飞行器气动参数辨识与组合优化
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摘要
飞行器气动参数辨识研究的主题,是应用系统辨识技术从飞行试验数据求取气动力,从而建立飞行器动力学系统的数学模型。它研究的对象是飞行器,解决的是空气动力学问题,采用的基本方程是飞行动力学的运动方程组,应用的研究手段是现代控制论中的滤波、预测和估计理论。
本文在纵向非线性动力学模型已知的情况下,用参数估计的方法,辨识飞行器运动过程中未知的气动力系数。首先,针对基于极大似然法和广义卡尔曼滤波的参数辨识方法,研究了飞行器气动参数辨识的一般特性。
然后,从辨识的精确性和效率性出发,针对计算精度高、计算效率低的极大似然一般算法采用了极大似然近似算法降低了极大似然法在辨识过程中计算效率低的问题;给出更高的辨识效率算法增广的广义卡尔曼滤波算法,把状态方程中的待辨识参数做为状态量加入状态方程中,利用广义卡尔曼滤波进行状态估计求解待辨识参数;针对增广的广义卡尔曼滤波计算效率高计算精度差的情况,提出增广的拟线性最优平滑滤波法对状态方程进行高度线性化处理,从而减少卡尔曼滤波中状态方程线性化不足所带来的误差,在精算量增加不大的前提下,辨识精度有了明显的提高。通过增广的拟线性最优平滑滤波法,解决了极大似然法和增广的广义卡尔曼滤波在辨识过程中计算精度和计算效率的矛盾。
最后,基于遗传算法的随机搜索算法,利用其全局优化特点,针对三种辨识方法建立适应度函数,对三种辨识算法所对应的个体进行评价,求解出最优权系数,组合优化从而进一步得到更精确的辨识解。
The theme of vehicle aerodynamic parameters identification applicated system identification technologies got the aerodynamics from flight test data to establish a mathematical model of dynamic systems. It is the object of research aircraft to solve the aerodynamics problems, the equation is the flight dynamics of the campaign equations, application of modern means of control theory of filtering, projections and estimates theory.
This paper has been aware from the vertical nonlinear dynamics model, the estimated parameters of the method used to identify unknown aircraft in the course of the campaign aerodynamic coefficients.
First of all, this paper based on the Maximum Likelihood (M.L) algorithm and Extended Kalman Filter(E.K.F) research the vehicle identification of the general parameters of aerodynamic characteristics.
The problem of Maximum Likelihood algorithm for calculating the efficiency of low and high accuracy to Extended Kalman Filter high-efficiency problem of low accuracy afford the Approximation Maximum Likelihood algorithm and Extended Quasi-linear Optimum Smoothing Filter to reduce the Maximum Likelihood algorithm in the identification process in terms of low efficiency and the low calculating the efficiency of EKF. Solve the conflict of Maximum Likelihood algorithm and Extended Kalman Filter in the process of identification accuracy and efficiency .
Finally, based on genetic algorithm of random search algorithm, using its global optimization characteristics of different identification method to establish fitness function, corresponding to different identification algorithm to evaluate the individual, for the optimal weights, so as to optimize the combination of more accurate Identification solution.
本文在纵向非线性动力学模型已知的情况下,用参数估计的方法,辨识飞行器运动过程中未知的气动力系数。首先,针对基于极大似然法和广义卡尔曼滤波的参数辨识方法,研究了飞行器气动参数辨识的一般特性。
然后,从辨识的精确性和效率性出发,针对计算精度高、计算效率低的极大似然一般算法采用了极大似然近似算法降低了极大似然法在辨识过程中计算效率低的问题;给出更高的辨识效率算法增广的广义卡尔曼滤波算法,把状态方程中的待辨识参数做为状态量加入状态方程中,利用广义卡尔曼滤波进行状态估计求解待辨识参数;针对增广的广义卡尔曼滤波计算效率高计算精度差的情况,提出增广的拟线性最优平滑滤波法对状态方程进行高度线性化处理,从而减少卡尔曼滤波中状态方程线性化不足所带来的误差,在精算量增加不大的前提下,辨识精度有了明显的提高。通过增广的拟线性最优平滑滤波法,解决了极大似然法和增广的广义卡尔曼滤波在辨识过程中计算精度和计算效率的矛盾。
最后,基于遗传算法的随机搜索算法,利用其全局优化特点,针对三种辨识方法建立适应度函数,对三种辨识算法所对应的个体进行评价,求解出最优权系数,组合优化从而进一步得到更精确的辨识解。
The theme of vehicle aerodynamic parameters identification applicated system identification technologies got the aerodynamics from flight test data to establish a mathematical model of dynamic systems. It is the object of research aircraft to solve the aerodynamics problems, the equation is the flight dynamics of the campaign equations, application of modern means of control theory of filtering, projections and estimates theory.
