无需建模或学习，实现未知系统在动态障碍中按时到达目标
Approximation-Free Control Barrier Functions for Prescribed-Time Reach-Avoid of Unknown Systems
- 用虚拟系统构造安全参考轨迹，避免真实系统建模
- 在未知动力学下确保实时安全与指定时间到达目标
- 适合动态环境中的无人系统控制，无需离线计算
我们研究了具有未知动力学的非线性系统在存在移动障碍物环境中的预定时间可达-避障（PT-RA）控制问题。与鲁棒或基于学习的控制屏障函数（CBF）方法不同，所提出的框架既不需要在线模型学习，也不需要不确定性边界估计。通过在简单的虚拟系统上求解基于CBF的二次规划（CBF-QP），生成一个满足时变、收紧障碍与目标集条件的安全部参考轨迹。利用无近似反馈律将真实系统约束在该参考附近的虚拟约束区（VCZ）内。该构造在无需显式模型识别或离线预计算的前提下，保证了未知动力学与动态约束下的实时安全性与预定时间目标可达性。仿真结果展示了可靠的动态障碍规避与及时收敛至目标集的能力。
原文摘要 · Abstract (English)
We study the prescribed-time reach-avoid (PT-RA) control problem for nonlinear systems with unknown dynamics operating in environments with moving obstacles. Unlike robust or learning based Control Barrier Function (CBF) methods, the proposed framework requires neither online model learning nor uncertainty bound estimation. A CBF-based Quadratic Program (CBF-QP) is solved on a simple virtual system to generate a safe reference satisfying PT-RA conditions with respect to time-varying, tightened obstacle and goal sets. The true system is confined to a Virtual Confinement Zone (VCZ) around this reference using an approximation-free feedback law. This construction guarantees real-time safety and prescribed-time target reachability under unknown dynamics and dynamic constraints without explicit model identification or offline precomputation. Simulation results illustrate reliable dynamic obstacle avoidance and timely convergence to the target set.
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