AI 中文总结
本文针对捷联导引头导弹，提出结合目标输入估计的MPCG制导律，通过LGRPM离散化OCP并处理约束，仿真显示其拦截性能优于PPNG。
AI 中文摘要
本文为配备捷联导引头的导弹提出一种基于视线角的非线性模型预测控制制导（MPCG）方法。传统比例导航制导（PNG）需要视线（LOS）角速率测量值，而捷联系统无法直接获取该值。MPCG则采用视线角及其导数作为状态变量，消除了弹体速率耦合及相关寄生反馈。该制导问题被构建为连续时间最优控制问题（OCP），通过勒让德-高斯-拉道伪谱法（LGRPM）离散化，作为含明确视场（FOV）和加速度约束的非线性规划（NLP）求解。预测时域第一步的目标加速度采用集成交互多模型（IMM）框架的自适应扩展卡尔曼滤波器（AEKF）进行估计。针对包含俯仰与偏航平面蛇形机动及桶滚机动的单一机动场景的仿真结果表明，MPCG在满足作战约束的同时实现了可靠拦截，在稳定性和鲁棒性方面优于纯比例导航制导（PPNG）。这表明MPCG为受导引头测量限制的现代导弹制导系统提供了一种实用且有效的解决方案。
英文摘要
This paper presents a look angle-based nonlinear model predictive control guidance (MPCG) method for missiles equipped with strapdown seekers. Conventional proportional navigation guidance (PNG) requires line-of-sight (LOS) rate measurements, which are not directly available in strapdown systems. MPCG instead employs look angles and their derivatives as state variables, eliminating body-rate coupling and associated parasitic feedback. The guidance problem is formulated as a continuous-time optimal control problem (OCP), discretized via the Legendre-Gauss-Radau pseudo-spectral method (LGRPM), and solved as a nonlinear program (NLP) incorporating explicit field-of-view (FOV) and acceleration constraints. Target acceleration at the first step of the prediction horizon is estimated using an adaptive extended Kalman filter (AEKF) integrated with an interacting multiple model (IMM) framework. Simulation results under single-maneuver scenarios, which include pitch and yaw plane weaving as well as barrel-roll maneuvers, demonstrate that MPCG achieves reliable interception while satisfying operational constraints, outperforming pure PNG (PPNG) in stability and resilience. This indicates that MPCG offers a practical and effective solution for modern missile guidance systems constrained by seeker measurement limitations.
