Rapid launch is one of the key indicators of UAV swarm operations, and one of the key decisions that determines the ultimate success of launch system development is the choice of its product architecture. The high-definition design structure matrix (HDDSM) method is introduced to model the product architecture of a UAV launch system, which reduces the workload of creating binary DSMs and improves the repeatability of product architecture design matrix (PADSM) creation. On this basis, this paper presents the classification of row (column) elements based on PADSM to correct the clustering objective function, and introduces the network structure clustering algorithm (SCAN). Combined with matrix block processing, a better UAV launch system architecture modular configuration scheme is obtained.

To improve the crashworthiness of the landing gear buffer, an oleo-pneumatic buffer combined with an expansion tube is proposed in this paper. A numerical analytical model based on deformation theory was used to acquire the buffer force of the combined-type buffer during crushing. A finite-element model (FEM) was established to simulate the crushing process of the landing gear expanding buffer at the crash speed. The buffer force–displacement curve was exported from the analysis results, and the buffer force in the stable period was compared with that obtained from the theoretical analytical model. The deviation, 10.99%, verified the effectiveness of the expansion tube FEM model. Buffer performance analysis models of oleo-pneumatic and metal expansion tube oleo-pneumatic combing buffers with high- and low-pressure chambers were established. A parametric model of the landing gear was established. The crashworthiness response of different landing gear buffers at crash speed was studied. The results proved the high buffer efficiency of the combined-type buffer and its superiority and feasibility.

This study introduces an advanced dual-mode compound attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers and dynamically adjusting the inertia weights and learning factors of the PSO algorithm to achieve the balance between global and local optimization performance, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers’ weighting coefficients, culminating in an optimized dual-mode compound attitude control system. A series of comparative simulation analyses are thoroughly executed to appraise the system’s responsiveness, stability, precision, and resilience to interference. The simulation outcomes demonstrate an average reduction of 42.21% in step response overshoot, an 18.52% decrease in settling time, a 53.18% decline in steady-state error, a 56.80% drop in the maximum deviation value, a 55.82% improvement in recovery speed, and a 75.61% enhancement in tracking precision for the proposed controller. The findings clearly verify the superior performance of the proposed control system, affirming its contribution to the advancement of RLV attitude control. The proposed controller holds promising potential for real application in attitude control systems and is poised to augment the reliability and mission success rate of RLVs under intricate flight scenarios.

Carrier-based unmanned aerial aircraft (UAV) structure is subjected to severe tensile load during takeoff, especially the drawbar, which affects its fatigue performance and structural safety. However, the complex structural features pose great challenges for the engineering design. Considering this situation, a structural design, fatigue analysis, and parameters optimisation joint working platform are urgently needed to solve this problem. In this study, numerical analysis of strain fatigue is carried out based on the laboratory fatigue failure of the carrier-based aircraft drawbar. Taking the sensitivity of drawbar parameters to stress and life into account and optimum design of drawbar with fatigue life as a target using the parametric method, this study also includes cutting-edge parameters of milling cutters, structural details of the drawbar and so on. Then an experimental design is applied using the Latin hypercube sampling method, and a surrogate model based on RBF neural network is established. Lastly, a multi-island genetic algorithm is introduced for optimisation. The results show that the error between the obtained optimal solution and simulation is 0.26%, while the optimised stress level is reduced by 15.7%, and the life of the drawbar is increased by 122%.