This paper investigates the distance-based formation and cooperative path-following control problems for multiple fixed-wing unmanned aerial vehicles (UAVs). In this study, we design the distance-based formation control structure to achieve the virtual leader and followers pre-defined rigid formation pattern, ensuring simultaneously relative localization. A path-following control strategy based on adaptive dynamic surface and neural network control technology is proposed to approximate the uncertain disturbances of the environment and unmodeled dynamics. And the longitudinal and lateral subsystems’ adaptive fault-tolerant controllers are designed, respectively, to achieve the fault-tolerant control of UAVs’ formation in three-dimensional environments. Furthermore, the adaptive sliding mode controller with an auxiliary controller is designed to realize the UAVs path following with limited input saturation. Finally, simulation examples are given to clarify and verify the effectiveness of the theoretical results.

Network repair is indispensable for maintaining network security. Conventional static repair is relatively inefficient. In this study, by considering the energy transfer between nodes, a dynamic repair model was established. The fundamental reason for the secondary failure of repaired nodes during the dynamic repair process is the coupling structure of failure networks. A dynamic repair strategy was proposed that can effectively prevent the secondary failure of repair nodes influenced by energy during repair and can cause the redundant capacity of repair nodes to be used reasonably. By turning off the energy transfer function of the link to control the excessive flow of energy into the repair node to avoid the occurrence of secondary failure; on the other hand, by sharing part of the load of the failure node, realize the rational use of the redundant capacity of the repair node to reduce the impact of the failure node on the overall function of the network. The proposed strategy mitigated the effect of failure nodes on network functions and substantially improved the recovery efficiency of network functions. Furthermore, redundant edges, behaving as energy redundant links in a network structure, can considerably improve the robustness of the network by optimizing the removal of redundant edges. Dynamic repair is not only an efficient repair method but also a highly flexible choice for network repair.

Ignoring real decision contexts in linguistic multi-criteria classification (MCC) causes unreasonable weights, rank reversal and unreliable classification. This paper develops a context-constrained MCC framework integrating case-based reasoning, grey target theory and cloud model. Unlike conventional data-only grey target methods, three practical contextual features structurally reshape all algorithm modules: linguistic dual fuzziness-randomness, labeled benchmark cases and expert weight preferences. First, cloud Hamming distance and cloud-based dual target centers are constructed to eliminate rank reversal, a property invalid without linguistic uncertainty. Second, benchmark case constraints build a target-score classification mechanism with case-calibrated grading thresholds. Third, expert weight bounds form a bi-objective optimization model fusing multi-source information to avoid data-biased weights. A riparian wetland ecological grading case validates the method. A rigorous mathematical proof, counterexample contrast and large-scale random simulation are further supplemented to verify that the proposed fixed dual cloud target center rule significantly mitigates rank-reversal phenomena triggered by adding new alternatives under the designed experimental settings. Comparisons show our framework yields domain-consistent weights, grading outputs with perfect agreement with independent blind expert judgments, and stable monotonic scoring performance (where larger target scores correspond to worse ecological conditions), which achieves more consistent grading results compared with cloud entropy weighting and virtual linguistic approaches in the tested wetland case.