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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2017, Vol. 38 ›› Issue (1): 320202-320202.doi: 10.7527/S1000-6893.2016.0222

• Electronics and Electrical Engineering and Control • Previous Articles     Next Articles

Optimal evasive maneuver strategy with potential threatening area being considered

YU Dateng, WANG Hua, SUN Fuyu   

  1. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
  • Received:2016-03-08 Revised:2016-07-28 Online:2017-01-15 Published:2016-08-16
  • Supported by:

    National Natural Science Foundation of China (11572345)

Abstract:

With the execution of a series of engineering applications of orbital transfer vehicles, the threat of non-cooperative rendezvous to the target spacecraft can be more and more serious. For this problem, this paper proposes a new evasive maneuver index-potential threatening area, using the characteristic of rendezvous. Compared with traditional evasive maneuver indexes such as relative distance and collision probability, the index of potential threatening area is more adapted to the target, and will improve its evasion ability when the chaser is a noncooperative spacecraft. A multi-impulse rendezvous optimization model is built, and then the definition and computing method for the potential threaten area are proposed. The target evasive optimization model is established by using genetic algorithm, and the potential threaten area is set as the optimization target. Based on the two optimization models, a case (with 100 km being the initial distance) of numerical simulation is executed to verify the correctness of the proposed models. The numerical results show that the potential threatening area has a rigorously monotone decreasing relationship with the magnitude of the impulse. The proposed approach offers a novel index in solving orbital evasion problem and can improve the viability of the target.

Key words: optimal evasive maneuver, potential threatening area, non-cooperative target, sequential quadratic programming, genetic algorithm

CLC Number: