HAN Peng, LI Mingtao, GAO Dong. A rapid TAEM trajectory planner based on iterative correction algorithm[J]. 2017, 49(10): 2017-10.
DOI:
HAN Peng, LI Mingtao, GAO Dong. A rapid TAEM trajectory planner based on iterative correction algorithm[J]. 2017, 49(10): 2017-10.DOI: 10.11918/j.issn.0367-6234.201610067.
A rapid TAEM trajectory planner based on iterative correction algorithm
the large-scale perturbation exists in initial position and azimuth angle states. A rapid trajectory planning method based on iterative correction algorithm has been proposed
to ensure that the RLV can get into the approach and landing interface successfully. According to the RLV's specific initial states
the method can select the direct or overhead heading alignment cone (HAC) mode automatically
and can generate a feasible reference profile quickly. First
the reference dynamic pressure-height profile can be generated based on the height and velocity constraints. Tracking the dynamic pressure-height profile is employed as the longitudinal guidance law
whereas tracking the ground track is used as the lateral guidance law. The longitudinal and lateral guidance laws ensure that the speed
flight path angle
azimuth angle
and lateral position of the RLV meet the terminal constraints. The ground track is composed by piecing together several flight segments that are defined by three geometric parameters. Two of the parameters
the position of HAC and its final radius
are determined by the iterative correction algorithm rapidly
until all of the required conditions for approach and landing interface are met. Simulation shows that the algorithm can select the direct or overhead heading alignment cone (HAC) mode automatically
and can generate a feasible reference profile quickly according to the RLV's specific initial states. The profile generation time are 4 to 12 seconds. The numerical methods used by the proposed algorithm are very mature and stable
and very easy to be realized in engineering. Compared with the strategy of the space shuttle
the algorithm does not rely on a number of reference trajectories that are calculated and stored offline
and the algorithm can a new reference trajectory autonomously according to the specific states. Simulation results have demonstrated the rapidity
robustness and practicability of the trajectory planning algorithm.