Multidisciplinary Collaborative Optimization of Mars Probe Based on Dynamic Penalty Function

Abstract

Focusing on the system design problem of Mars probe which is far away from the Earth and has the complex flight environment，the optimization function with comprehensive objective combined by coverage rate and ground sample distance was set up. Considering the orbit discipline，the payload discipline and the power discipline，the system-level and discipline-level optimization model of the Mars probe were established based on the cooperative optimization method，thus decomposing the complicated optimization problem into parallel cooperative optimization problems with multiple sub-disciplines. The adaptive dynamic penalty function is used to accelerate the convergence. The numeric results indicate that the collaborative optimization can be successfully applied in the system optimal design of Mars probe.

Keywords

Mars probe / system optimal design / modeling / collaborative optimization / penalty function

References

[1] Wu B，Huang H，Wu W. A non-nested collaborative optimization method for multidisciplinary design problems[C]// 16th International Conference on Computer Supported Cooperative Work in Design （CSCWD）. Wuhan：IEEE，2012.
[2] 赵凡宇,徐瑞,崔平远. 资源约束突破的航天器观测快速重调度优化算法[J]. 深空探测学报,2015,2(3):262-266
Zhao F Y,Xu R,Cui P Y. Measurement of fast recurrence of spacecraft observation based on resource constraint[J]. Journal of Deep Space Exploration,2015,2(3):262-266
[3] Braun R D. Collaborative optimization：an architecture for large-scale distributed design[D]. California：Stanford University，1996.
[4] Zheng Y，Li G H，Fu J M. A distributed MDO architecture and its application on small satellite[C]//Proceedings of SPIE-The International Society for Optical Engineering. USA：[s.n]，2009.
[5] Hwang J T，Lee D Y，Cutler J W. Large-scale MDO of a small satellite using a novel framework for the solution of coupled systems and derivatives[C]//Structural Dynamics and Materials Conference. Boston：AIAA，2013.
[6] San Scoucie M P，Hull P V，Tinker M L，et al. Lunar habitat optimization using genetic algorithms[R]. USA：NASA，2007.
[7] 裴晓强,黄海. 协同优化在卫星多学科设计优化中的初步应用[J]. 宇航学报,2006,27(5):1054-1058
Pei X Q,Huang H. Application of collaborative optimization in multi-disciplinary design optimization[J]. Journal of Astronautics,2006,27(5):1054-1058
[8] 谭春林,庞宝君,张凌燕,等. 对地观测卫星总体参数多学科优化[J]. 北京航空航天大学学报,2008,34(5):529-532
Tan C L,Pang B J,Zhang L Y,et al. Multidisciplinary optimization of the parameters of ground observation satellites[J]. Journal of Beijing University of Aeronautics and Astronautics,2008,34(5):529-532
[9] 张凌燕. 机动卫星总体多学科设计优化研究[D]. 北京：北京航空航天大学，2009.
Zhang L Y. Study on the optimization of the overall multidisciplinary design of maneuvering satellites[D]. Beijing：Beijing University of Aeronautics and Astronautics，2009.
[10] 李海燕，井元伟，马明旭，等. 基于动态罚函数法的协同优化算法[J]. 控制与决策，2009，24（6）：911-915，920-921.
Li H Y，Jing Y W，Ma M X，et al. Cooperation optimization algorithm based on dynamic penalty function method[J]. Control and Decision，2009，24（6）：911-915，920-921.
[11] 李海燕,马明旭,黄章俊,等. 自适应罚函数协同优化算法[J]. 系统仿真学报,2009,21(19):6178-6182
Li H Y,Ma M X,Huang Z J,et al. Adaptive penalty function co-optimization algorithm[J]. Journal of System Simulation,2009,21(19):6178-6182
[12] 赖宇阳. Isight参数优化理论与实例详解[M]. 北京：北京航空航天大学出版社，2012.
Lai Y Y. Isight parameter optimization theory and examples[M]. Beijing：Beihang University Press，2012.