# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 7 Conclusions  
This study proposes a resolution-based mission process control strategy. The effectiveness of the proposed method is tested in four cases, and several conclusions can be drawn. One is that when the mission process is not disturbed, there is no need to impose a control strategy, and the mission process can fully track the mission plan. Secondly, in the event of a disturbance to the mission process, the absence of a control strategy results in a delay to the mission plan, which accumulates over time due to the system state equations. Thirdly, in the case of a disturbance to the mission process, provided that the control strategy proposed in this paper is applied within the maximum anti-disturbance capability of the system, the error can be eliminated accurately and quickly, thus avoiding plan delays. Fourthly, once the disturbance exceeds the maximum anti-disturbance capability of the system, the disturbance cannot be completely eliminated through plan adjustment. Consequently, it is imperative that the mission manager pays close attention to the disturbance and takes control measures as early as possible. In conclusion, this paper addresses the issue of plan description and process control for large-scale group missions, offering a valuable reference for plan management. Conceptualization, R.Y. ; methodology, Y.W., Q.S. and A.W. ; software, X.X. and A.W. ; validation, H.L. ; formal analysis: Q.S. ; investigation, Y.W. ; resources, R.Y. ; data curation, X.X. ; writing--original draft preparation, A.W. and Q.S. ; writing--review and editing, H.L. ; visualization, A.W. and R.Y. ; supervision, R.Y. and H.L. ; project administration, R.Y. ; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(t\)} & \multicolumn{8}{c}{\(u\_i(t)\) (min)} & \multirow{2}{*}{\(y(t)\)} \\ \cline{2-2} \cline{4-7}  & **G2** & **D2** & **C2** & **F2** & **E4** & **E6** \\ \hline
5 & \(-3\) & 0 & 0 & 0 & 0 & 0 & 3 \\
16 & 0 & \(-4\) & 0 & 0 & 0 & 0 & 4 \\
25 & 0 & \(-1\) & 0 & 0 & 0 & 0 & 1 \\
36 & 0 & 0 & \(-1\) & \(-1\) & 0 & 0 & 1 \\
46 & 0 & 0 & 0 & \(-2\) & \(-3\) & 0 & 5 \\
62 & 0 & 0 & 0 & 0 & \(-2\) & \(-4\) & 6 \\
72 & 0 & 0 & 0 & 0 & 0 & \(-3\) & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The control quantities \(u_{i}(t)\) for each controllable moment in Experiment 4. The first column represents each controllable moment, and the last column of the table represents the system output \(y(t)\). **Funding:** This research was funded by the National Social Science Foundation of China grant number 2022-SKJ)-B-051. **Data Availability Statement:** The videos of four experiments are available at [[https://gitee.com/Volvo123/mission-process-control](https://gitee.com/Volvo123/mission-process-control)]([https://gitee.com/Volvo123/mission-process-control](https://gitee.com/Volvo123/mission-process-control)) (accessed on 10 May 2024). **Conflicts of Interest:** The authors declare no conflicts of interest. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Maximizing robustness of aircraft routing with heterogeneous maintenance tasks. _Transp. Res. E Logist. Transp. Rev._**2021**, _177_, 103237. [CrossRef]
* [PERSON] and [PERSON] (2021) [PERSON]; [PERSON]; [PERSON] Airline disruption management: A literature review and practical challenges. _Comput. Oper. Res._**2021**, _127_, 105137. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Dynamic aircraft recovery problem-an operational decision support framework. _Comput. Oper. Res._**2020**, _117_, 104892. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] A distributionally robust optimization approach for airline integrated recovery under in-flight pandemic transmission risks. _Transp. Res. C Emerg. Technol._**2023**, _152_, 104188. [CrossRef] [PubMed]
* [PERSON] et al.