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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers >Neural-network-based real-time trajectory replanning for Mars entry guidance
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Neural-network-based real-time trajectory replanning for Mars entry guidance

机译：火星进入导航的基于神经网络的实时轨迹重新规划

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摘要

The real-time trajectory replanning method which is used for the guidance of Mars entry is investigated in this paper. Comparing with the traditional Mars entry guidance methods, such as the reference-trajectory tracking guidance and predictor-corrector guidance, the real-time trajectory replanning method can increase the reliability of the mission remarkably. When faults occur during the Mars entry phase, a replacement trajectory will be planned quickly. Due to the limited onboard computing capacity, replanning the trajectory onboard is a challenging task. Corresponding to this problem, the neural network is trained to approximate the dynamics of the atmospheric entry. The uncertain factor of the atmospheric density is also included in the neural network. Then, by using the characters of the neural network, the analytical expressions of the Jacobian which are needed in trajectory optimization are derived. Finally, an estimation-replanning guidance procedure is introduced. The numerical simulation shows that the proposed guidance strategy can decrease the error of final states effectively, and the neural network approximation improves the computational speed of the nonlinear programming solver remarkably, which makes the method more suitable for use onboard.
机译：研究了用于火星进入的实时轨迹重新规划方法。相对于传统的火星进入制导方法，如参考轨迹跟踪制导和预测校正器制导，实时轨迹重新规划方法可以显着提高任务的可靠性。当火星进入阶段发生故障时，将迅速计划更换轨迹。由于机载计算能力有限，因此重新规划机载轨迹是一项艰巨的任务。对应于此问题，训练了神经网络以近似大气进入的动力学。神经网络中还包括大气密度的不确定因素。然后，利用神经网络的特征，推导轨迹优化所需的雅可比行列式的解析表达式。最后，介绍了估计重新规划指导程序。数值仿真表明，所提出的制导策略可以有效地减小最终状态的误差，神经网络逼近显着提高了非线性规划求解器的计算速度，使该方法更适合在机上使用。

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