In this work, we present Transformer-based Powered Descent Guidance (T-PDG), a scalable algorithm for reducing the computational complexity of the direct optimization formulation of the spacecraft powered descent guidance problem. T-PDG uses data from prior runs of trajectory optimization algorithms to train a transformer neural network, which accurately predicts the relationship between problem parameters and the globally optimal solution for the powered descent guidance problem. The solution is encoded as the set of tight constraints corresponding to the constrained minimum-cost trajectory and the optimal final time of landing. By leveraging the attention mechanism of transformer neural networks, large sequences of time series data can be accurately predicted when given only the spacecraft state and landing site parameters. When applied to the real problem of Mars powered descent guidance, T-PDG reduces the time for computing the 3 degree of freedom fuel-optimal trajectory, when compared to lossless convexification, from an order of 1-8 seconds to less than 500 milliseconds. A safe and optimal solution is guaranteed by including a feasibility check in T-PDG before returning the final trajectory.

翻译：本文提出基于Transformer的动力下降制导（T-PDG）算法，该可扩展算法可降低航天器动力下降制导问题直接优化表述的计算复杂度。T-PDG利用轨迹优化算法历史运行数据训练Transformer神经网络，精准预测问题参数与动力下降制导全局最优解之间的关联。该最优解被编码为对应约束最小代价轨迹的紧约束集合与最优着陆终时。通过利用Transformer神经网络的注意力机制，仅需给定航天器状态与着陆点参数即可准确预测大规模时序数据。应用于真实火星动力下降制导问题时，相较于无损凸优化方法，T-PDG将三自由度燃料最优轨迹的计算时间从1-8秒量级降至500毫秒以内。通过在返回最终轨迹前引入可行性校验，确保最优解满足安全与最优性要求。