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This paper investigates applying artificial intelligence (AI) algorithms to attitude control system of satellites to optimally tune the controller using high performance computing. This methodology is applied to the Virtual Telescope for X-ray Observation mission, which is a precise formation of two separate spacecraft observing multiple objects in the space in the X-ray domain. The mission is divided into phases based on the instrumentation and the mission goal. To reach an stable precise formation robust to stochastic slew and slew rate (i.e., Euler angles and angular velocities) in a minimal constrained time <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>, consumed energy of the attitude control system, denoted as <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>, and root-mean-square state error of attitude control system, denoted as <inline-formula> <tex-math notation="LaTeX">$e$ </tex-math></inline-formula>, are minimized. Monte-Carlo simulation is used for the sensitivity analysis of optimization and designing a controller. Deep neural networks (DNN), Gaussian processes (GP), and support vector regression (SVR) learn this optimization as a surrogate model, while their hyperparameters are optimized in a novel approach. THETA supercomputer at Argonne Leadership Computing Facility (ALCF) is used for optimizing the hyperparameters of DNN. The surrogate model meets the requirements of the mission, and it shows a better performance over the optimization and Monte-Carlo. The optimal DNN can satisfy the mission requirements <inline-formula> <tex-math notation="LaTeX">$e$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> while reducing <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula> for 90&#x0025; compared to the other given methods.

Designing Monte Carlo Simulation and an Optimal Machine Learning to Optimize and Model Space Missions