[1]傅惠民,杨海峰.双未知输入自校准滤波方法[J].控制与信息技术(原大功率变流技术),2019,(04):1-5.[doi:10.13889/j.issn.2096-5427.2019.04.200]
 FU Huimin,YANG Haifeng.A Dual-unknown-input Self-calibration Filtering Method[J].High Power Converter Technology,2019,(04):1-5.[doi:10.13889/j.issn.2096-5427.2019.04.200]
点击复制

双未知输入自校准滤波方法()
分享到:

《控制与信息技术》(原《大功率变流技术》)[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2019年04期
页码:
1-5
栏目:
“中国飞行力学学术年会”专刊
出版日期:
2019-08-05

文章信息/Info

Title:
A Dual-unknown-input Self-calibration Filtering Method
文章编号:
2096-5427(2019)04-0001-05
作者:
傅惠民杨海峰
(北京航空航天大学 小样本技术研究中心,北京 100083)
Author(s):
FU Huimin YANG Haifeng
( Research Center of Small Sample Technology, Beihang University, Beijing 100083, China )
关键词:
滤波双未知输入系统误差自校准导航故障诊断状态方程量测方程
Keywords:
filtering dual-unknown-input system error self-calibration navigation fault diagnosis state equation measurement equation
分类号:
V448;O231
DOI:
10.13889/j.issn.2096-5427.2019.04.200
文献标志码:
A
摘要:
在通信、导航、制导与控制、故障诊断等许多工程领域中,由于环境因素的影响、模型和参数的不当选取、量测设备故障等原因,系统状态方程和量测方程中往往含有未知输入(未知系统误差),而传统的Kalman滤波方法却无法消除这两种未知输入的影响,导致产生较大的滤波误差。为此,文章提出一种双未知输入自校准滤波方法,分别对线性系统和非线性系统进行了详细讨论,并给出了相应的公式和计算步骤。该方法能够自动识别状态方程和量测方程中有无未知输入,当有未知输入时,能对其进行自动估计、补偿和修正。大量实例计算和仿真模拟结果表明,与传统方法相比,该方法能够有效提高状态估计精度,且计算简单,便于工程应用。
Abstract:
In many engineering fields, such as communication, navigation, guidance and control, fault diagnosis and so on, state equations and measurement equations often contain unknown inputs(systematic errors) due to the influence of environmental factors, improper selection of models and parameters, and failure of measuring equipment. Traditional Kalman filtering methods cannot eliminate the effect of these two unknown inputs, leading to large filtering errors. For this reason, a dual-unknown-input self-calibration filtering method was proposed. The linear and non-linear systems were discussed in detail, and the corresponding formulas and calculation steps were given. This method can automatically identify the unknown inputs in state equations and measurement equations, and automatically estimate, compensate and modify them subsequently. Large number of examples and simulations show that, as compared to traditional methods, the proposed method can effectively improve the accuracy of the state estimation. In addition, the calculation is simple, which is convenient for engineering applications.

参考文献/References:

[1] KALMAN R E. A new approach linear filtering and prediction problems[J]. Journal of Fluids Engineering, 1960, 82(1):35-45.
 [2] FUJIMOTO O, OKITA Y, OZAKI S. Nonlinearity compensation Extended Kalman filter and its application to target motion[J]. Oki Technical Review, 1997, 63(159):1-12.
[3] JULIER S J, UHLMANN J K. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE, 2004,92(3):401-423.
[4] 傅惠民,肖强,吴云章,等. 秩滤波方法[J]. 机械强度, 2014,36(4):521-526.
 [5] BLANLE M, KINNAERT M, LUNZE J, et al. Diagnosis and fault-tolerant control[M]. Berlin, Germany: Springer, 2006.
[6] CHEN J, PATTON R. Robust model-based fault diagnosis for dynamic systems[M]. Norwell, MA, US: Kluwer Academic Publishers, 1999.
 [7] GILLIJNS S, MOOR B D. Unbiased minimum variance input and state estimation for linear discrete-time systems[J]. Automatic, 2007,43(1):111-116.
 [8] HSIEH C S, CHEN F C. Optimal solution of the two-stage Kalman estimator[J]. IEEE Transactions on Automatic Control, 1995, 44(1):194-199.
[9] HSIEH C S. Robust two-stage Kalman filters for systems with unknown inputs[J]. IEEE Transactions on Automatic Control, 2000, 45(12):2374-2378.
 [10] YANG Y, GAO W G. An Optimal Adaptive Kalman Filter[J]. Journal of Geodesy, 2006, 80(4):177-183.
[11] DIXON P J, BEST M C, GORDON T J. An Extended Adaptive Kalman Filter for Real-time State Estimation of Vehicle Handling Dynamics[J]. Vehicle System Dynamics, 2000, 34(1):57-75.
[12] 傅惠民,杨海峰, 文歆磊. 自识别自校准Kalman滤波方法[J]. 深空探测学报, 2019(4): 81-85.
[13] 傅惠民, 杨海峰, 文歆磊. 量测数据自校准融合方法[J]. 航空动力学报, 2019, 34(8):1759-1763.

相似文献/References:

[1]傅惠民,杨海峰. 双未知输入自校准滤波方法[J].控制与信息技术(原大功率变流技术),2019,(04):1.[doi:10.13889/j.issn.2096-5427.2019.04.200]
 FU Huimin,YANG Haifeng. A Dual Unknow Inputs Self-calibration Filtering Method[J].High Power Converter Technology,2019,(04):1.[doi:10.13889/j.issn.2096-5427.2019.04.200]

备注/Memo

备注/Memo:
收稿日期:2019-05-30
作者简介:傅惠民(1956—),男,教授,博士生导师,“长江学者”奖励计划特聘教授,主要从事小样本信息技术、信号分析与处理、数据融合方法、可靠性及估计理论等方面的研究。
基金项目:国家重点基础研究发展计划(2012CB720000);工信部2018 年智能制造综合标准化项目《基于数字仿真的机械产品可靠性测试方法标准研究与试验验证》
更新日期/Last Update: 2019-08-20