Multi-step ahead state estimation with hybrid algorithm for high-rate dynamic systems
Multi-step ahead state estimation with hybrid algorithm for high-rate dynamic systems
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DOI：
10.1016/j.ymssp.2022.109536
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发表时间：
2023-01
影响因子：
8.4
通讯作者：
Matthew Nelson;Vahid Barzegar;S. Laflamme;Chao Hu;A. R. Downey;Jason D. Bakos;Adam Thelen;Jacob Dodson
中科院分区：
文献类型：
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作者：
Matthew Nelson;Vahid Barzegar;S. Laflamme;Chao Hu;A. R. Downey;Jason D. Bakos;Adam Thelen;Jacob Dodson
高速率系统被定义为在小于100 ms的时间内经历通常大于100 g n的振幅的加速度的工程系统。示例包括自适应气囊展开系统、高超音速飞行器和主动爆炸缓解系统。在这些高速率应用中使用反馈机制对于确保其连续操作和安全性通常是至关重要的。本文感兴趣的是支持高速率结构健康监测（HRSHM），使亚毫秒决策系统所需的算法。HRSHM是一项复杂的任务，因为高速率系统的独特特征在于：（1）外部负载的大不确定性，（2）高水平的非平稳性和严重干扰，以及（3）系统配置变化产生的未建模动态，需要仔细制定自适应策略。本文研究了将数据驱动的预测模型与基于物理的状态观测器集成在一起的好处，以减少延迟和估计可操作信息的收敛时间。预测模型由长短期记忆（LSTM）细胞构建，执行多步提前信号预测，作为物理模型的输入，模型参考自适应系统（MRAS）。然后，MRAS执行预测信号而不是真实信号的状态估计。在一个包含快速移动边界条件的试验台上，对所提出的混合算法和基于物理的MRAS进行了比较研究。结果表明，该混合算法可以执行状态估计与零定时截止时间超调和高达50%的速度收敛时间相比，模型参考自适应系统在恒定的边界条件。然而，混合动力通常表现不佳的MRAS算法的收敛精度在运动的边界条件的收敛时间增加了20%，由于在学习预测中使用的新的动态的滞后。NSE算法的性能也在一个真正的高速率系统上进行了检查，在该系统中，它被证明能够定性地跟踪可操作的信息。
High-rate systems are defined as engineering systems that undergo accelerations of amplitudes typically greater than 100 g n over less than 100 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. The use of feedback mechanisms in these high-rate applications is often critical in ensuring their continuous operations and safety. Of interest to this paper are algorithms needed to support high-rate structural health monitoring (HRSHM) to empower sub-millisecond decision systems. HRSHM is a complex task because high-rate systems are uniquely characterized by (1) large uncertainties in the external loads,(2) high levels of non-stationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations that necessitate careful crafting of adaptive strategies. This paper studies benefits of integrating a data-driven predictive model with a physics-based state observer to reduce latency and convergence time estimating actionable information. The predictive model, constructed with long short-term memory (LSTM) cells, performs multi-step ahead signal prediction acting as the input to the physical model, a model reference adaptive system (MRAS). The MRAS then performs state estimation of the predicted signal rather than the true signal. A comparison study was done between the proposed hybrid algorithm and a physics-based MRAS on a testbed involving a fast-moving boundary condition. Results showed that the hybrid algorithm could perform state estimations with zero timing deadline overshoot and with up to 50% faster convergence time when compared to the MRAS under constant boundary conditions. However, the hybrid generally underperformed the MRAS algorithm in terms of convergence accuracy during motion of the boundary condition by increasing convergence time by 20%, attributable to the lag in learning the new dynamics used in predicting. The performance of the NSE algorithm was also examined on a true high-rate system, where it was shown to be capable of qualitatively tracking actionable information.