个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:大连理工大学
学位:博士
所在单位:力学与航空航天学院
学科:动力学与控制. 计算力学. 工程力学
电子邮箱:hjpeng@dlut.edu.cn
Adaptive surrogate model based multi-objective transfer trajectory optimization between different libration points
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论文类型:期刊论文
发表时间:2016-10-01
发表刊物:ADVANCES IN SPACE RESEARCH
收录刊物:SCIE、EI、Scopus
卷号:58
期号:7
页面范围:1331-1347
ISSN号:0273-1177
关键字:Surrogate model; Adaptive sampling; Multi-objective optimization; Libration point; Optimal control; Invariant manifold
摘要:An adaptive surrogate model-based multi-objective optimization strategy that combines the benefits of invariant manifolds and low thrust control toward developing a low-computational-cost transfer trajectory between libration orbits around the L-1 and L-2 libration points in the Sun-Earth system has been proposed in this paper. A new structure for a multi-objective transfer trajectory optimization model that divides the transfer trajectory into several segments and gives the dominations for invariant manifolds and low-thrust control in different segments has been established. To reduce the computational cost of multi-objective transfer trajectory optimization, a mixed sampling strategy-based adaptive surrogate model has been proposed. Numerical simulations show that the results obtained from the adaptive surrogate-based multi-objective optimization are in agreement with the results obtained using direct multi-objective optimization methods, and the computational workload of the adaptive surrogate-based multi-objective optimization is only approximately 10% of that of direct multi-objective optimization. Furthermore, the generating efficiency of the Pareto points of the adaptive surrogate-based multi-objective optimization is approximately 8 times that of the direct multi-objective optimization. Therefore, the proposed adaptive surrogate-based multi-objective optimization provides obvious advantages over direct multi-objective optimization methods. (C) 2016 COSPAR. Published by Elsevier Ltd. All rights reserved.