Surrogate-Based Optimization Design for Air-Launched Vehicle Using Iterative Terminal Guidance

Abstract

In recent years, the penetration of low-cost air-launched vehicles for nano/micro satellites has significantly increased worldwide. Conceptual design and overall parameters optimization of the air-launched vehicle has become an exigent task. In the present research, a modified surrogate-based sequential approximate optimization (SAO) framework with multidisciplinary simulation is proposed for overall design and parameters optimization of a solid air-launched vehicle system. In order to reduce the large computation costs of time-consuming simulation, a local density-based radial basis function is applied to build the surrogate model. In addition, an improved particle swarm algorithm with adaptive control parameters is proposed to ensure the efficiency and reliability of the optimization method. According to the LauncherOne air-launched vehicle, the overall optimization design problem aims to improve payload capacity with the same lift-off mass. Reasonable constraints are imposed to ensure the orbit injection accuracy and stability of the launch vehicle. The influences of the vehicle configuration, optimization method, and terminal guidance are considered and compared for eight different cases. Finally, the effect on the speed of optimization convergence of employing a terminal guidance module is investigated. The payload capability of the optimized configurations increased by 27.52% and 23.35%, respectively. The final estimated results and analysis show the significant efficiency of the proposed method. These results emphasize the ability of SAO to optimize the parameters of an air-launched vehicle at a lower computation cost.