## 4 Discussion  
In this work we have presented a two-fold approach to deal with data losses and controller failures in a networked predictive formation control scenario. First a state-of-the-art event based networked model predictive control approach was extended to enable it to adapt to changing network conditions by varying the control horizon length. The theoretical foundation of this adaption based on a Markovian network model was elaborated and a reference implementation further tested on a hardware testbed. Both the HW and SW of the testbed were introduced in the process. The second contribution is a controller failure detection and compensation approach tailored for a possibly large network of heterogeneous cooperating actuator- and controller nodes. The actuator both detects the failure and redistributes the control responsibility to a new controller. Using an auction based methodology for the redistribution it scales well with the number of nodes and allows to optimize for continuing good control performance despite the controller switch.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Node of Sat** & **Free Load** & **Hop Distance to S3** & **Load Cost Cl** & **Distance Cost Cd** & **Bid** \\ \hline
1 & 25\% & 2 & 5.4 & 5 & No Bid \\
2 & 50\% & 1 & 2.35 & 3 & 7 \\
4 & 45\% & 1 & 3.86 & 3 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Individual cost and fused bid values for all candidate nodes of the satellites.  
Figure 18: Control performance of the networked formation control of satellite 3 over a network with Markovian network characteristics with failure of the controller node of satellite 3 at time \(t=35\) s. The remaining control inputs of length \(N_{min}\) stored at the actuator successfully bridge the time until the control task is assigned to a new controller.  
The stability and smooth control behavior during the control handover to another node were demonstrated on the testbed with a controller failure during satellite reconfiguration scenario. Open points for further research are the inclusion of more elaborate models of the network into the control horizon length calculation. This could be realized by having the estimated future network state as part of the MPC state space which some previous scientific contributions in the domain of networked MPC already proposed. Another point for improvement would be to test our approach with more complex scenarios, e.g., a multi-satellite reconfiguration, as well as with non-linear systems with higher dynamic requirements like attitude control. Finally on the control-task redistribution side additional objectives like link-quality between satellites in addition to hop-distance could be used and also inter-task constraints or positive/negative task synergies could be taken into account.  
Conceptualization, F.K. and J.S.; Formal analysis, F.K. and S.H.; Funding acquisition, K.S.; Methodology, F.K., J.S. and S.H.; Project administration, F.K. and J.S.; Software, F.K. and S.H.; Supervision, K.S.; Validation, F.K.; Visualization, F.K. and S.H.; Writing--original draft, F.K. and J.S. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the German Aerospace Center (DLR) within the \"YETE-II\" project under the Grant Agreement No. 50 RA1733. and by the European Space Agency (ESA) by the Networking/Partnering Initiative (NPI) under Contract No. 4000113715/15/NL/MH/ats.  
The authors declare no conflict of interest. The founding sponsors had no role in the writing of the manuscript, and in the decision to publish the results.