AI 中文总结
本文提出Box-iLQR算法，结合对数障碍函数处理有界状态与控制，在航天器姿态控制及轨道转移中实现高精度约束轨迹优化与约束感知反馈，显著降低闭环终端误差。
AI 中文摘要
本文提出了一种用于非线性航空航天最优控制问题的约束迭代线性二次调节器（Box-iLQR）方法，该问题具有有界状态和控制。采用对数障碍函数，其目标有两重：将障碍参数减小至零以恢复受约束的最优轨迹，同时保留有限障碍以构建平滑的、具有约束意识的反馈策略用于闭环运行。该方法在航天器姿态控制、最小燃料平面轨道转移以及L2到L2晕轨道转移上得到了验证。Box-iLQR尊重所施加的约束，并生成标称轨迹以及时变反馈策略。在扰动、测量噪声和控制不确定性下的闭环仿真表明，终端误差显著降低。这些结果表明，Box-iLQR在单一框架内同时提供了高精度的约束轨迹优化和实用的约束感知反馈。
英文摘要
This paper presents a constrained iterative Linear Quadratic Regulator (Box-iLQR) method for nonlinear aerospace optimal-control problems with bounded states and controls. Logarithmic barrier functions are used with a two-fold objective: the barrier parameter is reduced toward zero to recover the constrained optimal trajectory, while a finite barrier is retained to construct a smooth, constraint-aware feedback policy for closed-loop operation. The method is demonstrated on spacecraft attitude control, a minimum-fuel planar orbit transfer, and an L2 to L2 halo-orbit transfer. Box-iLQR respects the imposed constraints and produces nominal trajectories together with time-varying feedback policies. Closed-loop simulations under disturbance, measurement noise, and control uncertainty show substantial reductions in terminal error. These results demonstrate that Box-iLQR provides both high-accuracy constrained trajectory optimization and practical constraint-aware feedback within a single framework.
