高超声速制导炮弹终端滑模控制

针对高超声速制导炮弹的动力学耦合与非线性控制问题,设计一种基于反馈线性化的终端滑模控制器。首先,兼顾控制系统设计的简便性要求与高超声速制导炮弹的强非线性特点,建立非线性控制模型。然后,对模型中动力学耦合问题,根据微分几何理论对其进行反馈线性化,实现俯仰通道与偏航通道的解耦。最后,对两通道分别设计终端滑模控制器,且控制器有限时间收敛。仿真结果表明,所设计的控制器能够快速稳定的追踪指令信号,且在外界干扰与参数摄动的情况下依然具有良好的鲁棒性。

SAR image recognition method based on multi-eigenspace and neural network

SAR images vary with different aspects greatly even for the same target,which makes it difficult to identify targets from SAR images based on a single aspect.To solve the problem,an SAR image recognition system based on multiple-eigenspaces of ICA and fuzzy min-max neural network ensemble is proposed.Some SAR image eigenspaces are constructed for different aspects based on independent component analysis.An independent neural network is trained for each of eigenspace of different aspects.The trained neural networks are used to recognize SAR targets,and their results are combined through D-S evidence reasoning.The simulation results show that the recognition accuracy is higher than that of using a single neural network with aspect estimation preprocessing.Moreover,it does not need aspect estimation preprocessing.