(350a) Control-Theoretic Considerations for Manufacturing in Space

As commercial space travel becomes more common, it becomes increasingly important to consider how people might be able to survive in distant locations, such as on colonies on Mars or during deep space travel. Since supplies from Earth would take months to reach people in space, help from Earth could not be relied upon when rapid solutions to problems are required, such as during an emergency, which represents a huge reliability and logistics challenge. To handle such issues, it would be expected that the future of commercial space will usher in new manufacturing considerations for production in space [1, 2]. However, the first question to be addressed is what does the future of manufacturing in space look like. Delivering computing devices into space will likely increase expenses and decrease sustainability, as it adds cargo costs into space and back from space or, if computing devices are not returned to earth, it removes otherwise recyclable electrical components from earth’s manufacturing system. Furthermore, if computing devices rapidly develop or are sensitive (e.g., quantum computers), consistently trying to replace computing devices in space used for manufacturing may not provide as streamlined or adaptive of a strategy as might be desired. We could ask if instead Cloud resources on earth might be tapped into to aid in manufacturing. However, the time delays involved present an issue. There have been prior studies on handling asynchronous or delayed measurements even in advanced control laws such as economic model predictive control [3]; however, delays in space are different because they involve both delay in the receipt of sensor measurements and actuator outputs.

In this work, we use several control-theoretic approaches to analyze the conditions under which manufacturing in space could take advantage of computations on earth. In one case, we utilize predictions of the process state both as the measurements reach the earth and as the control actions return to space to compute control actions. In another, we send a set of control actions back to the space manufacturing system and clarify what range of state measurements should lead the controller to select one of the control actions. We consider conditions under which doing this constant communication is an improvement to storing some set of control action-state measurement pairs in the space manufacturing system. We also discuss data privacy issues by discussing concepts which exist for securing Cloud computation (e.g., homomorphic encryption [4, 5]) or where redundancy might be used to aid in attempting to confuse an attacker.

[1] Owens, A., and De Weck, O., 2016. “Systems Analysis of In-Space Manufacturing Applications for the International Space Station and the Evolvable Mars Campaign.” AIAA SPACE 2016. 5394.

[2] Makaya, A., Pambaguian, L., Ghidini, T., Rohr, T., Lafont, U., and Meurisse, A., 2022. “Towards out of earth manufacturing: overview of the ESA materials and processes activities on manufacturing in space.” CEAS Space Journal pp.1-7.

[3] Heidarinejad, M., Liu, J., Christofides, P.D., 2012. “Economic model predictive control of nonlinear process systems using Lyapunov techniques.” AIChE Journal 58, pp. 855-870.

[4] Morris, L., 2013. “Analysis of partially and fully homomorphic encryption.” Rochester Institute of Technology pp. 1-5.

[5] Caroline, F. and Galand, F., 2007. “A survey of homomorphic encryption for nonspecialists.” EURASIP Journal on Information Security 2007 pp. 1-10.