This paper has been aware from the vertical nonlinear dynamics model, the estimated parameters of the method used to identify unknown aircraft in the course of the campaign aerodynamic coefficients.
First of all, this paper based on the Maximum Likelihood (M.L) algorithm and Extended Kalman Filter(E.K.F) research the vehicle identification of the general parameters of aerodynamic characteristics.
The problem of Maximum Likelihood algorithm for calculating the efficiency of low and high accuracy to Extended Kalman Filter high-efficiency problem of low accuracy afford the Approximation Maximum Likelihood algorithm and Extended Quasi-linear Optimum Smoothing Filter to reduce the Maximum Likelihood algorithm in the identification process in terms of low efficiency and the low calculating the efficiency of EKF. Solve the conflict of Maximum Likelihood algorithm and Extended Kalman Filter in the process of identification accuracy and efficiency .
Finally, based on genetic algorithm of random search algorithm, using its global optimization characteristics of different identification method to establish fitness function, corresponding to different identification algorithm to evaluate the individual, for the optimal weights, so as to optimize the combination of more accurate Identification solution.
引文
1李乃宏.现场条件下有控飞行器气动参数辨识的理论及应用研究.哈尔滨工业大学工学博士论文, 1992:2~4
2蔡金狮.飞行器气动参数辨识进展.力学进展, vol. 17, NO4, 1987:467
3蔡金狮.动力学系统辨识与建模.国防工业出版社, 1991.
4吴瑶华,王学孝.获取飞行器气动参数的第三种途径—统辨识和参数估计, 1994.
5于彩君.飞行数据处理与气动参数辨识.西北工业大学硕士论文, 2006:4,8~9
6李言俊,张科.系统辨识理论及应用.国防工业出版社,2006年7月:92~101
7 H. Greeburg. A Survey of Method for Determining Stability Parameters of an Airplane from Dynamic Flight Measurements. NACA TN2340. 1951
8 J. H. Vincent, N. k. Gupta, and W. E. Hall. Recent Result in Parameter Identification for High Angle of Attack Stall Regimes. AIAA 79-1640
9 T. J. Galraith, D. A. Keskar. Estimation of Nonlinear Aerodynamic from Transport Certification Maneuvers-A System Identification APProach. AIAA83-206
10 K. Wiliff. Aircraft Parameter Estimation-AIAA Dryden lecture in Reseaeh for 1987. NASA YM-88281
11王树桂.系统辨识及航行器参数辨识进展.昆明理工大学学报, 1995年06期:8~13
12张金槐.动态分布参数Bayes估计中的验前建模及验前分布的适应性.飞行器测控学报. 2004年1月:30~31
13 W. E. Hall, N. K. GuPta, R. G. Smith. Identification of Aircraft Stability and Control Coefficients for the High Angle of attack regime. ADA032800, Mar, 1974
14 Hollowell William T. Pilkey Walter D., Sieveka Edwin M.System identification of dynamic structure. Finite Elements in Analysis and Design. 1988, 4(1): 65~72
15 V. Klein. Determination of Stability and Control Parameters of a Light Airplane from Flight Data Using Two Estimation Method. NASA TP-1306, Mar, 1979:
16 D. B. Mackie. A Comparisons of parameter Estimation Result from Flight Test Data Using Linear and Non-linear Maximum Likelihood Method. DFVLR-FB84-06 Dec, 1983
17全昌业.气动参数辨识飞机飞行试验研究回顾.中国空气动力学回顾与发展, 1996/4
18全昌业,简政.提取飞机气动导数的Newton-Raphson方法应用研究.飞行试验, 1978/2
19罗菜增,全昌业等.运七飞机气动摸底试飞. FS 10-17-07, 1989. 10. 20中国飞行试验研究院技术档案室
20王锟,韩华亭,李小兵.遗传算法在导弹气动参数辨识中的应用.航空兵器, 2007,No. 5,56~58
21高素军.基于实测数据的系统辨识及实验研究.哈尔滨工业大学2006年硕士论文:2~3,8~9
22蔡金狮.飞行器系统辨识.宇航出版社, 1994:3,5,87~91,152~157
23夏智勋,盛湘饶,唐国金.用建模前估计法辨识飞行器的气动参数.国防科技大学学报, 1998, 06:8~9
24 Klein Vladislav. Application of system identification to high performance aircraft. Proceedings of the IEEE Conference on Decision and Control, 1993,3:2253~2258
25 Murphy P C, Klein V. Maximum Likelihood Algorithm Using an Effient Scheme for Computing Sensitivies and Parameter Confidence Intervals. AIAA 84-2084, 1984
26 Gopalrat nam G.. Jategaonkar 1i V. Some results of ATTAS flight data analysis using estimation method. DLK—FB 91-04, Braunschweig: DRR, 1991.
27崔平远.递推计算灵敏度的极大似然估计算法实现.自动化学报, 1995年01期:82~83
28汪清.不稳定飞机气动参数辨识的一种实用方法.飞行力学, 2001年01期:50~51
29 Liang Wang. Kalman Filtering Algorithm Based On Singular Value Decomposition. IEEE Proceedings of the 31st Conference On Decision and Control, 1992, CH3229-2/92/0000-0382
30富钰.广义卡尔曼滤波的计算机实现.网络与信息, 2007/05:1~2
31 Alierza. Robust Adaptive Kalman filtering With Unknown Input. IEEE Transactions on Acoustic. Speech and Signal Processing, August, 1989, Vo1. 37, No. 8:297~302
32肖顺达,余里宁.飞机纵向模型参考自适应控制增稳系统的一种简捷设计方法.西北工业大学学报, 1990 Vo1. 8, No. 1
33 Xia Zhixun. identification of aerodynamic coefficients using maximum likelihood method, Acta Aeronautica ET Astronautic a Sinica, 1998, 05:564~565
34陈农.非线性最优解在气动参数辨识中的应用.飞行力学, 2002年01:51
35 Klein V, BaLLerson J G. Aerodynamic parameter eslirnaled from flight and wind tunnel data Journal of Aircraft, 1986, 23 (4):306- 312
36崔平远.飞行器参数辨识方法与应用研究.飞行力学, 1996, 14(3):1~8
37 Wang Xiaopeng. Aerodynamic parameter identification of flight vehicles based on adaptive genetic algorithm. Journal of Astronautics, 2003, 03:304~306
38郑正谋.最佳线性滤波,西北工业大学出版社, 1985:35,37~38
39王万良.人工智能及其应用.高等教育出版社, 2005, 3:251
40周明,孙树栋.遗传算法原理及应用.国防工业出版社, 1999
41陈根社,陈新海.遗传算法的研究与进展.信息与控制, Vol13. No. 4:216~217
42席格庚.遗传算法综述.控制理论与应用, Vol13. No. 6:697~699
43戴冠中,徐乃平.一种新的优化搜索算法-遗传算法,控制理论与应用, Vol. 12, No. 3:265~269
44刘勇.非数值算法(二)—遗传算法.科学出版社, 1995
45王晓鹏.遗传算法及其在气动优化设计中的应用研究,西北工业大学博士论文, 2000:3~5
46张文修,梁怡.遗传算法的数学基础.西安交通大学出版社, 2000, 5:13~16
2蔡金狮.飞行器气动参数辨识进展.力学进展, vol. 17, NO4, 1987:467
3蔡金狮.动力学系统辨识与建模.国防工业出版社, 1991.
4吴瑶华,王学孝.获取飞行器气动参数的第三种途径—统辨识和参数估计, 1994.
