Chicken Swarm–Based Method for Ascent Trajectory Optimization of Hypersonic Vehicles
Publication: Journal of Aerospace Engineering
Volume 30, Issue 5
Abstract
Trajectory optimization of hypersonic vehicles has become a hotspot because of its advantages in flight speed and range. This task is challenging because of the strong nonlinear couplings among the aerodynamics, propulsion, and flight state of hypersonic vehicles. In this paper, a novel chicken swarm–based method is proposed to solve the ascent trajectory optimization problem. Firstly, the ascent trajectory optimization problem is formulated into the optimal control problem, and the cost function that ensures the minimum fuel consumption, which is subjected to various constraints such as dynamic pressure, load factor, and aerodynamic heating, is proposed. Then the principles of the chicken-swarm optimization (CSO) algorithm are explained, and the CSO-based trajectory optimization method is proposed. With the method, the control variables are discretized at a set of Chebyshev collocation points, and these points are the variables to be optimized in the CSO algorithm. Also, a segmented strategy is introduced to treat the constraints discriminately in the penalty terms of the cost function according to the flight state of the hypersonic vehicle. Based on such a strategy, the process of trajectory optimization with the CSO algorithm is depicted. A series of comparative experiments were conducted to investigate the feasibility and superiority of the proposed method. Furthermore, more experimental results are presented to discuss the influence of hierarchical order and collocation point selection on the performance of the CSO-based method.
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Acknowledgments
This research work is financially supported by the National Natural Science Foundation of China (NSFC) with the project reference number of 11502008.
References
Abdullah, A. B., Sapuan, S. M., and Samad, Z. (2014). “Profile measurement based on focus variation method for geometrical defect evaluation: A case study of cold forged propeller blade.” Adv. Mech. Eng., 1–9.
Bertin, J. J., and Cummings, R. M. (2003). “Fifty years of hypersonics: Where we’ve been, where we’re going.” Prog. Aerosp. Sci., 39(6), 511–536.
Bortoff, S. A. (2000). “Path planning for UAVs.” Proc., American Control Conf., IEEE, New York, 364–368.
Chen, Y. L., He, P. L., and Zhang, Y. H. (2015). “Combining penalty function with modified chicken swarm optimization for constrained optimization.” Int. Conf. on Information Sciences, Machinery, Materials and Energy, 1899–1907.
Duan, H., and Li, S. (2015). “Artificial bee colony-based direct collocation for reentry trajectory optimization of hypersonic vehicle.” IEEE Trans. Aerosp. Electron. Syst., 51(1), 615–626.
Fahroo, F., and Ross, I. M. (2002). “Direct trajectory optimization by a Chebyshev pseudospectral method.” J. Guid. Control Dyn., 25(1), 160–166.
Gao, H., Cai, Y., Chen, Z., and Yu, Z. (2015). “Offset-free output feedback robust model predictive control for a generic hypersonic vehicle.” J. Aerosp. Eng., 04014147.
Hu, X., Hu, C., Wu, L., and Gao, H. (2015). “Output tracking control for nonminimum phase flexible air-breathing hypersonic vehicle models.” J. Aerosp. Eng., 04014063.
Huntington, G. T., and Rao, A. V. (2008). “Optimal reconfiguration of spacecraft formations using the Gauss pseudospectral method.” J. Guid. Control Dyn., 31(3), 689–698.
Jorris, T. R., and Cobb, R. G. (2009). “Three-dimensional trajectory optimization satisfying waypoint and no-fly zone constraints.” J. Guid. Control Dyn., 32(2), 551–572.
Li, J., and Duan, H. (2015). “Simplified brain storm optimization approach to control parameter optimization in F/A-18 automatic carrier landing system.” Aerosp. Sci. Technol., 42, 187–195.
Li, J., Shen, Z., Ren, Z., and Song, X. (2010). “Trajectory optimization and reentry tracking research for lifting reentry vehicle.” Earth and Space 2010: Engineering, Science, Construction, and Operations in Challenging Environments, ASCE, Reston, VA, 1957–1964.
