中文摘要：

针对超低空空投下滑阶段执行器非线性、外界不确定性大气扰动以及模型存在未知非线性等因素干扰轨迹精确跟踪问题,提出一种鲁棒自适应神经网络动态面跟踪控制方法。建立了含执行器输入非线性的超低空空投载机纵向非线性模型,采用神经网络逼近模型中未知非线性函数,引入非线性鲁棒补偿项消除了执行器非线性建模误差和外界扰动。应用Lyapunov稳定性理论证明了闭环系统所有信号均是有界收敛的。仿真验证了所提方法既保证了轨迹跟踪的精确性又具有较强的鲁棒性。

英文摘要：

For the ultra-low altitude airdrop decline stage,many factors such as actuator nonlinearity,the uncertain atmospheric disturbances and model unknown nonlinearity affect the precision of trajectory tracking,a robust adaptive neural network dynamic surface control method was proposed.The ultra-low altitude airdrop longitudinal dynamics with actuator nonlinearity was established,the neural network was used to approximate unknown nonlinear functions of model and a nonlinear robust term was introduced to eliminate the actuator＇s nonlinear modeling error and external disturbances.From Lyapunov stability theorem,it is proved that all the signals in the closed-loop system are bounded.Simulation results confirm the perfect tracking performance and strong robustness of the proposed method.