Open Access Open Access  Restricted Access Subscription or Fee Access

Ascent Phase Trajectory Optimization of Launch Vehicle Using Theta-Particle Swarm Optimization with Different Thrust Scenarios


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irease.v9i6.10521

Abstract


Launch vehicle trajectory optimization has gained enormous significance in the recent past. Constraints handling and accuracy of launch vehicle system, are challenging factors, on account of their high degree of non-linearity. This paper brings in the application of theta-particle swarm optimization (TH-PSO), which is a recently emerged variant of particle swarm optimization (PSO), for launch vehicle trajectory optimization, which can efficiently handle the constraints and drive the system towards optimality. TH–PSO approach is implemented on a multistage liquid propellant rocket, taking angle of attack as the control parameter. Single and dual thrust cases were solved using TH-PSO technique, and a comparative study was made with classical PSO in terms of terminal error, IE consistency of solutions. Based on the statistics, it can be confirmed that in both single and dual thrust cases TH-PSO outperformed, classical PSO.
Copyright © 2016 Praise Worthy Prize - All rights reserved.

Keywords


Trajectory Optimization; PSO; THETA-PSO; Launch Vehicle; Different Thrust Scenarios

Full Text:

PDF


References


Mehta, R., Aerodynamic Design of Payload Fairing of Satellite Launch Vehicle, (2015) International Review of Aerospace Engineering (IREASE), 8 (5), pp. 167-173.
http://dx.doi.org/10.15866/irease.v8i5.8000

Remesh, N., Ramanan, R., Lalithambika, V., Fuel Optimum Lunar Soft Landing Trajectory Design Using Different Solution Schemes, (2016) International Review of Aerospace Engineering (IREASE), 9 (5), pp. 131-143.
http://dx.doi.org/10.15866/irease.v9i5.10119

Martin S. K. Leung and Anthony J. Calise., Hybrid approach to near-optimal launch vehicle guidance, ,Journal of Guidance, Control, and Dynamics, Vol. 17, No. 5 (1994), pp. 881-888.
http://dx.doi.org/10.2514/3.21285

John T. Betts., Survey of Numerical Methods for Trajectory Optimization. Journal Of Guidance, Control, And Dynamics Vol. 21, No. 2, AIAA 1998.
http://dx.doi.org/10.2514/2.4231

Bruce A. Conway., A Survey of Methods Available for the Numerical Optimization of Continuous Dynamic Systems. Journal Optimum Theory Application (2012) 152:271–306
http://dx.doi.org/10.1007/s10957-011-9918-z

Shan, Jinjun, Yuan Ren. Low-thrust trajectory design with constrained particle swarm optimization. Aerospace Science and Technology 36 (2014): 114-124.
http://dx.doi.org/10.1016/j.ast.2014.04.004

Eberhart, R. C, Shi,Y., Comparison Between Genetic Algorithms and Particle Swarm Optimization, Evolutionary Programming VII, Lecture Notes in Computer Science, Vol. 1447, Springer, New York, 1998, pp. 611–616.
http://dx.doi.org/10.1007/bfb0040812

Kennedy J, and Eberhart, R., Particle Swarm Optimization, Proceedings of the IEEE International Conference on Neural Networks, Inst. of Electrical and Electronics Engineers, Piscataway, NJ, 1995, pp. 1942–1948.
http://dx.doi.org/10.1109/icnn.1995.488968

Eberhart, R., Kennedy J., A New Optimizer Using Particle Swarm Theory, Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, 1995, pp. 39–43.
http://dx.doi.org/10.1109/mhs.1995.494215

Kalivarapu, V., Winer, E., Implementation of Digital Pheromones in Particle Swarm Optimization for Constrained Optimization Problems, 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Schaumburg, IL, AIAA Paper 2008-1974, 2008.
http://dx.doi.org/10.2514/6.2008-1974

M. Clerc, J. Kennedy, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation 6 (2002) 58–73.
http://dx.doi.org/10.1109/4235.985692

Trelea, I.C., The particle swarm optimization algorithm: convergence analysis and parameter selection. 2003 Inf. Process. Lett., 85(6):317-325.
http://dx.doi.org/10.1016/s0020-0190(02)00447-7

W. Zhong, S. Li, F. Qian, θ-PSO: A New Strategy of Particle Swarm Optimization, Zhong et al. / J Zhejiang Univ Sci A 2008 9(6):786-790.
http://dx.doi.org/10.1631/jzus.a071278

Feeley, T. S., Speyer, J. L., Techniques for Developing Approximate Optimal Advanced Launch System Guidance, Journal of Guidance, Control, and Dynamics, Vol. 17, No. 5, 1994, pp. 889-896.
http://dx.doi.org/10.2514/3.21286

Ping Lu., Nonlinear trajectory tracking guidance with application to a launch vehicle, Journal of Guidance, Control, and Dynamics, Vol. 19, No. 1 (1996), pp. 99-106.
http://dx.doi.org/10.2514/3.21585

M. Clerc, J. Kennedy, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation 6 (2002) 58–73.
http://dx.doi.org/10.1109/4235.985692

Ramya, S., Rajesh, N., Viswanathan, B., Vigneswari, B., Particle Swarm Optimization (PSO) based optimum Distributed Generation (DG) location and sizing for Voltage Stability and Loadability Enhancement in Radial Distribution System, (2014) International Review of Automatic Control (IREACO), 7 (3), pp. 288-293.

Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, in: Proceedings of the Congress Evoluationary Computer, 1998, pp. 69–73.
http://dx.doi.org/10.1109/icec.1998.699146

R. C. Eberhart and Y. Shi, Tracking and optimizing dynamic systems with particle swarms, in Proc. Congr. Evol. Comput., 2001, pp. 94–100.
http://dx.doi.org/10.1109/cec.2001.934376

F. van den Bergh, A.P. Engelbrecht, A cooperative approach to particle swarm optimization, IEEE Transactions on Evolutionary Computation 8 (3) (2004) 225–239
http://dx.doi.org/10.1109/tevc.2004.826069

J. Kennedy, Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance, in: Proceedings of IEEE Congress on Evolutionary Computation, 1999, pp. 1391–1938
http://dx.doi.org/10.1109/cec.1999.785509

J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks, 1995, pp. 1942–1948.
http://dx.doi.org/10.1109/icnn.1995.488968

Y.P. Chen, W.C. Peng, M.C. Jian, Particle swarm optimization with recombination and dynamic linkage discovery, IEEE Transactions on Systems, Man and Cybernetics – Part B: Cybernetics 37 (6) (2007) 1460–1470.
http://dx.doi.org/10.1109/tsmcb.2007.904019

J. J. Liang, A. K. Qin, P. N. Suganthan, S. Baskar, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Transactions on Evolutionary Computation 10 (2006) 281–295.
http://dx.doi.org/10.1109/tevc.2005.857610

S. Y. Ho, H. S. Lin, W. H. Liauh, S.H. Ho, OPSO: orthogonal particle swarm optimization and its application to task assignment problems, IEEE Transactionsion Systems, Man and Cybernetics – Part A: Systems and Humans 38 (2) (2008) 288–298.
http://dx.doi.org/10.1109/tsmca.2007.914796

C. Li, S. Yang, An adaptive learning particle swarm optimizer for function optimization, in: Proceedings of the Congress Evoluationary Computer, 2009, pp. 381–388.
http://dx.doi.org/10.1109/cec.2009.4982972

C. Li, S. Yang, T.T. Nguyen, A self-learning particle swarm optimizer for global optimization problems, IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics 42 (3) (2012) 627–646.
http://dx.doi.org/10.1109/tsmcb.2011.2171946

Zhong, Weimin, Jianliang Xing, Hongwei Ge, and Feng Qian. An Improved θ-PSO Algorithm with Mutation. In Symposium of 2007 International Conference on Intelligent Systems and Knowledge Engineering, pp. 1450-1455. Chengdu, 2007.
http://dx.doi.org/10.2991/iske.2007.66

H. Shayeghi, H. A. Shayanfar, A. Safari, Damping controller design for TCSC using theta-particle swarm optimization., Journal of Applied Science 11(16): 2924-2931, 2011.
http://dx.doi.org/10.3923/jas.2011.2924.2931

Amin Safari, θ - PSO Algorithm for UPFC Based Output Feedback Damping Controller., International Journal of Control and Automation Vol. 6, No. 1, February, 2013.
http://dx.doi.org/10.1109/ieeegcc.2009.5734304

Mehdi Derafshian Maram, Nima Amjady, Abbas Rezaey An Optimal Load Cut Policy with Event-Driven Design against Voltage Instability Using Theta-Particle Swarm Optimization, J. Basic. Appl. Sci. Res., 3(3)91-100, 2013.
http://dx.doi.org/10.1049/iet-gtd.2013.0780

Vahid Hosseinnezhad, Ebrahim Babaei., Economic load dispatch using θ - PSO., Electrical Power and Energy Systems 49 (2013) 160–169.
http://dx.doi.org/10.1016/j.ijepes.2013.01.002

Haiyan Lu, Weiqi Chen Dynamic-objective particle swarm optimization for constrained optimization problems., Journal of Combinatorial Optimization December 2006, Volume 12, Issue 4, pp 409-419.
http://dx.doi.org/10.1007/s10878-006-9004-x

Mengqi Hu, Teresa Wu, and Jeffery D. Weir Adaptive Particle Swarm Optimization With Multiple Adaptive Methods., IEEE Transactions On Evolutionary Computation, Vol. 17, No. 5, October 2013.
http://dx.doi.org/10.1109/tevc.2012.2232931

Ammar, Y., Boudghene Stambouli, A., Bekhti, M., Design and Optimization of Microsatellite Power System, (2015) International Review of Aerospace Engineering (IREASE), 8 (4), pp. 141-150.
http://dx.doi.org/10.15866/irease.v8i4.7334

Omar, H., Developing Geno-Fuzzy Controller for Satellite Stabilization with Gravity Gradient, (2014) International Review of Aerospace Engineering (IREASE), 7 (1), pp. 8-16.
http://dx.doi.org/10.15866/irease.v7i1.1337

Kassem, A., El-Bayoumi, G., Habib, T., Kamalaldin, K., Improving Satellite Orbit Estimation Using Commercial Cameras, (2015) International Review of Aerospace Engineering (IREASE), 8 (5), pp. 174-178.
http://dx.doi.org/10.15866/irease.v8i5.8279

Bousson, K., Gameiro, T., A Quintic Spline Approach to 4D Trajectory Generation for Unmanned Aerial Vehicles, (2015) International Review of Aerospace Engineering (IREASE), 8 (1), pp. 1-9.
http://dx.doi.org/10.15866/irease.v8i1.4780


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize