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Data-driven sensor fault estimation filter design with guaranteed stability

机译：数据驱动的传感器故障估计滤波器设计，具有保证的稳定性

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摘要

We propose a systematic method to directly identify a sensor fault estimation filter from plant input/output data collected under fault-free condition. This problem is challenging, especially when skipping the step of building an explicit state-space plant model in data-driven design, because the inverse of the underlying plant dynamics is required and needs to be stable. We show that it is possible to address this problem by relying on a system-inversion-based fault estimation filter that is parameterized using identified Markov parameters. Our novel data-driven approach improves estimation performance by avoiding the propagation of model reduction errors originating from identification of the state-space plant model into the designed filter. Furthermore, it allows additional design freedom to stabilize the obtained filter under the same stabilizability condition as the existing model-based system inversion. This crucial property enables its application to sensor faults in unstable plants, where existing data-driven filter designs could not be applied so far due to the lack of such stability guarantees (even after stabilizing the closed-loop system). A numerical simulation example of sensor faults in an unstable aircraft system illustrates the effectiveness of the proposed new method.
机译：我们提出了一种系统方法，可以从在无故障条件下收集的工厂输入/输出数据直接识别传感器故障估计滤波器。这个问题是具有挑战性的，尤其是在跳过数据驱动设计中建立显式状态空间工厂模型的步骤时，因为底层工厂动态的逆是必需的并且必须稳定。我们表明，有可能通过依靠基于系统反转的故障估计滤波器来解决此问题，该滤波器使用已识别的马尔可夫参数进行参数化。我们新颖的数据驱动方法避免了因识别状态空间工厂模型而导致的模型简化误差传播到设计的滤波器中，从而提高了估算性能。此外，它还具有额外的设计自由度，可在与现有基于模型的系统反演相同的稳定性条件下稳定获得的滤波器。这一关键特性使其可用于不稳定工厂中的传感器故障，由于缺乏这样的稳定​​性保证（即使在稳定闭环系统之后），目前无法应用现有的数据驱动滤波器设计。不稳定飞机系统中传感器故障的数值模拟例子说明了所提出的新方法的有效性。

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