###### Abstract
Distributed spacecraft systems with the described architecture were pioneered by the F6 program of DARPA [10; 11] which was unfortunately cancelled before its in-orbit demonstration in 2013 [12]. Distributed modular spacecraft systems, due to their apparent benefits, have nevertheless seen further continuation in several other projects both with a theoretical [5; 13; 14; 15; 16; 17] and application focus [18; 19]. Notable projects include the iBOSS initiative, that demonstrated the reconfiguration of the building blocks/nodes of a modular satellite with robotic manipulators on ground [20; 21], the Innocube satellite mission, which is scheduled to launch in 2023 [22] and is expected to demonstrate the wireless harness of a modular satellite under space conditions [23] and finally the Y  
Figure 1: Sketch of a fractionated spacecraft consisting of cooperating, wirelessly connected sensor-, actuator- and computing hardware nodes. Sensor data acquisition is handled by sensor nodes, low-level driving of the actuators is handled by the actuator nodes and high level data processing and control is dealt with by the computing nodes. projects [25; 26; 27], which developed formation and attitude control for distributed spacecraft and constitute the precursor missions for the Tamariw project, which is scheduled to demonstrate these technologies with an in-orbit assembly of two Cubesats in 2025. Despite the aforementioned advantages of such distributed spacecraft architectures, there are also several new challenges that these missions face. Especially the coordination and control of such a system of systems over a possibly unreliable and imperfect communication network poses a major task. It is therefore of utmost importance to have suitable control approaches for attitude and formation control, two of the most common control tasks in most missions, that are able to deal with the intra- and inter-satellite node network characteristics. The influencing characteristics of the node network on the control performance are mainly the delay and possible loss of information exchanged in the control loop. These effects can occur in the distributed control-loop on the network connection between sensor-node and controller-node and between controller-node and actuator-node. There already exist several control approaches which take one or more of these delay/loss scenarios into account. Specifically for wireless control networks there are recent developments to employ event-driven control to such systems to compensate for network effects and limit the required communication amount [28; 29; 30], some of them are already tailored for formation and attitude control of spacecraft [31; 32; 33]. A distributed robust \(h_{\infty}\) control approach where one controller node aboard one modular satellite controls the actuators of two satellites under the presence of intermittent sensor data losses was investigated in [34]. A further increase in control constraint capabilities while allowing information losses on the controller to actuator communication link in addition to sensor data losses was presented for model predictive attitude control in [25]. The development of optimal predictive formation control subject to sensor- and controller-data losses and delays was realized in [27]. All previous control approaches focus on how the controller node can compensate for the network effects such as information loss and delay and how it can optimize the single- or multi satellite performance. What happens however, if the controller node itself fails, either because of a hardware/software fault on the controller node or loss of connectivity of the controller node for a significant amount of time? This constitutes a central point of failure and can lead to partial or complete loss of a spacecraft for the mission objective. Therefore, in order to further increase the resilience and robustness of the fractionated multi-satellite architecture, a new approach to detect and then heal or mitigate the loss is needed.