Control barrier functions have shown great success in addressing control problems with safety guarantees. These methods usually find the next safe control input by solving an online quadratic programming problem. However, model uncertainty is a big challenge in synthesizing controllers. This may lead to the generation of unsafe control actions, resulting in severe consequences. In this paper, we develop a learning framework to deal with system uncertainty. Our method mainly focuses on learning the dynamics of the control barrier function, especially for high relative degree with respect to a system. We show that for each order, the time derivative of the control barrier function can be separated into the time derivative of the nominal control barrier function and a remainder. This implies that we can use a neural network to learn the remainder so that we can approximate the dynamics of the real control barrier function. We show by simulation that our method can generate safe trajectories under parametric uncertainty using a differential drive robot model.

中文翻译：

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高度学习安全关键控制的控制障碍功能

控制屏障功能在通过安全保证解决控制问题方面显示出巨大的成功。这些方法通常通过解决在线二次编程问题来找到下一个安全控制输入。但是，模型不确定性是综合控制器的一大挑战。这可能会导致产生不安全的控制措施，从而导致严重的后果。在本文中，我们开发了一个学习框架来处理系统不确定性。我们的方法主要侧重于学习控制屏障功能的动力学，尤其是对于系统的相对高度而言。我们表明，对于每个阶，控制障碍函数的时间导数可以分为名义控制障碍函数的时间导数和余数。这意味着我们可以使用神经网络来学习余数，以便可以逼近实际控制屏障函数的动力学。通过仿真表明，我们的方法可以使用差分驱动器机器人模型在参数不确定性下生成安全轨迹。