Li, Y., Qu, X., and Tan, W. (2014). “A phase compensator to prevent pilot induced oscillations by actuator rate limiting.” Int. Conf. on Systems and Informatics, IEEE, Piscataway, NJ, 57–62.
Meng, X., Liu, Y., Gao, X., and Zhang, H. (2014). “A new bio-inspired algorithm: Chicken swarm optimization.” Advances in swarm intelligence, Springer International Publishing, New York City, 86–94.
Morimoto, H. (1997). “Trajectory optimization for a hypersonic vehicle with constraint.” Georgia Institute of Technology, Atlanta.
Murillo, O. J., and Lu, P. (2010). “Fast ascent trajectory optimization for hypersonic air-breathing vehicles.” Proc., AIAA Guidance, Navigation, and Control Conf., American Institute of Aeronautics and Astronautics, Reston, VA, 8173–8196.
Pontryagin, L. S. (1987). Mathematical theory of optimal processes, CRC Press, FL.
Prasanna, H. M., Ghose, D., Bhat, M. S., Bhattacharyya, C., and Umakant, J. (2005). “Interpolation-aware trajectory optimization for a hypersonic vehicle using nonlinear programming.” AIAA Guidance, Navigation, and Control Conf. and Exhibit, American Institute of Aeronautics and Astronautics, Reston, VA, 6063–6074.
Qu, Y., Jiang, D., Gao, G., and Huo, Y. (2016). “Pipe routing approach for aircraft engines based on ant colony optimization.” J. Aerosp. Eng., 04015057.
Rahimi, A., Kumar, K. D., and Alighanbari, H. (2013). “Particle swarm optimization applied to spacecraft reentry trajectory.” J. Guid. Control Dyn., 36(1), 307–310.
Shi, Y., and Eberhart, R. C. (1998). “Parameter selection in particle swarm optimization.” Int. Conf. on Evolutionary Programming, Springer, Berlin, 591–600.
Smith, C. L., and Zielinski, S. L. (2014). “The startling intelligence of the common chicken.” Scientific American, 310(2), 60–65.
Sun, H., Yang, Z., and Zeng, J. (2013). “New tracking-control strategy for airbreathing hypersonic vehicles.” J. Guid. Control Dyn., 36(3), 846–859.
Trelea, I. C. (2003). “The particle swarm optimization algorithm: Convergence analysis and parameter selection.” Inf. Process. Lett., 85(6), 317–325.
Wang, H. (2015). “Highly efficient selective assembly method of horizontal stabilizer based on metamodeling and particle swarm optimization.” J. Aerosp. Eng., 04014095.
White, D., Bowers, A., Iliff, K., Noffz, G., Gonda, M., and Menousek, J. (1992). “Flight, propulsion, and thermal control of advanced aircraft and hypersonic vehicles.” Handbook of intelligent control: Neural, fuzzy, and adaptive approaches, Multiscience Press, New York, 357–465.
Wu, D., Kong, F., Gao, W., and Shen, Y. (2015). “Improved chicken swarm optimization.” Int. Conf. on Cyber Technology in Automation, Control, and Intelligent Systems, IEEE, New York, 681–685.
Xie, Y., Liu, L., Tang, G., and Zheng, W. (2013). “Highly constrained entry trajectory generation.” Acta Astronaut., 88, 44–60.
Yang, X. S., and He, X. (2013). “Bat algorithm: Literature review and applications.” Int. J. Bio-Inspired Computation, 5(3), 141–149.
Zong, Q., Wang, F., Tian, B., and Wang, J. (2015). “Robust adaptive approximate backstepping control design for a flexible air-breathing hypersonic vehicle.” J. Aerosp. Eng., 04014107.
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©2017 American Society of Civil Engineers.
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Received: Jun 29, 2016
Accepted: Feb 24, 2017
Published online: May 30, 2017
Published in print: Sep 1, 2017
Discussion open until: Oct 30, 2017
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