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自由空间光通信中精跟踪系统的辨识(英文) 被引量:3

Identification of fine tracking system for free space optical communications
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摘要 精确的精跟踪系统模型为研究精跟踪的控制算法,找到影响其动态、静态性能的关键因素提供了重要的条件。设计了精跟踪辨识系统,该系统包括:快速倾斜镜、CCD、DA以及相关的电子设备。精跟踪模型传统上被认为是一个二阶系统,通过输入输出数据、模型类和最小二乘等价准则等一系列过程确定精跟踪的模型。为了评估该辨识方法的性能,将最小二乘辨识法得到的模型与传统的频率响应法得到的模型做比较。通过验证发现,两种模型的输出与实际系统输出的模型残差平方和分别为8.20和89.52,相关系数分别为0.98和0.95。结果表明,最小二乘法得到的精跟踪模型比频率响应法得到的模型更准确地反映出实际系统的特性。 The precise model of the fine tracking system(FTS) provides a crucial condition to study the control strategy, and find the key factors which impact the dynamic and static characteristics of FTS. The fine tracking identify system is designed in this paper which includes the fast steering mirror(FSM), the charge coupled device(CCD), the Digital to Analog Converter(DAC) and the associated electronics.Traditionally, the model of the FTS is considered as a second order model, a series of process such as the input and output data, the model class and the equivalent standards of the least square method is presented to obtain the model. In order to evaluate the performance of the least square identification method, the model is compared with the one obtained by the traditional frequency response method. The residual sum of squares between these two model outputs and the actual output are 8.20 and 89.52,respectively; while the correlation coefficients are 0.98 and 0.95, respectively. The results indicate that the model of the FTS obtained by the least squares identification method is more accurate than the one obtained by the frequency response method.
出处 《红外与激光工程》 EI CSCD 北大核心 2015年第2期736-741,共6页 Infrared and Laser Engineering
基金 国家自然科学基金(201351S5002) 航空科学基金(201351S5002)
关键词 自由空间光通信 精跟踪系统 系统辨识 最小二乘辨识法 频率响应法 free space optical communications fine tracking system system identification least square identification method frequency response method
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  • 1孙冬梅,李永新.基于Volterra级数及神经网络的非线性系统建模[J].仪器仪表学报,2003,24(z2):5-7. 被引量:5
  • 2陈得宝,赵春霞.基于改进GA的WRBF神经网络设计与应用[J].南京理工大学学报,2007,31(3):370-374. 被引量:6
  • 3Chatterjee A. Parameter estimation of Duffing oscillator using Voherra series and muhi-tone excitation [ J ]. International Journal of Mechanical Sciences, 2010, 52 ( 1 ) : 1716-1722.
  • 4Du Baozhu, Lam J. Stability analysis of static recurrent neural networks using delay-partitioning and projection [ J ]. Neural Networks,2009,22( 1 ) :343-347.
  • 5Wu Wei, Wang Jian, Cheng Mingsong, et al. Convergence analysis of online gradient method for BP neural networks [J]. Neural Networks ,2011,24( 1 ) :91-98.
  • 6Peng Xiuyan, Men Zhiguo, Wang Xingmei, et al. The ship motion prediction approach based on BP neural network to identify volterra series kernels [ A ]. Proceedings of the 2011 IEEE International Conference Information Science and Engineering [ C ]. Yangzhou, China : IEEE, 2011 : 2197-2200.
  • 7Wong W K, Yuen C W M, Fan D D, et al. Stitching defect detection and classification using wavelet transform and BP neural network [ J ]. Expert Systems with Applications ,2009,36 ( 1 ) :3845-3856.
  • 8Yu Shiwei, Guo Xiufu, Zhu Kejun, et al. A neuro-fuzzy GA-BP method of seismic reservoir fuzzy rules extraction [ J ]. Expert Systems with Applications, 2010,37 ( 3 ) :2037-2042.
  • 9Xie Qiu, Liu Zixian. Efficiency evaluation for collaborative design based on GA-BP algorithm [ A ]. Proceedings of the 2008 12th International Conference on Computer Supported Cooperative Work in Design [ C ]. Xi'an, China: IEEE, 2008 : 234-240.
  • 10李辉,蔡敏,陈斌.基于自适应遗传算法优化BP神经网络[J].信息化研究,2010,36(9):36-38. 被引量:5

共引文献9

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  • 1邵兵,孙立宁,曲东升,王建国,秦昌.自由空间光通信ATP系统关键技术研究[J].压电与声光,2005,27(4):431-433. 被引量:8
  • 2WU Dong-su, GU Hong-bin, LI Peng. Comparative study on dynamic identification of parallel motion platform for a novel flight simulator[C]//IEEE. Proceedings of the 2009 IEEE International Conference on Robotics and Biomimetics. New York: IEEE, 2009: 2232-2237.
  • 3DENG Kun, LI Kai-jun, XIA Qun-sheng. Application of unscented Kalman filter for the state estimation of antiqock braking system[C]//IEEE. IEEE International Conference on Vehicular Electronics and Safety, 2006. New York: IEEE, 2006: 130-133.
  • 4ARAKI N, OKADA M, KONISHI Y. Parameter identification and swing-up control of an Aerobot system[C]//IEEE. IEEE International Conference on Industrial Technology, 2005. New Yorkz IEEE, 2005 1040-1045.
  • 5TORKAMANI S, BUTCHER E A. Optimal estimation of parameters and states in stochastic time-varying systems with time delay[J]. Communications in Nonlinear Science and Numerical Simulation, 2013, 18(8) . 2188-2201.
  • 6S)iKK.A S. On unscented Kalman filtering for state estimation of continuous-time nonlinear systems[J]. IEEE Transactions on Automatic Control, 2007, 52(9). 1631-1641.
  • 7CARLSSON J, NORDHEIM C. A parameter estimation method for continuous time dynamical systems based on the unscented Kalman filter and maximum likelihood[D], GOteborg: Chalmers University of Technology, 2011.
  • 8GEETHA M, ARUN K P, JOVITHA J. Comparative assessment of a chemical reactor using extended Kalman filter and unscented Kalman filter[J]. Procedia Technology, 2014, 14. 75-84.
  • 9JULIER S, UHLMANN J, DURRANT-WHYTE H F. A new method for the nonlinear transformation of mean.s and covariances in filters and estimators[J]. IEEE Transactions on Automatic Control, 2000, 45(3) 477-482.
  • 10杨静,郑南宁.一种基于SR-UKF的GPS/DR组合定位算法[J].系统仿真学报,2009,21(3):721-723. 被引量:7

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