论文:2016,Vol:34,Issue(4):587-592
引用本文:
刘艺蕾, 张炜, 刘明航, 王宏伟, 左军毅. 基于期望最大化算法和求容积卡尔曼平滑器的气动参数辨识算法[J]. 西北工业大学学报
Liu Yilei, Zhang Wei, Liu Minhang, Wang Hongwei, Zun Junyi. Aircraft System Identification via Cubature Kalman Smoother with Expectation Maximization Algorithm[J]. Northwestern polytechnical university

基于期望最大化算法和求容积卡尔曼平滑器的气动参数辨识算法
刘艺蕾1,2, 张炜1,3, 刘明航1,2, 王宏伟1,3, 左军毅1,3
1. 西北工业大学 航空学院, 陕西 西安 710072;
2. 中国航空工业第一飞机设计研究院, 陕西 西安 710089;
3. 陕西省实验飞机设计与试验技术工程试验室, 陕西 西安 710072
摘要:
针对初值和噪声统计特性未知情形下的飞行器系统辨识的问题,提出了基于期望最大化(expectation maximization,EM)和求容积卡尔曼平滑器(cubature Kalman smoother,CKS)的辨识算法。该算法用期望最大化算法对初值和噪声的统计特性进行估计;用求容积卡尔曼平滑器估计状态向量和未知参数。在期望最大化算法的求期望步骤中,所求的期望值通过求容积规则获得,用较少的采样点保证了估计精度;在期望最大化算法的最大化步骤中,未知量的最优值以解析解形式给出,减小了计算量。仿真结果说明,该算法在飞行器气动参数辨识问题中,能给出较好的辨识结果。与其他方法的对比验证说明新算法具有辨识精度高、收敛速度快等优点。
关键词:    参数辨识    期望最大化    求容积规则    求容积平滑器    飞机参数辨识   
Aircraft System Identification via Cubature Kalman Smoother with Expectation Maximization Algorithm
Liu Yilei1,2, Zhang Wei1,3, Liu Minhang1,2, Wang Hongwei1,3, Zun Junyi1,3
1. School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. AVIC Aircraft Co., Ltd, Xi'an 710089, China;
3. Experimental Aircraft Design and Flight Testing Lab of Shaanxi, Xi'an 710072, China
Abstract:
This paper developed a novel system identification algorithm to estimate parameter of aircraft dynamics modeled in state space. The developed method utilizes the cubature Kalman smoother to estimate the state and unknown parameters, combined with expectation-maximization algorithm, which estimates the statistics-unknown parameters, i.e., the mean and covariance of an initial state, and the covariance of both process noise and measurement noise. To reduce the computational cost with considerable accuracy decline, the cubature Kalman smoother is employed to approximate the expectation values in the expectation maximization. Further, the analytical forms of unknown statistics parameters are given in the maximization step, which makes the nonconvex numerical optimization unnecessary. Its effectiveness is demonstrated through one problem of estimating aircraft aerodynamic parameters. The result shows that the proposed algorithm is of high accuracy as well as converge faster compared with other algorithms.
Key words:    angular velocity    cost reductior    MATLAB    parameter extraction    cubature Kalman smoother    cubature Kalman filter    cubature rule    parameter identification    expectation Maximization    aircraft system identification   
收稿日期: 2016-04-01     修回日期:
DOI:
基金项目: 国家自然科学基金(11472222、61473227)、陕西省自然科学基金(2015JM6304)、航空科学基金(20151353018)及航天技术支撑基金(2014-HT-XGD)资助
通讯作者:     Email:
作者简介: 刘艺蕾(1990-),女,中航工业第一飞机设计研究院助理工程师,主要从事适航技术研究。
相关功能
PDF(1150KB) Free
打印本文
把本文推荐给朋友
作者相关文章
刘艺蕾  在本刊中的所有文章
张炜  在本刊中的所有文章
刘明航  在本刊中的所有文章
王宏伟  在本刊中的所有文章
左军毅  在本刊中的所有文章

参考文献:
[1] Jategaonkar R V. Flight Vehicle System Identification: A Time Domain Methodology[M]. Reston, VA: AIAA, 2006
[2] Hamel P G, Jategaonkar R V. Evolution of Flight Vehicle System Identification[J]. Journal of Aircraft, 1996, 33(1): 9-28
[3] Owens B, Brandon Jay, Croom Mark, et al. Overview of Dynamic Test Techniques for Flight Dynamics Research at NASA LaRC[C]//25th AIAA Aerodynamic Measurement Technology and Ground Testing Conference, Fluid Dynamics and Co-Located Conferences, 2006
[4] Iliff K W. Parameter Estimation for Flight Vehicles[J]. Journal of Guidance, Control, and Dynamics, 1989, 12(5): 609-622
[5] Kutluay U. An Application of Equation Error Method to Aerodynamic Model Identification And Parameter Estimation of a Gliding Flight Vehicle[C]//AIAA Atmospheric Flight Mechanics Conference, Chicato, Illinois, 2009
[6] Rohlfs M. Identification of Non-Linear Derivative Models From Bo 105 Flight Test Data[J]. Aeronautical Journal, 1998,102(1011): 1-8
[7] Li C W, Zou X H. Maximum Likelihood Method Based on Interior Point Algorithm for Aircraft Parameter Identification[J]. Jounal of Aircraft, 2005, 42(5): 1355-1358
[8] Paris A C, Alaverdi O. Nonlinear Aerodynamic Model Extraction from Flight-Test Data for the S-3B Viking[J]. Journal of Aircraft, 2005, 42(1): 26-32
[9] Kalman R E. A New Approach to Linear Filtering and Prediction Theory[J]. Trans on ASME J of Basic Engineering, 1960, 82(D):35-46
[10] Jazwinski A H. Stochastic Processes and Filtering Theory[M]. New York: Academic, 1970: 235-237
[11] Julier S J, Uhlmann J K. Unscented Filtering and Nonlinear Estimation[J]. Proceedings of the IEEE, 2004, 92(3): 401-422
[12] Ito K, Xiong K. Gaussian Filters for Nonlinear Filtering Problems[J]. IEEE Trans on Automatic Control, 2000,45(5): 910-927
[13] Arasaratnam I, Haykin S. Cubature Kalman Filters[J]. IEEE Trans on Automatic Control, 2009, 54(6): 1254-1269
[14] Sarkka S. Unscented Rauch-Tung-Striebel Smoother[J]. IEEE Trans on Automatic Control, 2008, 53(3): 845-849
[15] Arasaratnam I, Haykin S. Cubature Kalman Smoothers[J]. Automatica, 2011, 47: 2245-2250
[16] Dempster A, Laird N, Rubin D. Maximum Likelihood from Incomplete Data Via The EM Algorithm[J]. Journal of the Royal Statistical Society, 1977, 39(B):1-38
[17] Lange K. A Gradient Algorithm Locally Equivalent to the EM Algorithm[J]. Journal of the Royal Statistical Society, 1995, 57(2), 425-437
[18] Bavdekar V A, Deshpande A P, Patwardhan S C. Identification of Process and Measurement Noise Covariance for State and Parameter Estimation Using Extended Kalman Filter[J]. Journal of Process Control, 2011,21(4): 585-601
[19] Schön T B, Wills A, Ninness B. System Identification of Nonlinear State-Space Models[J]. Automatica, 2011, 47(1): 39-49
[20] Yokoyama N. Parameter Estimation of Aircraft Dynamics via Unscented Smoother with Expectation-Maximization Algorithm[J]. Journal of Guidance, Control, and Dynamics, 2011, 34(2): 426-436