基于FCMAC干扰观测器的动态逆再入制导  

Dynamic inversion reentry guidance based on FCMAC disturbance observer for lifting vehicle

作　　者：郑总准[1] 王永骥[1] 吴浩[2] 李传锋[1] 

机构地区：[1]华中科技大学图像信息处理与智能控制教育部重点实验室,湖北武汉430074 [2]北京航天自动控制研究所,北京100854

出　　处：《华中科技大学学报（自然科学版）》2010年第1期96-100,共5页Journal of Huazhong University of Science and Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(6067410560736023);教育部科研培育项目(20081383);航天支撑基金资助项目

摘　　要：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 lifting vehicle, the paper proposed a dynamic inversion reentry guidance law based on fuzzy cerebella model articulation control （FCMAC） disturbance observer. First of all, the drag acceleration energy trajectory tracking law was designed by nonlinear dynamic inversion method; and then, utilizing nonlinearity approximating ability and generalization ability, FCMAC disturbance observer was developed to deal with the uncertainties in the nominal model and disturbance on line. Finally, robust stability of the closed-loop system under this tracking law was proved. Simulation results shows that the dependence of model in dynamic inversion method is degraded and the robustness of guidance system is enhanced by using FCMAC disturbance observer to reconstruct inversion error on line.

关 键 词：神经网络 干扰观测器 再入制导 轨迹跟踪 动态逆 

分 类 号：V412[航空宇航科学与技术—航空宇航推进理论与工程]