[1]姜永明,王长青,徐 骋.基于递推最小二乘法的飞行器模型参数在线辨识[J].控制与信息技术(原大功率变流技术),2019,(04):58-64.[doi:10.13889/j.issn.2096-5427.2019.04.010]
 JIANG Yongming,WANG Changqing,XU Cheng.Online Identification of Aircraft Model Parameters Based on Recursive Least Squares Method[J].High Power Converter Technology,2019,(04):58-64.[doi:10.13889/j.issn.2096-5427.2019.04.010]
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基于递推最小二乘法的飞行器模型参数在线辨识()
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《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:
58-64
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:
Online Identification of Aircraft Model Parameters Based on Recursive Least Squares Method
文章编号:
2096-5427(2019)04-0058-07
作者:
姜永明王长青徐 骋
(复杂系统控制与智能协同技术重点实验室,北京 100074)
Author(s):
JIANG Yongming WANG Changqing XU Cheng
( Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing 100074, China )
关键词:
飞行器模型参数在线辨识递推最小二乘法多输入多输出
Keywords:
aircraft model parameters online identification recursive least squares method multiple input and multiple output (MIMO)
分类号:
V412
DOI:
10.13889/j.issn.2096-5427.2019.04.010
文献标志码:
A
摘要:
为了实现飞行控制系统的在线更新、飞行能力在线评估以及飞行器故障的在线检测,需对飞行器动力学模型中未知参数的在线辨识方法进行研究。首先,在分析飞行器特性的基础上选取纵向小扰动线性运动方程和纵向非线性运动方程作为辨识的模型;之后采用递推最小二乘法分别对单输入多输出线性运动模型和多输入多输出非线性运动模型里面的线性子系统进行参数辨识。仿真结果表明,递推最小二乘法收敛速度快、计算效率高、辨识精度基本满足工程要求,可以用于在线辨识;但是该方法只适用于简单线性模型,无法直接用于非线性复杂运动模型。
Abstract:
In order to realize online update of the flight control system, online assessment of flight capability and online detection of an aircraft fault, the online identification method of unknown parameters in the aircraft dynamics model was studied. Firstly, based on the analysis of aircraft characteristics, the longitudinal small disturbance linear motion equation and the longitudinal nonlinear motion equation are selected as the identification model. Then, the recursive least squares method is used to identify the parameters of the Single Input and Multiple Output (SISO) linear motion model and the linear subsystem of the multiple input and multiple output (MIMO) nonlinear motion model. The simulation results show that the recursive least squares method has fast convergence speed and high computational efficiency. The identification accuracy meets the engineering requirements and can be used for online identification. However, this method is only suitable for simple linear models and cannot be directly adopted for nonlinear complex motion models.

参考文献/References:

[1] 鲁兴举.飞行器状态空间模型参数在线辨识方法[D].长沙:国防科学技术大学, 2016年.
[2] NAPOLITANO M R, SONG Y, SEANOR B. On-line parameter estimation for restructurable flight control systems[J]. Aircraft Design, 2001, 4(1): 19-50.
 [3] 史忠科. 模型在线辨识方法及其应用[J]. 控制理论与应用, 1995, 12(6):787-791.
[4] CHU Q P, MULDER J A, Van WOERKOM P T L M. Modified recursive maximum likelihood adaptive filter for nonlinear aircraft flight-path reconstruction[J]. Journal of Guidance, Control, and Dynamics, 1996, 19(6): 1285-1295.
 [5] 李新国,方群.有翼导弹飞行动力学[M].西安:西北工业大学出版社, 2016.
[6] 李言俊,张科.系统辨识理论及应用[M].北京:国防工业出版社,2016.
 [7] 钱杏芳,林瑞雄,赵亚男,等.导弹飞行力学[M].北京:北京理工大学出版社,2014.

备注/Memo

备注/Memo:
收稿日期:2019-05-14
作者简介:姜永明(1994—),男,硕士研究生,研究方向为导航制导与控制。
基金项目:国家自然科学基金(61803356);国防基础科研计划(JCKY2017204B064)
更新日期/Last Update: 2019-08-20