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LI Peng, GU Hong-bin, WU Dong-su. Parameter identification method of motion platform of helmet mounted display servo system[J]. Journal of Traffic and Transportation Engineering, 2015, 15(5): 72-84. doi: 10.19818/j.cnki.1671-1637.2015.05.010
Citation: LI Peng, GU Hong-bin, WU Dong-su. Parameter identification method of motion platform of helmet mounted display servo system[J]. Journal of Traffic and Transportation Engineering, 2015, 15(5): 72-84. doi: 10.19818/j.cnki.1671-1637.2015.05.010
shu

Parameter identification method of motion platform of helmet mounted display servo system

doi: 10.19818/j.cnki.1671-1637.2015.05.010
  • 1.

    School of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, Jiangsu, China

  • 2.

    School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China

More Information
  • Author Bio:

    LI Peng(1983-), male, lecturer, PhD, +86-25-85428593, lipengaq@nuaa.edu.cn

  • Received Date: 2015-05-20
  • Publish Date: 2015-10-25
    Abstract
  • The nondeterminacies and time-varying characteristics of parameters for motion platform of helmet mounted display servo system(HMDSS)were analyzed, the identification processes of continuous-discrete extended Kalman filter(CDEKF) and continuous-discrete square-root unscented Kalman filter(CDSR-UKF)were derived, the parameter identification model of motion platform of HMDSS was presented based on the system dynamics model, and the identification effects of CDEKF and CDSR-UKF were compared by simulation.The mutation experiment of parameters for motion platform was designed and implemented to verify the practicability of CDSR-UKF.Simulation result indicates that the standard error ratios, convergence time ratios and root mean square error ratios of CDEKF to CDSR-UKF are 1.9-6.3, 1.0-27.7and 1.4-11.0, which means that CDSR-UKF has higher identification precision, stability and convergence velocity than CDEKF.The average convergence time of CDSR-UKF is about 0.002 s, so CDSRUKF has better capacity of real-time identification.The online estimation error of CDSR-UKF is less than 10%, and the convergence times against large parameter mutation and normal parameter mutation are about 0.30 sand 0.04 srespectively, so CDSR-UKF can well trace changingprocesses of identification parameters and satisfy parameter identification requirements of motion platform of HMDSS in normal usage environment.

     

