenarios caused by changes in actuators, sensors, and the system's operational environment are conducted to demonstrate the efficiency of the proposed approach. The simulation outcomes affirm that the proposed approach is capable of effectively modeling system performance deterioration, diagnosing faults, and controlling recovery, offering promising technical support for the secure operation of intricate closed-loop control systems with stringent precision and reliability requirements.

中文翻译：

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数据驱动的VFDR运行性能退化检测与恢复控制集成研究

变流管道火箭（VFDR）是高超音速飞机的首选推进系统。然而，由于部件故障、老化和运行条件变化，它很容易出现健康退化问题，这对飞行安全提出了重大挑战。本研究介绍了一种数据驱动的集成控制方法，用于建模、诊断和补救性能恶化。最初，使用线性参数变化 (LPV) 建模技术和间隙度量理论开发了一种性能退化的开集建模方法，解决了 VFDR 系统中数据收集、重叠故障类别和未识别故障建模的障碍;从而为检测性能下降提供重要的数据支持。此外，采用 LPV 模型进行性能退化，训练结合多尺度 CNN 和 LSTM 的混合深度神经网络，使用多维时域序列数据在线诊断系统性能问题。随后，应用利用滑模补偿的平滑过渡控制方法，基于诊断结果和预先建立的 VFDR 性能恢复控制器来重新获得对系统性能的控制。对由执行器、传感器和系统运行环境的变化引起的三种类型的性能恶化场景进行了仿真，以证明所提出方法的效率。仿真结果证实，该方法能够有效地对系统性能恶化进行建模、诊断故障和控制恢复，为具有严格精度和可靠性要求的复杂闭环控制系统的安全运行提供有前景的技术支持。