Volume 47 Issue 2
Feb.  2021
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ZHOU Rui, ZHANG Yuhang, XIONG Wei, et al. A reentry steady glide guidance algorithm based on fuzzy control[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(2): 197-206. doi: 10.13700/j.bh.1001-5965.2020.0264(in Chinese)
Citation: ZHOU Rui, ZHANG Yuhang, XIONG Wei, et al. A reentry steady glide guidance algorithm based on fuzzy control[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(2): 197-206. doi: 10.13700/j.bh.1001-5965.2020.0264(in Chinese)

A reentry steady glide guidance algorithm based on fuzzy control

doi: 10.13700/j.bh.1001-5965.2020.0264
Funds:

National Natural Science Foundation of China 61773031

CETC Key Laboratory of Aerospace Information Applications Foundation 

More Information
  • Corresponding author: ZHANG Yuhang. E-mail: zhyhbuaa@163.com
  • Received Date: 15 Jun 2020
  • Accepted Date: 17 Jul 2020
  • Publish Date: 20 Feb 2021
  • Considering the periodic oscillation in reentry glide of Lifting Hypersonic Vehicle (LHV), a feedback correction method based on fuzzy logic and fuzzy control is proposed to reduce the oscillation and keep reentry glide trajectory smooth. First, the longitudinal guidance is developed based on the prediction of the landing error and the correction of the guidance command, and a fuzzy controller whose input consists of altitude ratio and airspeed is applied to outer loop of the bank angle control system. Then, the lateral guidance is designed by the course angle error corridor and bank reversal logic, which realizes the lateral controls in large transverse range conditions. This method is independent of Quasi-Equilibrium Glide Condition (QEGC) and the problem of parameters design in parametric feedback law is avoided, which enhances the adaptive ability. Based on LHV model, the numerical simulations show that periodic oscillation is effectively reduced by the fuzzy feedback control law within terminal and reentry corridor constraints. Meanwhile, the Monte Carlo simulation with random dispersions and errors verifies the robustness of the proposed algorithm.

     

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