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针对超低空空投下滑阶段考虑执行器输入死区、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等问题,提出了一种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死区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死区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。仿真验证表明,所提方法既保证了轨迹跟踪的精确性,又具有强鲁棒性。
In order to solve the problem that the dead-zone of actuator input, the disturbance of uncertain atmosphere and the accurate tracking of the interference trajectory due to the unknown nonlinearity of the model, a dynamic face tracking control method based on adaptive neural network is proposed. Longitudinal nonlinear model of the ultralight empty spacecraft with actuator input dead zone is established. The unknown nonlinear function in the model is approximated by neural network, and the nonlinear robust compensation is introduced to eliminate the deadtime modeling error and disturbance of the actuator. 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.