Analysis and Optimization of Flight Stability of Unmanned Airship Lift Body Configuration Based on Computer Algorithms

The lifting body design, with its physical characteristics of low heat flow rate re-entry and high internal volume utilization, occupies a classic and efficient position in unmanned airship design. This design effectively improves the lift efficiency of the airship, while reducing energy consumption and enhancing overall performance. Based on this background, this article is dedicated to studying the flight stability analysis and optimization methods of the lifting body configuration of unmanned airships, using advanced computing technology to enhance the maneuverability and safety of airships. The core of this article is to develop an innovative optimization strategy that combines the strengths of genetic algorithms (GA) and machine learning (ML). GA explores optimal solutions in complex parameter spaces by simulating natural selection and genetic mechanisms, making it particularly suitable for multivariable, nonlinear optimization problems. ML technology, on the other hand, can learn and predict model performance based on a large amount of data, providing accurate feedback and guidance for the optimization process. Experimental data shows that this optimization strategy significantly improves the stability of unmanned airships under various flight conditions, not only enhancing their anti-interference ability, but also optimizing flight attitude control.