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Ascent Guidance Trajectory Optimization Using Evolutionary Algorithm Considering Engine Gimbal and Aerodynamic Control


To achieve high-range capability, reusability, and aircraft-like operability in spaceplanes, the latter have been investigated actively. One of the critical technologies for spaceplanes is the flexible guidance methodology, which can accommodate various mission requirements and constraints. The application of an evolutionary algorithm to the real-time optimization of longitudinal ascent guidance trajectories is presented herein. The combination of the engine gimbal and aerodynamic control surfaces is considered to improve the design flexibility of the trajectories and mitigate the elevator hinge moment. The range of guidance commands for the engine gimbal angle and the angle of attack that satisfy the longitudinal trim condition are identified in advance and stored as tabular data. The performance of the proposed guidance algorithm is evaluated via a flight simulation of a subscale flight demonstrator.

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  1. Lu P, Pan B (2010) Highly constrained optimal launch ascent guidance. J Guid Control Dyn 33:404–414

    Article  Google Scholar 

  2. McHenry RL, Brand TJ, Long AD et al (1979) Space shuttle ascent guidance, navigation and control. J Astronaut Sci 27:1–38

    Google Scholar 

  3. Al-Garni A, Kassem HA (2007) On the optimization of aerospace plane ascent trajectory. Trans Jpn Soc Aeronaut Space Sci 50:113–120

    Article  Google Scholar 

  4. Zhou H, Wang X, Bai Y, Cui N (2017) Ascent phase trajectory optimization for vehicle with multi-combined cycle engine based on improved particle swarm optimization. Acta Astronaut 140:156–165

    Article  Google Scholar 

  5. Maddock AC, Minisci E (2016) Spaceplane trajectory optimisation with evolutionary-based initialisation. In: 2016 IEEE symposium series on computational intelligence (SSCI)

  6. Miyamoto S, Matsumoto T, Yonemoto K (2014) Dynamically distributed genetic algorithm and its application to diversely selectable trajectory optimization of winged rockets. Trans JSASS Aerosp Technol Jpn 12:37–46

    Google Scholar 

  7. Murakami M, Fujikawa T, Yonemoto K (2021) Ascent guidance trajectory optimization for winged rocket using evolutionary computation. In: 60th Annual conference of the society of instrument and control engineers of Japan (SICE), p 1026–1031

  8. Watanabe A, Yonemoto K, Fujikawa T et al (2020) Design and development status of experimental winged rocket WIRES#015. In: Asia-Pacific international symposium on aerospace technology P00210

  9. NOAA, NASA and U.S. Air Force (1976) U.S. Standard atmosphere 1976. NASA TM-X-74335

  10. Suzuki S, Yoshizawa T (1994) Multiobjective trajectory optimization by goal programming with fuzzy decisions. J Guid Control Dyn 17:297–303

    Article  Google Scholar 

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Correspondence to Masaaki Murakami.

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An earlier version of this paper was presented at APISAT 2021, Jeju, South Korea, in November 2021.

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Murakami, M., Yonemoto, K. & Fujikawa, T. Ascent Guidance Trajectory Optimization Using Evolutionary Algorithm Considering Engine Gimbal and Aerodynamic Control. Int. J. Aeronaut. Space Sci. (2022).

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  • Guidance
  • Trajectory optimization
  • Evolutionary algorithm
  • Spaceplane