基于高保真度代理模型的卫星结构优化

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (23) : 208-215.

论文

基于高保真度代理模型的卫星结构优化

杨丽丽1，孔祥龙1,2，李文龙1，许浩1，尤超蓝1

作者信息 +

Satellite structure optimization based on high fidelity surrogate model

YANG Lili1, KONG Xianglong1,2, LI Wenlong1, XU Hao1, YOU Chaolan1

Author information +

文章历史 +

摘要

为了提高卫星结构优化的设计质量和计算效率，本文结合径向基函数（RBF）代理模型和自适应模拟退火（ASA）算法提出了一种基于高保真度动态代理模型（HFDSM）的全局优化方法。该方法依据全局优化结果构造了一种搜索空间自适应更新策略，在优化过程中完成搜索空间的更新后，在其内部补充样本点以及重建近代理模型，并以最优解的预测误差和目标函数的下降程度作为优化过程收敛的判定准则，保证了优化的全局收敛性和最优解处的模型精确性。高维测试函数和工字梁算例的优化结果表明该方法不仅能获得高精度的优化结果，还显著提高了优化求解的效率。最后，采用该方法求解某高维卫星结构优化问题，优化结果中结构基频及动力学响应等约束函数的最大预测误差仅为0.65%，并且相对于直接采用自适应模拟退火算法进行求解，时间成本降低了50%以上，从而验证了该方法在求解卫星结构设计优化问题时具有很高的精确性和计算效率。

Abstract

To improve the design quality and computation efficiency of satellite structural optimization problems, a global optimization method based on high fidelity dynamic surrogate model（HFDSM）was proposed by combining radial basis function（RBF） surrogate model and adaptive simulated annealing （ASA）algorithm. In this method, an adaptive updating strategy of search space was constructed according to the global optimization results. During optimization process, new sampling points were added in the updated search space and then the surrogate model was reconstructed. The prediction error of surrogate model in the optimal point and the decreasing degree of the objective function were considered as the termination criteria of optimization process simultaneously, so that the global convergence of optimization and the model accuracy at the optimal solution were guaranteed. The optimization results of high dimensional test functions and the I-beam design problem show that the presented method can improve the optimization efficiency significantly with high accuracy. Finally, the proposed method was applied to a high dimensional optimization problem of satellite structure. In the optimization results, the maximum prediction error of constraints such as the fundamental frequency and structural dynamic response was only 0.65%, and the computational expense was reduced by more than 50% compared with directly adapting the adaptive simulated annealing algorithm. As a result, the proposed optimization method was validated in solving satellite structural problems with high accuracy and efficiency.

导出引用

杨丽丽1，孔祥龙1,2，李文龙1，许浩1，尤超蓝1. 基于高保真度代理模型的卫星结构优化[J]. 振动与冲击, 2021, 40(23): 208-215

YANG Lili1, KONG Xianglong1,2, LI Wenlong1, XU Hao1, YOU Chaolan1. Satellite structure optimization based on high fidelity surrogate model[J]. Journal of Vibration and Shock, 2021, 40(23): 208-215

