Gliding Trajectory Optimization Method Based on Particle Swarm Optimization and Direct Shooting Method

Article Preview

Abstract:

Glide trajectory optimization of vehicle can greatly improve the performance of missile. As is well-known, methods of trajectory optimization can be divided into direct and indirect methods. Generally, the direct method is convenient and can obtain the optimal solution with higher probability. Based on the direct method, a missile trajectory is optimized by discretizing the control quantity (angle of attack) and transforming the original optimal control problem to a nonlinear programing problem (NLP) in the present paper. The particle swarm optimization algorithm that is easy to implement and has higher convergence rate is utilized to solve the transformed NLP to generate the optimal angle of attack rule. Simulation results show that with the optimal rule, gliding distance of missile is clearly improved compared to the initial one.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

270-275

Citation:

Online since:

August 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] E.M. Yong, G.J. Tang, Lei Chen. Summary of aircraft trajectory optimization methods. J. Astr, 2008, 29(2): 398-406.

Google Scholar

[2] J.T. Beas, Survey of Numerical Methods for Trajectory Optimization. J Guid Control Dynam, Voi.21,No.2,1998,PP.193-20.

Google Scholar

[3] C. L. Darby, W. W. Hager, and A. V. Rao, An hp-adaptive pseudospectral method for solving optimal control problems, Optim Contr Appl Met, vol. 32, no. 4, pp.476-502, (2011).

DOI: 10.1002/oca.957

Google Scholar

[4] M.S. Voss, X. Feng, ARMA model selection using particle swarm optimization and AIC criteria. In Proceedings of the 15th IFAC World Congress on Automatic Control and AIC criteri [a A]. 15th TrienniaI WorId Congres[s C. BarceIona], Spain:IFAC,(2002).

DOI: 10.3182/20020721-6-es-1901.00469

Google Scholar

[5] ]F. Van den Bergh, A. P. Engelbrecht, (2000). Cooperative learning in neural networks using particle swarm optimizers. S Afr Comp J, (26), p-84.

Google Scholar

[6] J. Kennedy, R. Eberhart, Particle swarm optimization. Pr IEEE Int conf, Vol. 4. No. 2. (1995).

Google Scholar

[7] D. A. Benson, Gauss pseudospectral transcription for optimal control. MIT, (2005).

Google Scholar

[8] L.C. Darby, D. Garg, and A. V. Rao, Costate estimation using multiple-intrerval pseudospectral method, J Sp Rock, vol. 48, no. 5, pp.856-866, (2011).

DOI: 10.2514/1.a32040

Google Scholar

[9] A. V, Rao, K.A. Clarke, Performance optimization of a maneuvering re-entry vehicle using a Legendre pseudospectral method, 2002: 5-8.

DOI: 10.2514/6.2002-4885

Google Scholar