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The advancement of Earth observation satellite research in past decades has demonstrated itself to be productive and increasingly important. Utilized for applications such as climate monitoring, communication, GPS, defense, and space research, our dependence on reliable satellite systems is ever-increasing. The success of satellites in these scenarios is fundamentally the result of its attitude determination system, consisting of control and estimation subsystems, which govern its sensors and actuators. For simple missions, attitude pose determination can be computed onboard the satellite. Typically, however, ground stations or other satellites (i.e. constellations) are involved in a satellite’s operation, processing large amounts of data or complex control algorithms. This information and control cycle is enabled through the application of Networked Control Systems (NCS). The NCS uses a wireless network or communication system as the intermediate line of communication between plant, actuators, sensors, and other systems. This enables relatively fast communication and data transmittance over long distances, as well as the decentralization of navigation and control through system distribution. However, this method is vulnerable to various forms of time delay and packet loss, which ultimately affects the control performance of a satellite. It is demonstrated in literature that the effects of these NCS properties can be mitigated, increasing its viability, through various implementations of smart systems into the satellite framework. Using techniques such as neural networks and reinforcement learning, the satellite can perceive and act based on environmental information, while considering experiential memory and attention allocation. The following comprehensive survey discusses methods for improving the robustness of networked satellite systems from a smart systems perspective, providing an advanced foundation for these concepts.
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