Application of Improved Fish Swarm Algorithm in Optimization of Simulator Motion Washout
CSTR:
Author:
  • Article
  • | |
  • Metrics
  • |
  • Reference [21]
  • |
  • Related [20]
  • | | |
  • Comments
    Abstract:

    Flight simulators are important equipment for simulating and reproducing real flight activities, and the simulation effects of simulators have been attracting wide attention. However, the motion platform based on the classical washout algorithm for restoring motion trajectories faces problems such as conservative parameter settings and poor simulation effects. Therefore, this study proposes a filter parameter optimization method based on an improved artificial fish swarm algorithm. Specifically, by the human vestibular perception error model, the corresponding objective function is obtained; then, the improved fish swarm algorithm is used to optimize the natural cut-off frequency in the filter; finally, the optimized filter parameters are simulated and verified through the simulation model built on Simulink. The results show that compared with those of the classical washout algorithm and the basic artificial fish swarm algorithm, the new parameters obtained by the improved algorithm can effectively improve the motion perception effect during the algorithm washout, reduce the motion error, and save more motion space.

    Reference
    [1] Conrad B, Schmidt S, Douvillier J. Washout circuit design for multi-degrees of freedom moving base simulators. Proceedings of the AIAA Visual and Motion Simulation Conference. Palo Alto: AIAA, 1973.
    [2] Parrish RV, Dieudonne JE, Bowles BL, et al. Coordinated adaptive washout for motion simulators. Journal of Aircraft, 1975, 12(1): 44–50. [doi: 10.2514/3.59800
    [3] Asadi H, Mohamed S, Nahavandi S. Incorporating human perception with the motion washout filter using fuzzy logic control. IEEE/ASME Transactions on Mechatronics, 2015, 20(6): 3276–32849. [doi: 10.1109/TMECH.2015.2405934
    [4] Asadi H, Mohamed S, Nelson K, et al. Human perception-based washout filtering using genetic algorithm. Proceedings of the 22nd International Conference on Neural Information Processing. Istanbul: Springer, 2015. 401–411.
    [5] Asadi H, Mohammadi A, Mohamed S, et al. A Particle Swarm Optimization-based washout filter for improving simulator motion fidelity. Proceedings of 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Budapest: IEEE, 2016. 1963–1968.
    [6] 郭盛, 刘烨磊, 曲海波, 等. 飞行模拟器洗出算法的改进及实现. 北京交通大学学报, 2014, 38(1): 117–121
    [7] 王辉, 李儒周. 基于模糊逻辑的体感算法动感逼真度分析. 科学技术与工程, 2018, 18(10): 250–254. [doi: 10.3969/j.issn.1671-1815.2018.10.043
    [8] 王辉, 张保峰. 飞行模拟器新型倾斜协调体感算法应用分析. 重庆大学学报, 2019, 42(5): 19–26
    [9] 王辉, 张保峰. 基于模糊自适应飞行模拟器体感算法的研究. 系统仿真学报, 2019, 31(10): 2052–2057
    [10] 李炜增, 贾慈力. 六自由度飞行模拟器洗出算法参数优化研究. 智能计算机与应用, 2019, 9(5): 54–57, 62
    [11] 王辉, 柳颖涛. 体感模拟算法在运动洗出中的优化. 机械设计, 2021, 38(3): 119–125
    [12] 刘伟超,王辉.飞行模拟器洗出算法多重滤波信号补偿优化研究. 系统仿真学报: 1–8. http://kns.cnki.net/kcms/detail/11.3092.V.20211202.2048.004.html.
    [13] 李晓磊, 邵之江, 钱积新. 一种基于动物自治体的寻优模式: 鱼群算法. 系统工程理论与实践, 2002, 22(11): 32–38. [doi: 10.3321/j.issn:1000-6788.2002.11.007
    [14] 高健. 飞行模拟器动感模拟系统逼真度研究[博士学位论文]. 哈尔滨: 哈尔滨工业大学, 2013.
    [15] 李晓磊, 冯少辉, 钱积新, 等. 基于人工鱼群算法的鲁棒PID控制器参数整定方法研究. 信息与控制, 2004, 33(1): 112–115. [doi: 10.3969/j.issn.1002-0411.2004.01.025
    [16] 李晓磊, 薛云灿, 路飞, 等. 基于人工鱼群算法的参数估计方法. 山东大学学报(工学版), 2004, 34(3): 84–87. [doi: 10.3969/j.issn.1672-3961.2004.03.020
    [17] 张英杰, 李志武, 奉中华. 一种基于动态参数调整的改进人工鱼群算法. 湖南大学学报(自然科学版), 2012, 39(5): 77–82
    [18] 马宪民, 刘妮. 自适应视野的人工鱼群算法求解最短路径问题. 通信学报, 2014, 35(1): 1–6. [doi: 10.3969/j.issn.1000-436x.2014.01.001
    [19] 刘东林, 李乐乐. 一种新颖的改进人工鱼群算法. 计算机科学, 2017, 44(4): 281–287. [doi: 10.11896/j.issn.1002-137X.2017.04.058
    [20] Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: A gravitational search algorithm. Information Sciences, 2009, 179(13): 2232–2248. [doi: 10.1016/j.ins.2009.03.004
    [21] 王辉, 吕兴顺. 基于萤火虫算法的飞行模拟运动平台洗出算法优化. 机械设计, 2020, 37(3): 28–32
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

王辉,阿迪娜.改进鱼群算法在模拟机运动洗出优化中的应用.计算机系统应用,2022,31(8):265-272

Copy
Share
Article Metrics
  • Abstract:544
  • PDF: 1458
  • HTML: 1129
  • Cited by: 0
History
  • Received:November 19,2021
  • Revised:December 13,2021
  • Online: June 07,2022
Article QR Code
You are the first990374Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063