协同施加相对拦截角的非线性最优制导
Nonlinear Optimal Guidance for Cooperatively Imposing Relative Intercept Angles
AI总结：
本文针对多追击者协同拦截问题，提出基于庞特里亚金极大值原理参数化和前馈神经网络的非线性最优制导方法，实现特定相对拦截角约束下的毫秒级实时生成。
摘要：
本文研究了非线性条件下多个追击者拦截目标的最优协同制导问题。由于某些相对角度能够改善可观测性，制导指令被要求协同控制追击者以特定相对角度拦截目标。通过利用神经网络，提出了一种实时生成非线性协同最优制导指令的方法。具体而言，首先建立了带有相对拦截角约束的最优控制问题。然后，利用庞特里亚金极大值原理推导最优性必要条件，并进一步用于参数化非线性最优制导律。由此，可以通过简单传播生成从状态到非线性最优制导指令映射的数据集。利用该数据集训练一个简单的前馈神经网络，以生成非线性最优制导指令。最后，给出了数值算例，结果表明具有特定相对角度的非线性最优制导指令可以在不到一毫秒的时间内生成。
英文摘要：
The optimal cooperative guidance in the nonlinear setting for intercepting a target by multiple pursuers is studied in the paper. As certain relative angles can improve observability, the guidance command is required to cooperatively control the pursuers to intercept the target with specific relative angles. By using the neural networks, an approach for real-time generation of the nonlinear cooperative optimal guidance command is developed. Specifically, the optimal control problem with constraints on relative intercepting angles is formulated. Then, Pontryagin's maximum principle is used to derive the necessary conditions for optimality, which are further employed to parameterize the nonlinear optimal guidance law. As a result, the dataset for the mapping from state to nonlinear optimal guidance command can be generated by a simple propagation. A simple feedforward neural network is trained by the dataset to generate the nonlinear optimal guidance command. Finally, numerical examples are presented, showing that a nonlinear optimal guidance command with specific relative angles can be generated within a faction of a millisecond.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
