论文标题
在存在干扰的情况下，使用控制屏障功能训练神经网络控制器
Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances
论文作者
论文摘要
控制障碍功能（CBF）最近已用于非线性系统的可证明安全反馈控制定律。这些反馈控制方法通常通过求解在线二次程序（QP）来计算下一个控件输入。实时求解QP可能是资源约束系统的计算昂贵过程。在这项工作中，我们建议使用模仿学习来学习基于神经网络的反馈控制器，以满足CBF约束。在此过程中，我们还为在外部干扰下开发了用于系统的新型高级CBF。我们演示了受外部干扰（例如风或电流）的独轮车模型上的框架。
Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems. These feedback control methods typically compute the next control input by solving an online Quadratic Program (QP). Solving QP in real-time can be a computationally expensive process for resource constraint systems. In this work, we propose to use imitation learning to learn Neural Network-based feedback controllers which will satisfy the CBF constraints. In the process, we also develop a new class of High Order CBF for systems under external disturbances. We demonstrate the framework on a unicycle model subject to external disturbances, e.g., wind or currents.