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    • References(26)
  • [1]
    LI Peng, GU Hong-bin, WU Dong-su. Dimensional design and corresponding methodology for helmet mounted display with 6-DOF parallel manipulator based on requirements of head motion[J]. Acta Aeronautica et Astronautica Sinica, 2011, 32(4): 739-750. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201104019.htm
    [2]
    LI Peng, GU Hong-bin, WU Dong-su, et al. Active compliance control of helmet mounted display with parallel manipulator[J]. Acta Aeronautica et Astronautica Sinica, 2012, 33(5): 928-939. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB201205020.htm
    [3]
    WU 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.
    [4]
    DENG Kun, LI Kai-jun, XIA Qun-sheng. Application of unscented Kalman filter for the state estimation of anti-lock braking system[C]∥IEEE. IEEE International Conference on Vehicular Electronics and Safety, 2006. New York: IEEE, 2006: 130-133.
    [5]
    ARAKI N, OKADA M, KONISHI Y. Parameter identification and swing-up control of an Acrobot system[C]∥IEEE. IEEE International Conference on Industrial Technology, 2005. New York: IEEE, 2005: 1040-1045.
    [6]
    TORKAMANI 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. doi: 10.1016/j.cnsns.2012.12.017
    [7]
    SÄRKKÄ S. On unscented Kalman filtering for state estimation of continuous-time nonlinear systems[J]. IEEE Transactions on Automatic Control, 2007, 52(9): 1631-1641. doi: 10.1109/TAC.2007.904453
    [8]
    CARLSSON J, NORDHEIM C. A parameter estimation method for continuous time dynamical systems based on the unscented Kalman filter and maximum likelihood[D]. Göteborg: Chalmers University of Technology, 2011.
    [9]
    BI Jun, ZHANG Jia-wei, ZHANG Dong, et al. A correlation analysis and modeling for battery SOC and driving mileage of electric vehicle[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(1): 49-54. (in Chinese) doi: 10.3969/j.issn.1009-6744.2015.01.010
    [10]
    ZHOU Hao-tian, AI Yong, SHAN Xin, et al. Identification of fine tracking system for free space optical communications[J]. Infrared and Laser Engineering, 2015, 44(2): 736-741.
    [11]
    ZHAO Hai-sen, DU Zhong-lan, LIU Xiao-fang, et al. An on-line identification method for rotor resistance of squirrel cage induction motors based on recursive least square method and model reference adaptive system[J]. Proceedings of the CSEE, 2014, 34(30): 5386-5394. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201430017.htm
    [12]
    ZHAO Li-jun, DENG Ning-ning, GE Zhu-hong, et al. Real-time road condition estimation for four-wheel-drive vehicle[J]. Journal of Harbin Institute of Technology, 2014, 46(11): 42-46. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201411007.htm
    [13]
    LI Hong, HANG Ying, HAN Bang-cheng, et al. Analysis of friction torque model of DGCMG gimbal servo system with its identification[J]. Journal of Vibration, Measurement and Diagnosis, 2014, 34(6): 1001-1007, 1165. (in Chinese) doi: 10.3969/j.issn.1004-6801.2014.06.003
    [14]
    ZHANG Cheng-xin, GAO Feng, LI Yan. On-line compensation model based on real-time feedback thermal errors of machine tools[J]. China Mechanical Engineering, 2015, 26(3): 361-365. (in Chinese) doi: 10.3969/j.issn.1004-132X.2015.03.013
    [15]
    SONG Xiang, LI Xu, ZHANG Wei-gong, et al. Joint estimation method of key parameters for automotive active safety[J]. Journal of Traffic and Transportation Engineering, 2014, 14(1): 65-74. (in Chinese) doi: 10.3969/j.issn.1671-1637.2014.01.011
    [16]
    BAO Rui-xin, JIA Min, SABBIONI E, et al. Vehicle state and parameter estimation under driving situation based on extended Kalman particle filter method[J]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(2): 301-306. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NYJX201502044.htm
    [17]
    YU Shun-jing, CHENG Yan-qing, QIAN Wei-qi. Research on transonic aerodynamic parameter online identification[J]. Journal of Astronautics, 2011, 32(6): 1211-1216. (in Chinese) doi: 10.3873/j.issn.1000-1328.2011.06.001
    [18]
    GE Quan-bo, LI Wen-bin, SUN Ruo-yu, et al. Centralized fusion algorithm based on EKF for multisensor non-linear systems[J]. Acta Automatica Sinica, 2013, 39(6): 816-825. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201306015.htm
    [19]
    GEETHA 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. doi: 10.1016/j.protcy.2014.08.011
    [20]
    SHI Yong, HAN Chong-zhao. Adaptive UKF method with applications to target tracking[J]. Acta Automatica Sinica, 2011, 37(6): 755-759. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201106013.htm
    [21]
    JULIER S, UHLMANN J, DURRANT-WHYTE H F. A new method for the nonlinear transformation of means and covariances in filters and estimators[J]. IEEE Transactions on Automatic Control, 2000, 45(3): 477-482. doi: 10.1109/9.847726
    [22]
    ZHAO You-qun, LIN Fen. Vehicle state estimation based on unscented Kalman filter algorithm[J]. China Mechanical Engineering, 2010, 21(5): 615-619, 629. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJX201005025.htm
    [23]
    XU Xiao-lai, LEI Ying-jie, XIE Wen-biao. Self-organising intuitionistic fuzzy neural networks based on UKF[J]. Acta Electronica Sinica, 2010, 38(3): 638-645. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201003027.htm
    [24]
    DING Jian-ming, LIN Jian-hui, WANG Han, et al. Performance parameter estimation method of high-speed train based on Rao-Blackwellised particle filter[J]. Journal of Traffic and Transportation Engineering, 2014, 14(3): 52-57. (in Chinese) doi: 10.3969/j.issn.1671-1637.2014.03.011
    [25]
    YANG Jing, ZHENG Nan-ning. Integrated positioning algorithm for GPS/DR based on SR-UKF[J]. Journal of System Simulation, 2009, 21(3): 721-723, 742. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ200903026.htm
    [26]
    WANG Xiao-xu, LIANG Yan, PAN Quan, et al. Unscented Kalman filter for nonlinear systems with colored measurement noise[J]. Acta Automatica Sinica, 2012, 38(6): 986-998. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201206011.htm
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