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.
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Published In
Journal of Aerospace Engineering
Volume 30 • Issue 5 • September 2017
Copyright
©2017 American Society of Civil Engineers.
History
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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