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针对升力式再入飞行器的制导问题,首先利用准平衡滑翔原理给出标准的阻力加速度-速度剖面,并对阻力加速度跟踪制导原理进行分析,然后利用自回归小脑模型神经网络(RCMAC)网络良好的非线性逼近能力、泛化能力和自学习能力,采用基于RCMAC网络的动态逆方法实现对阻力加速度的跟踪,并证明闭环系统的稳定性.三自由度仿真结果表明,该制导方式降低了动态逆方法对模型的依赖,增强了制导系统的鲁棒性.
Aiming at the guidance problem of lift-type reentry vehicle, firstly, the standard acceleration-velocity profile is given by using the principle of quasi-balance gliding, and the guidance principle of resistance acceleration tracking is analyzed. Then, using the self-regressive cerebellar model neural network (RCMAC) Nonlinear approximation ability, generalization ability and self-learning ability, the dynamic inverse method based on RCMAC network is used to track the resistance acceleration and to prove the stability of the closed-loop system.The results of three degrees of freedom simulation show that this guidance method reduces the dynamic inverse The dependence of the method on the model enhances the robustness of the guidance system.