基于马尔可夫参数的故障估计滤波器设计与稳定性保证
Fault Estimation Filter Design with Guaranteed Stability Using Markov Parameters
AI总结：
本文提出一种基于马尔可夫参数的设计方法，用于直接设计状态空间故障估计滤波器，无需显式构建系统模型，实现滤波器增益设计以保证稳定性和子最优H2性能。
摘要：
对于加性执行器和传感器故障，我们提出一种系统方法，直接从无故障数据中识别出的马尔可夫参数设计状态空间故障估计滤波器。我们通过参数化基于系统反演的故障估计滤波器，利用识别出的马尔可夫参数来解决这个问题。即使不构建显式状态空间植物模型，我们的新方法仍允许设计滤波器增益以实现稳定性和子最优H2性能。这种设计自由度目前其他数据驱动的故障估计滤波器设计无法实现。所提设计的另一个优点是确定状态阶数的便利性：滤波器的状态阶数越高，估计性能越好，但计算负担越重。相比之下，当使用识别出的状态空间植物模型进行滤波器设计时，确定阶数变得复杂，因为模型不匹配会复杂地传播到故障估计误差中。使用不稳定飞机系统的仿真展示了所提新方法的有效性。
英文摘要：
For additive actuator and sensor faults, we propose a systematic method to design a state-space fault estimation filter directly from Markov parameters identified from fault-free data. We address this problem by parameterizing a system-inversion-based fault estimation filter with the identified Markov parameters. Even without building an explicit state-space plant model, our novel approach still allows the filter gain design for stabilization and suboptimal $\mathcal{H}_2$ performance. This design freedom cannot be achieved by other existing data-driven fault estimation filter designs so far. Another benefit of our proposed design is the convenience of determining the state order: a higher state order of the filter leads to better estimation performance, at the cost of heavier computational burden. In contrast, order determination is cumbersome when using an identified state-space plant model for the filter design, because of the complicated propagation of the model mismatch into the fault estimation errors. Simulations using an unstable aircraft system illustrate the effectiveness of the proposed new method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
