基于自适应扰动观测器的旋转弹神经网络过载驾驶仪设计

The spinning projectiles are influenced by various disturbances during flight, includingthe model uncertainties due to the drastic variations in aerodynamic parameters during cross-domain flight, as well as the external perturbations caused by external forces and moments. The research aims to address the robust control challenges of dual-channel spinning projectiles in high-dynamic flight environments. A pseudo-inverse feedback controller is designed based on the trajectory linearization control method, and an adaptive feedforward compensation controller is developed using radial basis function neural networks to accurately approximate the model uncertainties. Finally, an adaptive disturbance observer is designedby treating the neural network approximation errors and external disturbances as total disturbance based on the fixed-time stability theory, which is used to accurately estimate and compensate for total disturbance. The ultimate uniform boundness (UUB) of the closed-loop system is rigorously proven through Lyapunov theory. The effectiveness of the proposed methodology is illustrated through numerical simulations.