The Guidance, Navigation and Control (GNC) of air and space vehicles, including artillery rockets, has been one of the spearheads of research in the aerospace field in recent times. Increasing the accuracy of these ballistic projectiles is the major goal. Using Global Navigation Satellite Systems (GNSS) and inertial navigation systems, accuracy may be detached from range. However, during the terminal stage of flight, when movement is governed by highly changing nonlinear forces and moments, GNC strategies based on these systems cause enormous errors in determining attitude and position. These effects can be diminished using additional sensors, independent of jamming and cumulative errors, such as the quadrant photo-detector semi-active laser. This paper proposes a novel non-linear hybridization algorithm, which is based on neural networks, to feed GNC systems while complexity and costs are reduced. It fuses the information from multi-sensor signals, such as GNSS, inertial navigation systems, and semi-active lasers, to predict the line of sight vector, which joins the target and the projectile and drives the flight. As compared to traditional approaches, the use of a neural network presents the advantage that once the network is trained, it is no longer necessary to know the physical-mathematical foundations that govern the dynamics of flight. Instead, it is the network that learns the dynamics. Six-degree-of-freedom non-linear simulations, which are based on real flight dynamics, are used to train the neural networks in the hybridization process. Once training is finished, the approach is tested and simulated together with modified proportional navigation techniques and control methods. Monte Carlo analysis is conducted to determine the suitability of the closed-loop performance across a full spectrum of uncertainty conditions regarding launch, sensor performance, atmospheric, and thrust.

包括火炮火箭在内的航空航天器的制导，导航和控制（GNC）一直是近年来航空航天领域研究的先锋之一。主要目标是提高这些弹道导弹的精确度。使用全球导航卫星系统（GNSS）和惯性导航系统，精度可能会超出范围。但是，在飞行的最终阶段，当运动由高度变化的非线性力和力矩控制时，基于这些系统的GNC策略会在确定姿态和位置时产生巨大的误差。可以使用其他传感器来减少这些影响，而不受干扰和累积误差的影响，例如象限光电探测器半有源激光器。本文提出了一种基于神经网络的新型非线性杂交算法，为GNC系统供电，同时降低了复杂性和成本。它融合了来自多传感器信号（如GNSS，惯性导航系统和半主动激光器）的信息，以预测视线矢量，该视线矢量将目标和弹丸连接在一起并驱动飞行。与传统方法相比，使用神经网络具有以下优势：一旦对网络进行了训练，就不再需要了解控制飞行动力学的物理数学基础。相反，是网络学习动态。基于真实飞行动力学的六自由度非线性仿真被用来训练杂交过程中的神经网络。训练结束后 该方法与改进的比例导航技术和控制方法一起进行了测试和模拟。进行蒙特卡洛分析以确定在涉及发射，传感器性能，大气和推力的不确定性的整个频谱范围内闭环性能的适用性。