Volume 45 Issue 2
Feb.  2019
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CHEN Erkang, JING Wuxing, GAO Changshenget al. State/parameter moving horizon estimation for elastic hypersonic vehicles[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 291-298. doi: 10.13700/j.bh.1001-5965.2018.0273(in Chinese)
Citation: CHEN Erkang, JING Wuxing, GAO Changshenget al. State/parameter moving horizon estimation for elastic hypersonic vehicles[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 291-298. doi: 10.13700/j.bh.1001-5965.2018.0273(in Chinese)

State/parameter moving horizon estimation for elastic hypersonic vehicles

doi: 10.13700/j.bh.1001-5965.2018.0273
Funds:

National Natural Science Foundation of China 11572097

More Information
  • Corresponding author: JING Wuxing, E-mail: jingwuxing@hit.edu.cn
  • Received Date: 15 May 2018
  • Accepted Date: 28 Jul 2018
  • Publish Date: 20 Feb 2019
  • Considering the nonlinearity, uncertainty and rigid/elastic coupling of elastic hypersonic vehicles, a state/parameter joint estimation method based on QR decomposition and moving horizon estimation is proposed. First, this method transforms the state/parameter estimation problem into an optimization problem with fixed-number variables by introducing moving horizon strategy, and it can deal with the time-varying parameter estimation better than Kalman filter. Second, by utilizing the forward dynamic programming principle, the computation of arrival-cost is converted into a least-square problem that is solved by QR decomposition, and the arrival-cost update algorithm based on QR decomposition is given. In this way, the moving horizon estimation is based on optimization, and the feedback mechanism is introduced to improve the estimation accuracy and speed. The simulation results demonstrate that the accuracy of moving horizon estimation is obviously higher than that of extended Kalman filter, and the arrival-cost update strategy based on QR decomposition is better than the traditional arrival-cost update method based on the estimated error covariance in speed.

     

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