We use Reinforcement Meta-Learning to optimize an adaptive integrated guidance, navigation, and control system suitable for exoatmospheric interception of a maneuvering target. The system maps observations consisting of strapdown seeker angles and rate gyro measurements directly to thruster on-off commands. Using a high fidelity six degree-of-freedom simulator, we demonstrate that the optimized policy can adapt to parasitic effects including seeker angle measurement lag, thruster control lag, the parasitic attitude loop resulting from scale factor errors and Gaussian noise on angle and rotational velocity measurements, and a time varying center of mass caused by fuel consumption and slosh. Importantly, the optimized policy gives good performance over a wide range of challenging target maneuvers. Unlike previous work that enhances range observability by inducing line of sight oscillations, our system is optimized to use only measurements available from the seeker and rate gyros. Through extensive Monte Carlo simulation of randomized exoatmospheric interception scenarios, we demonstrate that the optimized policy gives performance close to that of augmented proportional navigation with perfect knowledge of the full engagement state. The optimized system is computationally efficient and requires minimal memory, and should be compatible with today's flight processors.

中文翻译：

---

用于拦截具有寄生姿态环机动飞行目标的强化元学习

我们使用强化元学习来优化适合于大气层外拦截机动目标的自适应集成制导、导航和控制系统。该系统将包括捷联导引头角度和速率陀螺仪测量值的观测结果直接映射到推进器开关命令。使用高保真六自由度模拟器，我们证明优化的策略可以适应寄生效应，包括导引头角度测量滞后、推进器控制滞后、由比例因子误差引起的寄生姿态环以及角度和旋转速度的高斯噪声测量值，以及由燃料消耗和晃动引起的时变质心。重要的是，优化的策略在广泛的具有挑战性的目标机动中提供了良好的性能。与之前通过引起视线振荡来增强距离可观测性的工作不同，我们的系统经过优化，仅使用导引头和速率陀螺仪提供的测量值。通过对随机大气层外拦截场景的广泛蒙特卡罗模拟，我们证明了优化策略的性能接近于增强比例导航的性能，并具有完全参与状态的完美知识。优化后的系统计算效率高，需要的内存最少，应该与当今的飞行处理器兼容。我们证明了优化策略的性能接近于增强比例导航的性能，并且完全了解完全参与状态。优化后的系统计算效率高，需要的内存最少，应该与当今的飞行处理器兼容。我们证明了优化策略的性能接近于增强比例导航的性能，并且完全了解完全参与状态。优化后的系统计算效率高，需要的内存最少，应该与当今的飞行处理器兼容。