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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Multiple model adaptive estimation with filter spawning
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Multiple model adaptive estimation with filter spawning

机译：带滤波器生成的多模型自适应估计

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摘要

Multiple model adaptive estimation (MMAE) with filter spawning is used to detect and estimate partial actuator failures on the VISTA F-16. The truth model is a full six-degree-of-freedom simulation provided by Calspan and General Dynamics. The design models are chosen as 13-state linearized models, including first order actuator models. Actuator failures are incorporated into the truth model and design model assuming a "failure to free stream." Filter spawning is used to include additional filters with partial actuator failure hypotheses into the MMAE bank. The spawned filters are based on varying degrees of partial failures (in terms of effectiveness) associated with the complete-actuaton-failure hypothesis with the highest conditional probability of correctness at the current time. Thus, a blended estimate of the failure effectiveness is found using the filters' estimates based upon a no-failure hypothesis, a complete actuator failure hypothesis, and the spawned filters' partial-failure hypotheses. This yields substantial precision in effectiveness estimation, compared with what is possible without spawning additional filters, making partial failure adaptation a viable methodology.
机译：具有过滤器生成功能的多模型自适应估计（MMAE）用于检测和估计VISTA F-16上的部分执行器故障。真实模型是由Calspan和General Dynamics提供的完整的六自由度仿真。选择设计模型作为13状态线性化模型，包括一阶执行器模型。假设“自由流失败”，则将执行器故障合并到真值模型和设计模型中。过滤器生成用于将带有部分执行器故障假设的其他过滤器包含到MMAE库中。生成的过滤器基于与当前时间具有最高条件正确性概率的完全精算故障假设相关的部分故障程度（就有效性而言）。因此，基于无故障假说，完整的执行器故障假说和产生的过滤器的部分故障假说，使用过滤器的估计值可以找到故障有效性的混合估计值。与不产生额外过滤器的情况相比，这可以在有效性估计方面产生相当大的精度，从而使部分故障适应成为可行的方法。

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