5于彩君.飞行数据处理与气动参数辨识.西北工业大学硕士论文, 2006:4,8~9
6李言俊,张科.系统辨识理论及应用.国防工业出版社,2006年7月:92~101
7 H. Greeburg. A Survey of Method for Determining Stability Parameters of an Airplane from Dynamic Flight Measurements. NACA TN2340. 1951
8 J. H. Vincent, N. k. Gupta, and W. E. Hall. Recent Result in Parameter Identification for High Angle of Attack Stall Regimes. AIAA 79-1640
9 T. J. Galraith, D. A. Keskar. Estimation of Nonlinear Aerodynamic from Transport Certification Maneuvers-A System Identification APProach. AIAA83-206
10 K. Wiliff. Aircraft Parameter Estimation-AIAA Dryden lecture in Reseaeh for 1987. NASA YM-88281
11王树桂.系统辨识及航行器参数辨识进展.昆明理工大学学报, 1995年06期:8~13
12张金槐.动态分布参数Bayes估计中的验前建模及验前分布的适应性.飞行器测控学报. 2004年1月:30~31
13 W. E. Hall, N. K. GuPta, R. G. Smith. Identification of Aircraft Stability and Control Coefficients for the High Angle of attack regime. ADA032800, Mar, 1974
14 Hollowell William T. Pilkey Walter D., Sieveka Edwin M.System identification of dynamic structure. Finite Elements in Analysis and Design. 1988, 4(1): 65~72
15 V. Klein. Determination of Stability and Control Parameters of a Light Airplane from Flight Data Using Two Estimation Method. NASA TP-1306, Mar, 1979:
16 D. B. Mackie. A Comparisons of parameter Estimation Result from Flight Test Data Using Linear and Non-linear Maximum Likelihood Method. DFVLR-FB84-06 Dec, 1983
17全昌业.气动参数辨识飞机飞行试验研究回顾.中国空气动力学回顾与发展, 1996/4
18全昌业,简政.提取飞机气动导数的Newton-Raphson方法应用研究.飞行试验, 1978/2
19罗菜增,全昌业等.运七飞机气动摸底试飞. FS 10-17-07, 1989. 10. 20中国飞行试验研究院技术档案室
20王锟,韩华亭,李小兵.遗传算法在导弹气动参数辨识中的应用.航空兵器, 2007,No. 5,56~58
21高素军.基于实测数据的系统辨识及实验研究.哈尔滨工业大学2006年硕士论文:2~3,8~9
22蔡金狮.飞行器系统辨识.宇航出版社, 1994:3,5,87~91,152~157
23夏智勋,盛湘饶,唐国金.用建模前估计法辨识飞行器的气动参数.国防科技大学学报, 1998, 06:8~9
24 Klein Vladislav. Application of system identification to high performance aircraft. Proceedings of the IEEE Conference on Decision and Control, 1993,3:2253~2258
25 Murphy P C, Klein V. Maximum Likelihood Algorithm Using an Effient Scheme for Computing Sensitivies and Parameter Confidence Intervals. AIAA 84-2084, 1984
26 Gopalrat nam G.. Jategaonkar 1i V. Some results of ATTAS flight data analysis using estimation method. DLK—FB 91-04, Braunschweig: DRR, 1991.
27崔平远.递推计算灵敏度的极大似然估计算法实现.自动化学报, 1995年01期:82~83
28汪清.不稳定飞机气动参数辨识的一种实用方法.飞行力学, 2001年01期:50~51
29 Liang Wang. Kalman Filtering Algorithm Based On Singular Value Decomposition. IEEE Proceedings of the 31st Conference On Decision and Control, 1992, CH3229-2/92/0000-0382
30富钰.广义卡尔曼滤波的计算机实现.网络与信息, 2007/05:1~2
31 Alierza. Robust Adaptive Kalman filtering With Unknown Input. IEEE Transactions on Acoustic. Speech and Signal Processing, August, 1989, Vo1. 37, No. 8:297~302
32肖顺达,余里宁.飞机纵向模型参考自适应控制增稳系统的一种简捷设计方法.西北工业大学学报, 1990 Vo1. 8, No. 1
33 Xia Zhixun. identification of aerodynamic coefficients using maximum likelihood method, Acta Aeronautica ET Astronautic a Sinica, 1998, 05:564~565
34陈农.非线性最优解在气动参数辨识中的应用.飞行力学, 2002年01:51
35 Klein V, BaLLerson J G. Aerodynamic parameter eslirnaled from flight and wind tunnel data Journal of Aircraft, 1986, 23 (4):306- 312
36崔平远.飞行器参数辨识方法与应用研究.飞行力学, 1996, 14(3):1~8
37 Wang Xiaopeng. Aerodynamic parameter identification of flight vehicles based on adaptive genetic algorithm. Journal of Astronautics, 2003, 03:304~306
38郑正谋.最佳线性滤波,西北工业大学出版社, 1985:35,37~38
39王万良.人工智能及其应用.高等教育出版社, 2005, 3:251
40周明,孙树栋.遗传算法原理及应用.国防工业出版社, 1999
41陈根社,陈新海.遗传算法的研究与进展.信息与控制, Vol13. No. 4:216~217
42席格庚.遗传算法综述.控制理论与应用, Vol13. No. 6:697~699
43戴冠中,徐乃平.一种新的优化搜索算法-遗传算法,控制理论与应用, Vol. 12, No. 3:265~269
44刘勇.非数值算法(二)—遗传算法.科学出版社, 1995
45王晓鹏.遗传算法及其在气动优化设计中的应用研究,西北工业大学博士论文, 2000:3~5
46张文修,梁怡.遗传算法的数学基础.西安交通大学出版社, 2000, 5:13~16