This talk introduces a digital twin (DT) formulation of two interacting spacecraft during close-proximity on-orbit operations. We leverage dynamics-based models for the rendezvous of a chaser and a noncooperative target spacecraft, as well as their implementation via both optimal control [1,2] and model predictive control [3]. Continuous data assimilation techniques are implemented to track the satellites' trajectories and the evolution of key model parameters, such as spacecraft geometry, inertia, and attitude. As more refined data becomes available during the maneuver lifetime, a continuously calibrated representation of the context and behavior of the interacting spacecraft is enabled with an appropriate representation of the uncertainty in the digital twin state. We formulate these tasks for each spacecraft instance via a graphical model developed for digital twins [4] to account for the uncertainty associated with the quantities of interest. A multiple-instance digital twin abstraction is then implemented to model the interaction between the separate spacecraft instances, resulting in a DT-enabled rendezvous operation that drives the mission's success by optimizing specific maneuvering metrics, such as the risk of collision, precision, and fuel consumption. REFERENCES [1] B. P. Malladi, R. G. Sanfelice, and E. A. Butcher, “Robust hybrid supervisory control for spacecraft close proximity missions,” Annual Reviews in Control, vol. 52. pp. 316–329, 2021. doi: 10.1016/j.arcontrol.2021.11.001 [2] G. Boyarko, O. Yakimenko, and M. Romano, “Optimal Rendezvous Trajectories of a Controlled Spacecraft and a Tumbling Object,” Journal of Guidance, Control, and Dynamics, vol. 34, no. 4. American Institute of Aeronautics and Astronautics (AIAA), pp. 1239–1252, 2011. doi: 10.2514/1.47645 [3] C. Petersen, and I. Kolmanovsky, "Coupled Translation and Rotation Dynamics for Precise Rendezvous and Docking with Periodic Reference Governor Control Scheme," Proceedings of 26th AAS/AIAA Space Flight Mechanics Meeting, Paper No. AAS 16-507, AAS/AIAA, Napa, CA, 2016 [4] M. G. Kapteyn, J. V. R. Pretorius, and K. E. Willcox, “A probabilistic graphical model foundation for enabling predictive digital twins at scale,” Nature Computational Science, vol. 1, no. 5, pp. 337–347, 2021. doi: 10.1038/s43588-021-00069-0