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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。
Aiming at the problems of the tracking of the trajectory of the actuator with non-linearity, uncertain atmospheric disturbance in the outside world and the unknown nonlinearity of the model, a robust adaptive neural network dynamic surface following control method is proposed. Longitudinal nonlinear model of ultralow airless space vehicle with actuator input nonlinearity is established. The neural network is used to approximate the unknown nonlinear function in the model, and the nonlinear robust compensation term is introduced to eliminate the actuator nonlinear modeling error and disturbance. The Lyapunov stability theory is used to prove that all signals in the closed-loop system are boundedly convergent. Simulation results show that the proposed method not only guarantees the accuracy of trajectory tracking but also has strong robustness.