In hypersonic flight control, characterized by challenges posed by input saturation, model parameter uncertainties, and external disturbances, this paper introduces a pioneering anti-input saturation control method based on RBFNN adaptivity. We have developed adaptive laws to enhance control system adaptability and robustness by integrating mission profiles, actuator saturation failure modes, and self-evolving neural network design. Furthermore, our approach introduces a novel anti-input saturation auxiliary system, effectively addressing input saturation constraints. This innovation ensures system stability and precise tracking, even in severe input saturation constraints. The results reveal that the system’s steady-state tracking error remains under 2% under input saturation constraints, and the convergence speed demonstrates an impressive 20% improvement. These findings underscore this research’s substantial advancement in hypersonic flight control. It may significantly enhance the controllability and performance of hypersonic vehicles in real-world scenarios.

The thermal problem of high-altitude airships has an essential impact on position control and energy system performance. Adjusting the airship’s attitude angle causes differences in thermal performance during position alterations. This paper studies an airship’s energy and thermal performance under variable attitudes. We establish an airship solar radiation and thermal model to analyze power output under different thermal conditions. The thermal performance of airships at varying pitch angles is investigated using computational fluid dynamic (CFD) software (Fluent V6.3.26). To determine the optimal distribution of pitch angles under various conditions, we have developed an optimization model that considers both the presence and absence of thermal influence. We have also assessed the impact of airship geometric parameters on the optimal pitch angle, considering the diversity of airship shapes. Our results demonstrate that pitch angle alterations significantly influence airships’ temperature and flow field distribution. But the degree of necessity of considering the thermal effect in calculating the optimal pitch angle distribution varies depending on the date and latitude, with the most vital need observed during low-latitude summer and the weakest during high-latitude winter. The findings of this research have significant reference value in selecting operation strategies and the control of operating performance for high-altitude airships.

Evaluating the mission efficiency of various drone configurations under complex, multi-source, and multi-dimensional requirements remains a significant challenge. This study aimed to develop a comprehensive decision support system (DSS) that employs mission efficiency evaluation, probabilistic hesitant fuzzy sets (PHFs), and multi-attribute decision-making (MADM) methods to assess and optimize drone design. In the proposed method, mission efficiency is defined as a composite measure of the flight performance, adaptability, and economic viability required to complete a mission. By designing a “demand–capability–design” mapping approach, this system effectively resolves multi-attribute conflicts in the decision-making process. To demonstrate the proposed approach, a set of small electric vertical takeoff and landing fixed-wing (e-VTOLFW) drones are compared and ranked based on their mission efficiency. The impacts of different mission requirements on drone evaluation are also discussed. The results demonstrate that this model resolves the traditional issue of unclear information flow in drone design. By improving the evaluation criteria, it enhances informed decision making and the robustness of evaluation results in drone design assessments. Additionally, the model is generalizable and can be widely applied to similar fields such as “demand–product design”, improving the understanding and optimization of product performance.

Conventional fixed-gain PID controllers face inherent limitations in maintaining optimal performance across the diverse and dynamic flight phases of cruise missiles. To overcome these challenges, we propose Time-Fusion Proximal Policy Optimization (TF-PPO), a novel adaptive reinforcement learning framework designed specifically for cruise missile control. TF-PPO synergistically integrates Long Short-Term Memory (LSTM) networks for enhanced temporal state perception and phase-specific reward engineering enabling self-evolution of PID parameters. Extensive hardware-in-the-loop experiments tailored to cruise missile dynamics demonstrate that TF-PPO achieves a 36.3% improvement in control accuracy over conventional PID methods. The proposed framework provides a robust, high-precision adaptive control solution capable of enhancing the performance of cruise missile systems under varying operational.