| 引用本文: | 廖瑛,尹大伟,郑宇昕,等.基于自适应Kalman滤波算法的航空发动机可测参数及其偏离量估计.[J].国防科技大学学报,2012,34(4):1-6.[点击复制] |
| LIAO Ying,YIN Dawei,ZHENG Yuxin,et al.Aeroengine measurable parameters estimation using adaptive Kalman filter algorithm[J].Journal of National University of Defense Technology,2012,34(4):1-6[点击复制] |
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| 基于自适应Kalman滤波算法的航空发动机可测参数及其偏离量估计 |
| 廖瑛1, 尹大伟2, 郑宇昕1, 门路3 |
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(1.国防科技大学 航天与材料工程学院，湖南 长沙 410073;2.海军装备研究院, 上海 200436;3.91467部队，山东 青岛 266311)
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| 摘要: |
| 针对采用估计可测参数偏离量建立航空发动机机载自适应模型的方案中，可测参数偏离量估计的问题，引入了CA（Constant Acceleration）模型，建立了简化的可测参数状态方程和测量方程，采用自适应Kalman滤波算法直接估计可测参数，由估计出的可测参数与发动机非线性模型计算的额定值之差，获得可测参数偏离量。为解决因简化的状态模型系统误差较大，采用标准Kalman滤波会出现估计严重偏离真值的问题，分析了标准Kalman滤波准则和状态模型误差对滤波结果的影响，采用动态调整状态预报在滤波估计结果中权重的策略，给出了单因子自适应Kalman滤波算法准则及递推公式，使滤波估计准确。对不同的可测参数分别采取序列滤波的方法，减少了运算量。以仿真产生的发动机测量数据为例，对系统模型和所设计的算法进行验证，计算结果表明，所设计的滤波算法具有很快的收敛速度和计算速度，结果优于标准Kalman滤波算法，具有更好的估计精度和一定的工程应用价值。 |
| 关键词: 航空发动机 机载自适应模型 自适应Kalman滤波 参数估计 自适应因子 |
| DOI： |
| 投稿日期：2011-09-28 |
| 基金项目:国家部委资助项目 |
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| Aeroengine measurable parameters estimation using adaptive Kalman filter algorithm |
| LIAO Ying1, YIN Dawei2, ZHENG Yuxin1, MEN Lu3 |
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(1.College of Aerospace and Materials Engineering, National University of Defense Technology, Changsha 410073, China;2.Naval Academy of Armament, Shanghai 200436, China;3.PLA Unit 91467, Qingdao 266311, China)
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| Abstract: |
| Aeroengine onboard adaptive model can be built by estimating the biases of measurable parameters, so the CA (constant acceleration) model was introduced to establish the measurable variables state-space equations and observation equations, and adaptive Kalman filter algorithm was employed to estimate the measurable variables directly. The bias of measurable variables can be obtained by subtracting standard model computing values from the estimated result of measurable variables. When using the standard Kalman filter algorithm to estimate the measurable parameters, the results will offset prominently because of the great system state-space model error. The principle of standard Kalman filter and the impact of model error to the filter estimate results were analyzed, and the technology of dynamically adjusting the weight of state prediction in the filter estimate results was introduced, then the single factor adaptive Kalman filter estimation principle and the recursion formula were presented, which was aimed to make the estimation more accurate. In order to reduce calculation cost, the sequence filter was applied separately to process different measured parameters. The algorithm and system model were verified using the simulated data. The calculation results show that the designed filter can converge rapidly, and the computing speed is satisfied. The estimate results are better than the standard filter’s. So the adaptive filter algorithm has better estimation precision and have some engineering value. |
| Keywords: aeroengine onboard adaptive model adaptive Kalman filter parameter estimation adaptive factor |
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