发表机构
University of Kentucky（肯塔基大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种基于约束近似动态规划的固定翼飞机滚动时域控制，通过软最小控制屏障函数处理状态约束，实现非短视的最优控制，并在风不确定性下验证了约束满足与性能。
AI 中文摘要
我们提出了一种针对受状态约束的固定翼飞机的滚动时域最优控制。这些约束包括操作约束，如高度、地理围栏和避障，以及飞行路径角、滚转角和空速的界限。状态约束被组合成一个单一的软最小控制屏障函数（CBF）。我们在约束近似动态规划中使用这个复合CBF，以获得一系列解析闭式控制函数，这些函数在满足CBF约束的情况下近似最小化二次有限时域积分代价。所得的滚动时域控制是非短视的，因为它近似优化积分代价，同时在整个预测时域内始终满足状态约束。我们在模拟中展示了约束满足和性能，模拟了固定翼飞机在风不确定性下穿越充满障碍物的空域。我们还将此滚动时域控制与其他两种方法进行了比较。
英文摘要
We present a receding-horizon optimal control for fixed-wing aircraft subject to state constraints. These constraints include operational constraints such as altitude, geofencing, and obstacle avoidance, as well as bounds on flight-path angle, roll angle, and airspeed. The state constraints are composed into a single soft-minimum control barrier function (CBF). We use this composite CBF in a constrained-approximate dynamic program to obtain a sequence of analytic closed-form control functions that approximately minimize a quadratic finite-horizon integral cost subject to the CBF constraint. The resulting receding-horizon control is non-myopic in the sense that it approximately optimizes the integral cost while satisfying the state constraint at all times along the entire prediction horizon. We demonstrate constraint satisfaction and performance in simulations of a fixed-wing aircraft navigating an obstacle-filled airspace under wind uncertainty. We also compare this receding-horizon control with 2 other methods.
