Reentry Trajectory Optimization Based on Time-Domain Improved Particle Swarm Optimization

Bo Yang; Hai Xiao Wang

doi:10.4028/www.scientific.net/AMM.734.482

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 734Reentry Trajectory Optimization Based on...

Reentry Trajectory Optimization Based on Time-Domain Improved Particle Swarm Optimization

Abstract:

A new time-domain improved PSO algorithm is proposed to solve the problem of reentry trajectory optimization. The approach uses time-domain basis functions fitting the control variables, solves free final time optimal control directly, and sets parameters by using vehicle's dynamic characteristics. Simulation of a reentry vehicle with no-fly zone constraints is used to demonstrate the effectiveness and veracity of algorithm in reentry trajectory optimization. The final condition error is less than 1%.

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Periodical:

Applied Mechanics and Materials (Volume 734)

Pages:

482-486

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.734.482

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Online since:

February 2015

Authors:

Bo Yang, Hai Xiao Wang*

Keywords:

Hypersonic Vehicle, Particle Swarm, Reentry, Trajectory Optimization

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* - Corresponding Author

References

[1] Pontani, M., and Conway, B. A.: Particle Swarm Optimization Applied to Space Trajectories, Journal of Guidance, Control, and Dynamics, Vol. 33, No. 5, (2010).

DOI: 10.2514/1.48475

Google Scholar

[2] Zhang Ding-ni, Liu Yi: Reentry trajectory optimization based on improved genetic algorithm and sequential quadratic programming. Journal of Zhejiang University (Engineering Science), 2014, 48(1): 161-167.

Google Scholar

[3] Eberhart, R., and Kennedy, J: A New Optimizer Using Particle Swarm Theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science 1995, MHS '95, 1995, p.39–43.

DOI: 10.1109/mhs.1995.494215

Google Scholar

[4] Jiao, B., Lian, Z., and Gu, X.: A Dynamic Inertia Weight Particle Swarm Optimization Algorithm, Chaos, Solitons and Fractals, Vol. 37, No. 3, 2008, p.698–705. doi: 10. 1016/j. chaos. 2006. 09. 063.

DOI: 10.1016/j.chaos.2006.09.063

Google Scholar

[5] Premalatha, K., and Natarajan, A. M.: Hybrid PSO and GA for Global Maximization, International Journal of Open Problems in Computer Science and Mathematics, Vol. 2, No. 4, 2009, p.597–608.

Google Scholar

[6] ZHOU Wenya, YANG Di, LI Shunli: Solution of reentry trajectory with maximum cross range by using Gauss pseudospectral method[J]. Systems Engineer ing and Electronic. 2010, 32(5): 1038–10 (in Chinese).

Google Scholar

[7] Reza Kamyar, Ehsan Taheri,: Aircraft Optimal Terrain/Threat-Based Trajectory Planning and Control. Journal of Guidance, Control, and Dynamics. 2014, 37(2): 466-483.

DOI: 10.2514/1.61339

Google Scholar

[8] J. Karimi, Seid H. Pourtakdoust: Optimal maneuver-based motion planning over terrain and threats using a dynamic hybrid PSO algorithm[J]. Aerospace Science and Technology 26 (2013) 60–71.

DOI: 10.1016/j.ast.2012.02.014

Google Scholar