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.

翻译：本文研究了在动态障碍物环境中运行的未知非线性系统的规定时间到达-规避控制问题。与基于鲁棒性或学习的控制屏障函数方法不同，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，生成满足关于时变、紧缩障碍物和目标集的规定时间到达-规避条件的安全参考轨迹。利用一种无需逼近的反馈律，将真实系统限制在该参考轨迹周围的虚拟约束区域内。该构造保证了在未知动力学和动态约束下，无需显式模型辨识或离线预计算，即可实现实时安全性与规定时间目标可达性。仿真结果验证了该方法在动态障碍物规避和及时收敛至目标集方面的可靠性。