Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the Robust Policy CBF (RPCBF), a practical method of constructing CBF approximations that is easy to implement and robust to disturbances via the estimation of a value function. We demonstrate the effectiveness of our method in simulation on a variety of high relative degree input-constrained systems. Finally, we demonstrate the benefits of RPCBF in compensating for model errors on a hardware quadcopter platform by treating the model errors as disturbances. The project page can be found at https://oswinso.xyz/rpcbf.

翻译：控制屏障函数（CBF）已被证明是执行非线性系统安全控制综合的有效工具。然而，对于高相对阶系统，在存在扰动和输入约束的情况下保证安全性是一个难题。本文提出鲁棒策略控制屏障函数（RPCBF），这是一种通过值函数估计来构建CBF近似值的实用方法，该方法易于实现且对扰动具有鲁棒性。我们在多种高相对阶输入受限系统的仿真中验证了该方法的有效性。最后，我们将模型误差视为扰动，在硬件四旋翼平台上展示了RPCBF在补偿模型误差方面的优势。项目页面可在 https://oswinso.xyz/rpcbf 找到。