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Trajectory optimisation of satellite launch vehicles using network flow-based algorithm

Published online by Cambridge University Press:  02 October 2020

R. Zardashti*
Affiliation:
Assistant Professor Faculty of Aerospace Malek Ashtari University of Technology Iran
S. Rahimi*
Affiliation:
Faculty of Aerospace Malek Ashtari University of Technology Iran

Abstract

A trajectory optimisation procedure is addressed to generate a reference trajectory for Satellite Launch Vehicles (SLVs). Using a grid-based discrete scheme, a Modified Minimum Cost Network Flow (MCNF)-based algorithm over a large-scale network is proposed. By using the network grid around the Earth and the discrete dynamic equations of motion, the optimum trajectory from a launch point to the desired orbit is obtained exactly by minimisation of a cost functional subject to the nonlinear dynamics and mission constraints of the SLV. Several objectives such as the flight time and terminal conditions may be assigned to each arc in the network. Simulation results demonstrate the capability of the proposed algorithm to generate an admissible trajectory in the minimum possible time compared with previous works.

Type
Research Article
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

REFERENCES

Hromkovic, J. Algorithms for Hard Problems, 2004, Springer Verlag, Germany.CrossRefGoogle Scholar
Ghosh, A., S. Dehuri Evolutionary algorithms for multi-criterion optimization: a survey, Int. J. Comput. Inform. Sci., 2004, 2, (1).Google Scholar
Bayley, D.J., Hartfield, Jr., Burkhalter, J.E. and Jenkins, R.M. Design optimization of a space launch vehicle using a genetic algorithm, J. Spacecr. Rockets, 2008, 45, (4).CrossRefGoogle Scholar
Dileep, M.V., Kamath, S. and Nair, V.G. Optimal trajectory generation of launch vehicle using PSO algorithm, IEEE International Conference on Futuristic trend in Computational Analysis and Knowledge Management, July 2015, pp 5660.CrossRefGoogle Scholar
Henshaw, C.G. and Robert, M. Sanner Variational technique for spacecraft trajectory planning, J. Aerosp. Eng., 2010, 23, (3), pp 147156.CrossRefGoogle Scholar
Lu, P. and Pan, B. Highly constrained optimal launch ascent guidance, J Guid. Control Dyn., 2010, 33, (2), pp 404414.CrossRefGoogle Scholar
Betts, J.T. and Huffman, W.P. Path constrained trajectory optimization using sparse sequential quadratic programming, J. Guid. Control Dyn., 1993, 16, (1), pp 5968.CrossRefGoogle Scholar
Ricciardi, L.A., Vasile, M., Toso, F. and Maddockx, C.A. Multi-objective optimal control of ascent trajectories for launch vehicles AIAA Conf., 2016, DOI: 10.2514/6.2016-5669.CrossRefGoogle Scholar
Szczerba, R. New cell decomposition techniques for planning optimal paths, 1996, University of Notre Dame, Notre Dame.Google Scholar
Hart, P., Nilsson, P. and Raphael, B. A formal basis for the heuristic determination of minimum cost paths, IEEE Trans. Syst. Sci. Cybern., 1968, 4, (2), pp 100107.Google Scholar
Rippel, E., Bargill, A. and Shimkin, N. Fast graph-search algorithms for general aviation flight trajectory generation, Technion, September 2004, Israel Institute of Technology, Haifa, Israel.Google Scholar
Cormen, T., Leiserson, C., Rivest, R. and Stein, C. Introduction to Algorithms, 1990, McGraw Hill.Google Scholar
Gath, P.F. and Calise, A.J. Design and evaluation of a three-dimensional optimal ascent guidance algorithm, J. Guid. Control Dyn., 2001, 24, (2), pp 296304.CrossRefGoogle Scholar
Lu, X., Wang, Y. and Liu, L. Optimal ascent guidance for air-breathing launch vehicle based on optimal trajectory correction, J. Math. Prob. Eng., 2013, 2013, Article ID 313197, 11 pages.Google Scholar
Ricciardi, L.A. and Vasile, M. Improved archiving and search strategies for multi agent collaborative search, International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems (EUROGEN), 2015.Google Scholar
Bairstow, B.K., Weck, O. and Sobieszczanski-Sobieski, J. Trajectory optimization of satellite launch vehicle using self adaptive differential evolution algorithm, IEEE Power, Communication and Information Technology Conference (PCITC), 2015.Google Scholar
Buontempo, F. Genetic Algorithms and Machine Learning for Programmers, February 2019, Pragmatic Bookshelf.Google Scholar
Roshanian, J., Bataleblu, A.A. and Ebrahimi, M. Robust ascent trajectory design and optimization of a typical launch vehicle, IMechE C J. Mech. Eng. Sci., January 2018, 232, (24), pp 46014614.Google Scholar
Karsli, G. and Tekinalp, O. Trajectory optimization of advanced launch system, IEEE Proceedings of 2nd International Conference on Recent Advances in Space Technologies, October 2005, pp 374378.Google Scholar
Zardashti, R. and Bagherian, M. A new model for optimal TF/TA flight path design problem, Aeronaut. J., February 2016, pp. 301308.Google Scholar
Zardashti, R., Nikkhah, A.A. and Yazdanpanah, M.J. Constrained optimal terrain following/threat avoidance trajectory planning using network flow, Aeronaut. J., January 2016, pp 523539.CrossRefGoogle Scholar
Marler, R.T. and Arora, J.S. Survey of multi-objective optimization methods for engineering, J. Struct. Multidisc. Optim., January 2004, 26, pp 369395.CrossRefGoogle Scholar
Zardashti, R. Optimal and constrained terrain following/threat avoidance guidance using nonlinear approach, PhD Thesis, 2015, K.N. Toosi University of Technology, Faculty of Aerospace Engineering, Tehran, Iran.Google Scholar
Krenzke, T. Ant colony optimization for agile motion planning, MSc Thesis at the Massachusetts Institute of Technology, 2006.Google Scholar
Miele, A. Flight Mechanics, Vol. I, Theory of Flight Paths, Addison-Wesley, Reading MA, 1962.Google Scholar
Ahuja, R.K., Magnanti, T.L. and Orlin, J.B. Network flows, Theory, Algorithms and Applications, Prentice Hall, Englewood Cliffs, 1993.Google Scholar