This article proposes a fuzzy adaptive fault-tolerant control (FTC) strategy solving the fast fixed-time constrained tracking problem for hypersonic flight vehicles (HFVs). To handle the actuator faults, a two-step design method is first proposed that only needs to estimate the upper and lower bounds of actuator parameters, under which the adverse influences of actuator faults (e.g. lock-in-place, loss of effectiveness, etc.) have been compensated via an adaptive fashion instead of conventional robust ways. In comparison with the state-of-the-art, we have also presented a new fixed-time corollary that has proved a smaller upper bound of convergence time under the same condition of conventional fixed-time stability. To avoid singularity issues often encountered in fixed-time designs, we introduce a piecewise but differentiable switching control laws whose continuity and differentiability are guaranteed everywhere through an appropriate design. Such a design not only preserves the continuity and differentiability of virtual and actual control laws, but also ensures the continuity of their time derivatives. Fuzzy logic systems are exploited to tackle continuous unknown dynamics and asymmetric time-varying barrier functions are utilized to confine flight states within some predefined compact sets all the time provided their initial conditions remain therein. This property is of great significance in dealing with the challenge that the operating regions of the flight state variables are asymmetric and time-varying in practice, especially when executing various flight missions. Comparative simulations have been performed to validate the effectiveness of the proposed control scheme in terms of fixed-time convergence, smoothness and time-varying constraints satisfaction.

To overcome the limited communication bandwidth in the deployment of command, control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) services, a service deployment platform was extended from the cloud to a terminal platform based on the concept of fog computing. Additionally, computing and memory resources on the terminal platform were used to alleviate the communication pressure due to the flow of combat information. A corresponding C4ISR service deployment model was constructed, and the quantum evolutionary algorithm, the shortest path algorithm, and the pairwise exchange method were combined to create a two-level search algorithm. This method can plan the operational information flow path and service deployment simultaneously, thereby achieving the optimal deployment of services even under the constraints of limited communication bandwidth.

In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper introduces a layered obstacle avoidance structure that merges vortex artificial potential (VAPF) fields with reinforcement learning (RL) for motion control. This approach dynamically adjusts the target position through VAPF, strategically guiding the drone to avoid obstacles indirectly. Additionally, it employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to facilitate the training of the motion controller. Simulation experiments demonstrate that the incorporation of the VAPF effectively mitigates the issue of local minima and significantly enhances the success rate of drone navigation, reduces the average arrival time and the number of sharp turns, and results in smoother paths. This solution harmoniously combines the flexibility of VAPF methods with the precision of RL for motion control, offering an effective strategy for autonomous navigation of quadrotor drones in complex environments.

Aiming at the selection problem for course of action (COA) of aviation swarm, this paper proposes an optimized selection method for COA of aviation swarm based on dynamic influence nets (DINs), and discrete artificial bee colony (DABC) algorithm. Firstly, based on the basic concept of the aviation swarm combat plan, static and dynamic modeling and analysis are performed, respectively. Then, the probability propagation mechanism of DINs, which mainly includes key parameter determination and probability propagation algorithm, is established. Subsequently, based on the analysis of the evaluation index, the model is solved by using DABC algorithm with real number coding. Finally, this paper takes the offshore island attack task as an example, and carries out multiple sets of simulation cases to compare DABC algorithm with discrete glowworm swarm optimization (DGSO) algorighm and discrete particle swarm optimization (DPSO) algorithm, through all these cases, the rationality of the model, and the effectiveness and superiority of the algorithm are verified.

As airspace congestion becomes more and more serious, it not only affects the efficiency and quality of aviation activities, but also poses a greater safety hazard. For in-depth exploration of the airspace network, a new airspace association network that differs from the traditional aviation approach of organizing the airspace into a network of sectors based on aircraft traffic flow is proposed. By judging the relationship between the size and location of the airspace and other properties, the establishment of an association network provides new ideas for airspace conflict detection and other aerial tasks. The three key factors of airspace use conflict were extracted and quantified, then the Analytic Hierarchy Process (AHP) was used to assign weights to basic symmetric operators corresponding to three factors to calculate the final airspace correction degree, which reveals the magnitude of correlation degree between the airspace caused by the combined action of the above three factors. Finally, an airspace association network model was established according to the association degree, using basic symmetric operators to explore the general symmetry and network characteristics in real-world airspace association networks, and the feature indices, such as degree, strength, average correlation, clustering coefficient, and structure information entropy of the network, are proposed. The results show that the nodes with higher node degree and node association strength are often key nodes in the network, and the areas with higher average association degree and clustering coefficient are often more prone to airspace conflicts. At the same time, it was also verified that the safety distance between airspace has the greatest impact on the characteristics of the airspace association network.

With the rapid development of the aviation industry, the complexity and intertwinement of various aviation activities have been continuously increasing, leading to the escalating of potential conflicts in limited airspace resources. Efficient airspace management is crucial for flight safety. To address the issues of conflict detection and resolution in localized high-density multi-airspace environments, this paper proposes an end-to-end integrated framework that integrates octree spatial partitioning, hierarchical conflict detection, and a conflict resolution module based on the Improved Whale Migration Algorithm (IWMA). This framework takes the airspace safety separation standards as constraints and the minimization of multi-airspace adjustment offsets as the optimization objective. The IWMA integrates diverse population structures and multi-strategy optimization technologies to balance global search and local exploration capabilities and avoid falling into local optima. Experiments on high-density conflict scenarios of different scales have verified the effectiveness and robustness of the proposed method. Simulation results show that compared with traditional detection methods and other classic optimization algorithms, the conflict detection and resolution method proposed in this paper can quickly and efficiently provide reliable solutions for multi-airspace conflict issues.