基于最优制导模板的神经网络预测制导方法

针对传统预测制导方法中高精度制导与快速实时解算之间的矛盾,提出了一种基于最优制导模板的神经网络预测制导方法。该方法采用基于高置信度飞行器运动模型仿真计算预测弹道落点,利用优化理论进行迭代解算制导变量,以此为基础离线生成样本数据;通过选择合适的多结构模态神经网络,进行基于调度管理的神经网络训练,完成神经网络控制器的设计。针对CAV进行了算例设计,结果表明:该制导方法在线计算量少,制导解算速度快,制导精度高,综合性能远优于传统的预测制导方法。

Neural Network Predictive Guidance Method Based on Pattern of Optimal Guidance

In order to solve the contradiction between high guidance accuracy and fast real-time solving in traditional predictive guidance method, this paper presents a neural network predictive guidance method based on pattern of optimal guidance. The method predicts trajectory based on high believable simulation of kinematic aircraft model, and uses optimization theory to iterative solution of guide variable, so as to generate off-line sample data. By means of choosing multi-modal neural network, training neural network based on dispatching management, to complete the design of the neural network prediction guidance controller. CAV as an example to design, results show that: The method is less real time calculation, fast real-time solution and high guidance accuracy, which comprehensive performance is far better than traditional predictive guidance method.