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Volume 42 Issue 6
Jun.  2016
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Journal of Beijing University of Aeronautics and Astronautics  > 2016  >  42(6): 1279-1285.
WU Chengliang, CAO Yunfeng, ZHUANG Likui, et al. Path tracking for visual navigation based on H2/H∞[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(6): 1279-1285. doi: 10.13700/j.bh.1001-5965.2015.0370(in Chinese)
Citation: WU Chengliang, CAO Yunfeng, ZHUANG Likui, et al. Path tracking for visual navigation based on H2/H∞[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(6): 1279-1285. doi: 10.13700/j.bh.1001-5965.2015.0370(in Chinese)
shu

Path tracking for visual navigation based on H2/H∞

doi: 10.13700/j.bh.1001-5965.2015.0370
  • 1.

    Institute of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China

  • 2. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China

  • 3. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China

  • Received Date: 05 Jun 2015
  • Publish Date: 20 Jun 2016
    Abstract
  • Visual navigation system was physically divided into sensors (camera and laser rangefinder) and computer vision processing, so as to study the characteristics of visual navigation system theoretically. And then visual measurement model was established, which was based on the above computer vision processing. The uncertainties of visual position measurement model were divided into parameter uncertainties and input interference. The sensitivity degrees triggered by different kinds of model uncertainties in visual position estimation were analyzed in detail by simulation. Finally, based on the introduction of visual measurement model uncertainty, visual measurement model perturbation ranges, under the constraint conditions of both track control system stability and tracking performance, were given in the study of the H2/H∞ hybrid robust control application to trajectory tracking, which is of certain guiding significance to computer vision algorithm.

     

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