Multidisciplinary system design optimization of on orbit satellite assembly architectures

Multidisciplinary system design optimization is performed to determine an optimal on-orbit satellite assembly mission architecture. Modern satellite assembly and launch systems rely on monolithic launches of complete satellites. There are many benefits, however, that may be attained through deviating from the status quo. Launching robotic, on-orbit assemble-able satellite modules increases the number of launches, but since each is launch is of a smaller satellite, the net launch costs can be decreased. Additionally, the price per pound of satellite launches can be decreased through ride-share programs with other missions. Spacecraft versatility is increased when not restricted to the selected launch vehicle payload weight capacity and geometric envelope by instead relying on reconfiguration once in orbit to achieve a final operational configuration. To facilitate the realization of these and other benefits, this paper addresses the architecture of an on-orbit assembly mission that requires an assembly satellite(s) to rendezvous with customer modules and assemble them into a complete satellite in the desired final orbit. A model of this assembly mission is created which calculates costs over the whole multi-assembly mission lifetime of one through five assembler satellites and the downtime which customer satellites experience while on orbit waiting for assembly to be complete. Five design variables are identified for this model, namely, the number of assembler satellites, the number of modules each assembler can manipulate at once, the starting location of each assembler, the autonomy level of the assemblers, and type of transfer orbits utilized by the assemblers. By varying these five aspects of the mission, an optimization is carried out which seeks to minimize total cost of the assembler mission and downtime of the customer satellites while holding to certain parameters and meeting constraints. This paper presents the problem, a simulation of the problem, the optimization methodology, and the results of that optimization.