基于回归向量动态扩张的变体飞行器在线参数 辨识方法

Aerodynamic parameter uncertainty caused by large-envelope flight of morphing flight vehicles is addressed through an online parameter identification method based on regression vector dynamic expansion. First, a polynomial regression equation is constructed from the aerodynamic model of morphing flight vehicles, and a generalized state observer is designed to dynamically expand regression vectors. This transforms the persistent excitation condition ensuring parameter estimation consistency into an interval excitation condition, reducing conservatism in excitation signal design. Second, the parameter identification update law is formulated by integrating the expanded polynomial regression equation with a gradient descent algorithm, enabling online identification of aerodynamic parameters. Finally, the effectiveness of this approach is validated through simulation tests on flight mission scenarios. Results demonstrate rapid and precise identification of uncertain aerodynamic parameters under interval excitation conditions, with superior convergence performance compared to traditional methods.