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针对升力式飞行器不确定的非线性模型,提出了基于模糊小脑模型神经网络(FCMAC)干扰观测器的动态逆再入轨迹跟踪制导律.首先采用非线性动态逆设计阻力加速度-能量标准轨迹的跟踪方法;然后利用FCMAC网络良好的非线性逼近能力、泛化能力和自学习能力,设计干扰观测器对模型的不确定性和外部干扰进行在线补偿;最后给出了闭环误差系统的鲁棒稳定性证明.仿真结果表明:FCMAC干扰观测器通过在线重构逆误差,降低了动态逆方法对模型的依赖,增强了跟踪系统的鲁棒性.
Aiming at the uncertain nonlinear model of lift vehicle, a dynamic anti-reentry trajectory tracking guidance law based on fuzzy cerebellar model neural network (FCMAC) disturbance observer is proposed.Firstly, a nonlinear dynamic inverse design is used to track the drag acceleration-energy standard trajectory Then, by using the good non-linear approximation ability, generalization ability and self-learning ability of FCMAC network, the disturbance observer is designed to compensate the model's uncertainty and external disturbance online. Finally, the robust stability of the closed-loop error system The simulation results show that the FCMAC disturbance observer reduces the dependence of the dynamic inverse method on the model and enhances the robustness of the tracking system through the online reconstruction of the inverse error.