参考文献

[1] Yoon Y H, Moon J K, Lee E S, et al. Parallel optimal design of satellite bus structures using particle swarm optimization[C]//Proceedings of the 48th AIAA/ASME/ASCE/AHS/ASC Structural Dynamics, and Materials Conference, Honolulu, Hawaii, 2007.
[2] 夏昊, 陈昌亚, 王德禹.基于多目标粒子群算法的卫星结构动力学优化[J].上海交通大学学报, 2015, 49(9): 1400-1403.
XIA Hao, CHEN Changya, WANG Deyu. Dynamical optimization of satellite structure based on multi-objective particle swarm optimization algorithm [J].Journal of Shanghai Jiaotong University, 2015, 49(9): 1400-1403.
[3] 郑侃, 廖文和, 张翔.基于近似模型管理的微小卫星结构多目标优化设计[J].中国机械工程, 2012, 23(6): 655-659.
ZHENG Kan, LIAO Wenhe, ZHANG Xiang. Multi-objective optimization design for microsatellite structure based on approximation model management [J]. China Mechanical Engineering, 2012, 23(6): 655-659.
[4] 袁野, 陈昌亚, 王德禹.基于支持向量机的卫星动力学多目标优化[J].振动与冲击, 2013, 32(22): 189-192.
YUAN Ye, CHEN Changya, WANG Deyu. Multi-objective dynamic optimization of a satellite based on support vector machine [J]. Journal of Vibration and Shock, 2013, 32(22): 189-192.
[5] Wang G G, Dong Z M, Aitchison P. Adaptive response surface method—a global optimization scheme for approximation-based design problems [J]. Engineering Optimization, 2001, 33(6): 707-733.
[6] Wang G G. Adaptive response surface method using inherited Latin hypercube design points [J]. Journal of Mechanical Design, 2003, 125(2): 210-220.
[7] Jones D R, Schonlau M, Welch W J. Efficient global optimization of expensive black-box functions[J]. Journal of Global Optimization, 1998, 13(4): 455-492.
[8] Viana F C, Haftka R, Watson L. Efficient Global Optimization Algorithm Assisted by Multiple Surrogate Techniques [J]. Journal of Global Optimization, 2013, 56(2): 669–689.
[9] Hu W, Li M, Azarm S, et al. Multi-objective robust optimization under interval uncertainty using online approximation and constraint cuts [J]. Journal of Mechanical Design, 2011, 133(6): 61002.
[10] Hu W, Azarm S, Almansoori A. New approximation assisted multi-objective collaborative robust optimization (new AA-McRO) under interval uncertainty [J]. Structural and Multidisciplinary Optimization, 2013, 47(1): 19-35.
[11] 曾锋, 周金柱. 集成最小化置信下限和信赖域的动态代理模型优化策略[J]. 机械工程学报, 2017, 53(13): 170-178.
ZENG Feng, ZHOU Jinzhu. Optimization strategy for dynamic metamodel integrating minimize lower confidence bound and trust region [J]. Journal of Mechanical Engineering, 2017, 53(13): 170-178.
[12] Peng L, Liu L, Long T, et al. An Efficient Truss Structure Optimization Framework Based on CAD/CAE Integration and Sequential Radial Basis Function Metamodel [J]. Structural and Multidisciplinary Optimization, 2014, 50(2): 329–346.
[13] 龙腾, 郭晓松, 彭磊, 等.基于信赖域的动态径向基函数代理模型优化策略[J]. 机械工程学报, 2014, 50(7): 185-190.
LONG Teng, GUO Xiaosong, PENG Lei, et al. Optimization strategy using dynamic radial basis function metamodel based on trust region [J]. Journal of Mechanical Engineering, 2014, 50(7): 185-190.
[14] Wang D, Wu Z, Fei Y, et al. Structural design employing a sequential approximation optimization approach [J]. Computers and Structures, 2014, 134: 76-87.
[15] Shi R, Liu L, Long T, et al. Sequential Radial Basis Function Using Support Vector Machine for Expensive Design Optimization [J].AIAA Journal, 2017,55(1):214-227.
[16] Xing J, Luo Y, Gao Z. A global optimization strategy based on the Kriging surrogate model and parallel computing [J]. Structural and Multi-disciplinary Optimization, 2020: 1-13.
[17] Jin R, Chen W, Simpson T W. Comparative studies of metamodeling techniques under multiple modeling criteria [J]. Structural and Multidisciplinary Optimization,2001, 23(1):1–13.
[18] 夏昊, 诸成, 陈昌亚, 等.基于近似模型的卫星动力学多目标优化[J].上海航天, 2013, 30(5): 48-52.
XIA Hao, ZHU Cheng, CHEN Changya, et al. Dynamic multi-objective optimization of satellite based on approximation models [J]. Aerospace Shanghai, 2013, 30(5): 48-52.
[19] Gutmann H M. A radial basis function method for global optimization [J]. Journal of Global Optimization, 2001, 19: 201-227.
[20] Ingber L. Adaptive simulated annealing (ASA): Lessons learned [J]. Control & Cybernetics, 1996, 25(1): 33-54.