Filter-Based Sequential Radial Basis Function Method for Spacecraft Multidisciplinary Design Optimization

Spacecraft system design is practically a complex multidisciplinary design optimization (MDO) problem. Because of the application of high-fidelity simulation models, the massive computational cost of spacecraft MDO problems becomes a bottleneck and challenging problem in engineering practices. To address the issue, this paper proposes a novel filter-based sequential radial basis function (FSRBF) method for effectively and efficiently solving spacecraft MDO problems. In FSRBF, to handle expensive constraints, a filter is constructed, augmented, and refined based on the concept of Pareto nondomination, which is then combined with a support vector machine (SVM) to construct the filter-based region of interest (FROI) for sequentially bias sampling. During the optimization process, the expensive multidisciplinary analysis process is approximated by RBF metamodels to reduce the computational cost. The RBF metamodels are gradually updated via consecutively sampling within the FROI, which leads the search to rapidly reach the feasible optimum. A number of numerical benchmark problems are used to demonstrate the desirable performance of the proposed FSRBF compared with several alternative methods. In the end, FSRBF is applied to the design of an all-electric GEO satellite and a small Earth observation satellite to illustrate its capability for real-world spacecraft MDO problems. The results show that FSRBF can successfully obtain feasible solutions to improve the design quality of satellite systems. Moreover, the required computational cost of FSRBF is much lower than that of competitive methods, which illustrates the effectiveness and practicality of the proposed FSRBF for solving spacecraft MDO problems.